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HSE SCIENTIFIC JOURNAL
Publisher:
National Research University
Higher School of Economics
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The journal is published quarterly
The journal is included into the list of peer reviewed scientific editions established by the Supreme Certification
Commission of the Russian Federation
Editor-in-Chief:
A. Golosov
Deputy Editor-in-Chief
S. Maltseva
Y. Koucheryavy
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online versions in English and Russian – open access
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© National Research University
Higher School of Economics
Vol. 13 No 1 – 2019
Information systems and technologies in businessV.I. Ananyin, K.V. Zimin, R.D. Gimranov, M.I. Lugachev, K.G. Skripkin
Real time enterprise management in the digitalization era ............. 7
Data analysis and intelligence systemsA.V. Demidovskij, E.A. Babkin
Developing a distributed linguistic decision making system ........... 18
Modeling of social and economic systemsA.S. Akopov, A.L. Beklaryan, M. Thakur, B.D. Verma
Developing parallel real-coded genetic algorithms
for decision-making systems of socio-ecological
and economic planning ................................................................ 33
M.A. Myznikova, L.N. Brazhnikova
Development of strategic management tools
for heat supply enterprises in the Donetsk region .......................... 45
N.K. Khachatryan, G.L. Beklaryan, S.V. Borisova, F.A. Belousov
Research into the dynamics of railway track capacities
in a model for organizing cargo transportation between
two node stations ......................................................................... 59
Information securityM.V. Tumbinskaya, B.I. Bayanov, R.Zh. Rakhimov, N.V. Kormiltcev, A.D. Uvarov
Analysis and forecast of undesirable cloud services traffic ............. 71
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
2
ABOUT THE JOURNAL
Business Informatics is a peer reviewed interdisciplinary academic journal published since
2007 by National Research University Higher School of Economics (HSE), Moscow,
Russian Federation. The journal is administered by School of Business Informatics.
The journal is published quarterly.
The mission of the journal is to develop business informatics as a new field within both information
technologies and management. It provides dissemination of latest technical and methodological
developments, promotes new competences and provides a framework for discussion in the field of
application of modern IT solutions in business, management and economics.
The journal publishes papers in the areas of, but not limited to:
data analysis and intelligence systems
information systems and technologies in business
mathematical methods and algorithms of business informatics
software engineering
internet technologies
business processes modeling and analysis
standardization, certification, quality, innovations
legal aspects of business informatics
decision making and business intelligence
modeling of social and economic systems
information security.
The journal is included into the list of peer reviewed scientific editions established by the Supreme
Certification Commission of the Russian Federation.
The journal is included into Web of Science Emerging Sources Citation Index (WoS ESCI) and
Russian Science Citation Index on the Web of Science platform (RSCI).
International Standard Serial Number (ISSN): 2587-814X (in English), 1998-0663 (in Russian).
Editor-in-Chief: Dr. Alexey Golosov – President of FORS Development Center, Moscow,
Russian Federation.
EDITOR-IN-CHIEF
Alexey Golosov FORS Development Center, Moscow, Russia
DEPUTY EDITOR-IN-CHIEF
Svetlana Maltseva National Research University Higher School of Economics, Moscow, Russia
Yevgeni Koucheryavy Tampere University of Technology, Tampere, Finland
EDITORIAL BOARD
Habib Abdulrab National Institute of Applied Sciences, Rouen, France
Sergey Avdoshin National Research University Higher School of Economics, Moscow, Russia
Andranik Akopov National Research University Higher School of Economics, Moscow, Russia
Fuad Aleskerov National Research University Higher School of Economics, Moscow, Russia
Alexander Afanasyev Institute for Information Transmission Problems (Kharkevich Institute), Russian Academy of Sciences, Moscow, Russia
Anton Afanasyev Central Economics and Mathematics Institute, Russian Academy of Sciences, Moscow, Russia
Eduard Babkin National Research University Higher School of Economics, Nizhny Novgorod, Russia
Sergey Balandin Finnish-Russian University Cooperation in Telecommunications (FRUCT), Helsinki, Finland
Vladimir BarakhninInstitute of Computational Technologies, Siberian Branch of Russian Academy of Sciences, Novosibirsk, Russia
Alexander Baranov Federal Tax Service, Moscow, Russia
Jorg BeckerUniversity of Munster, Munster, Germany
Vladimir Belov Ryazan State Radio Engineering University, Ryazan, Russia
Alexander Chkhartishvili V.A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia
Vladimir Efimushkin Central Research Institute of Communications, Moscow, Russia
Tatiana Gavrilova Saint-Petersburg University, St. Petersburg, Russia
Herv GlotinUniversity of Toulon, La Garde, France
Andrey Gribov CyberPlat Company, Moscow, Russia
Alexander Gromoff National Research University Higher School of Economics, Moscow, Russia
Vladimir Gurvich Rutgers, The State University of New Jersey, Rutgers, USA
Laurence Jacobs University of Zurich, Zurich, Switzerland
Liliya Demidova Ryazan State Radio Engineering University, Ryazan, Russia
EDITORIAL BOARD
Iosif Diskin Russian Public Opinion Research Center, Moscow, Russia
Nikolay Ilyin Federal Security Guard of the Russian Federation, Moscow, Russia
Dmitry Isaev National Research University Higher School of Economics, Moscow, Russia
Alexander Ivannikov Institute for Design Problems in Microelectronics, Russian Academy of Sciences, Moscow, Russia
Valery Kalyagin National Research University Higher School of Economics, Nizhny Novgorod, Russia
Tatiana Kravchenko National Research University Higher School of Economics, Moscow, Russia
Sergei Kuznetsov National Research University Higher School of Economics, Moscow, Russia
Kwei-Jay LinNagoya Institute of Technology, Nagoya, Japan
Mikhail Lugachev Lomonosov Moscow State University, Moscow, Russia
Peter Major UN Commission on Science and Technology for Development, Geneva, Switzerland
Boris Mirkin National Research University Higher School of Economics, Moscow, Russia
Vadim Mottl Tula State University, Tula, Russia
Dmitry Nazarov Ural State University of Economics, Ekaterinburg, Russia
Dmitry Palchunov Novosibirsk State University, Novosibirsk, Russia
Panagote (Panos) Pardalos University of Florida, Gainesville, USA
scar PastorPolytechnic University of Valencia, Valencia, Spain
Joachim Posegga University of Passau, Passau, Germany
Kurt Sandkuhl University of Rostock, Rostock, Germany
Yuriy Shmidt Far Eastern Federal University, Vladivostok, Russia
Christine Strauss University of Vienna, Vienna, Austria
Ali Sunyaev Karlsruhe Institute of Technology, Karlsruhe, Germany
Victor Taratukhin University of Munster, Munster, Germany
Jos TriboletUniversidade de Lisboa, Lisbon, Portugal
Olga Tsukanova Saint-Petersburg National Research University of Information Technologies, Mechanics and Optics, St. Petersburg, Russia
Mikhail Ulyanov V.A. Trapeznikov Institute of Control Sciences, Russian Academy of Sciences, Moscow, Russia
Raissa Uskenbayeva International Information Technology University, Almaty, Kazakhstan
Marcus Westner Regensburg University of Applied Sciences, Regensburg, Germany
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
4
ABOUT THE HIGHER SCHOOLOF ECONOMICS
Consistently ranked as one of Russia’s top universities, the Higher School of
Economics (HSE) is a leader in Russian education and one of the preeminent
economics and social sciences universities in Eastern Europe and Eurasia.
Having rapidly grown into a well-renowned research university over two decades, HSE
sets itself apart with its international presence and cooperation.
Our faculty, researchers, and students represent over 50 countries, and are dedicated
to maintaining the highest academic standards. Our newly adopted structural reforms
support both HSE’s drive to internationalize and the groundbreaking research of our
faculty, researchers, and students.
Now a dynamic university with four campuses, HSE is a leader in combining Russian
educational traditions with the best international teaching and research practices. HSE
offers outstanding educational programs from secondary school to doctoral studies,
with top departments and research centers in a number of international fields.
Since 2013, HSE has been a member of the 5-100 Russian Academic Excellence
Project, a highly selective government program aimed at boosting the international
competitiveness of Russian universities.
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
5
ABOUT THE SCHOOL OF BUSINESS INFORMATICS
The School of Business Informatics is one of the leading divisions of HSE’s
Faculty of Business and Management. The School offers students diverse courses
taught by full-time HSE instructors and invited business practitioners. Students
are also given the opportunity to carry out fundamental and applied projects at various
academic centers and laboratories.
Within the undergraduate program, students participate each year in different case-
competitions (PWC, E&Y, Deloitte, Cisco, Google, CIMA, Microsoft Imagine CUP,
IBM Smarter Planet, GMC etc.) and some of them are usually as being best students by
IBM, Microsoft, SAP, etc. Students also have an opportunity to participate in exchange
programs with the University of Passau, the University of Munster, the University of
Business and Economics in Vienna, the Seoul National University of Science and
Technology, the Radbound University Nijmegen and various summer schools (Hong
Kong, Israel etc.). Graduates successfully continue their studies in Russia and abroad,
start their own businesses and are employed in high-skilled positions in IT companies.
There are four graduate programs provided by the School:
Business Informatics
E-Business;
Information Security Management;
Big Data Systems.
The School’s activities are aimed at achieving greater integration into the global
education and research community. A member of the European Research Center for
Information Systems (ERCIS), the School cooperates with leading universities and
research institutions around the world through academic exchange programs and
participation in international educational and research projects.
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
7
Real time enterprise management in the digitalization era
Vladimir I. Ananyin a
E-mail: [email protected]
Konstantin V. Zimin b E-mail: [email protected]
Rinat D. Gimranov c E-mail: [email protected]
Mikhail I. Lugachev d E-mail: [email protected]
Kirill G. Skripkin d E-mail: [email protected]
a Russian Presidential Academy of National Economy and Public Administration Address: 82, Prospect Vernadskogo, Moscow 119571, Russia
b The Russian Union of CIO Address: 34, Seleznevskaya Street, Moscow 123056, Russia
c PJSC Surgutneftegaz Address: 1 block 1, Grigoriya Kukuevitskogo Street, Surgut 628415, Russia
d Lomonosov Moscow State UniversityAddress: 1 build. 46, GSP-1, Leninskie Gory, Moscow 119991, Russia
Abstract
This paper discusses real time control of an enterprise. The history of this concept is associated with the arrival of the real time enterprise (RTE) concept in 2002. The RTE concept has been interpreted variously, mainly in the areas of computer science and marketing. With the advent of new digital technologies and digital organizations, the RTE concept has received a new practical application in management.
This paper discusses an important characteristic of the RTE concept – real time scale and the division value of this scale. The authors have investigated the factors infl uencing the division value of this scale. The composition of these factors includes not only management, but also digitalization factors. We propose considering the real time scale as a time characteristic of organization adaptation to dynamics, uncertainties and complexities that are present in its environment. In this case, the division value of the real time scale is the time that characterizes the limit after which there is a loss of control in the organization.
INFORMATION SYSTEMS AND TECHNOLOGIES IN BUSINESS
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
8
There are two groups of factors infl uencing the division value of the real time scale: objective factors (for example, the speed of the actual operating processes), and subjective factors (for example, limitations on participants’ knowledge of the real situation and/or their opportunistic behavior). Nevertheless, the real time scale is a real phenomenon which has objective manifestations. In a well managed organization, management always spontaneously reaches a consensus regarding the division value of the real time scale. Meanwhile, the division value of real time scale is the time division value of a real clock which is suffi cient for precise planning and control of deviations from the plan.
Key words: digital enterprise; real time enterprise; variability; enterprise manageability; dynamics,
uncertainties and complexities of environment.
Citation: Ananyin V.I., Zimin K.V., Gimranov R.D., Lugachev M.I., Skriprin K.G. (2019)
Real time enterprise management in the digitalization era. Business Informatics, vol. 13, no 1,
pp. 7–17.
DOI: 10.17323/1998-0663.2019.1.7.17
Introduction
Due to digitalization, a “technologi-
cal rearmament race” has already
begun, and it is accelerating. Its
main goal is not just to introduce new infor-
mation technologies but to digitize businesses
as well. It is shown in [1] that digitalization
of enterprises creates innovative management
practices in the fields of organizational, infor-
mational, and human capital. These new prac-
tices are complementary, and they are mutually
enhancing each other.
Among these new practices, the most impor-
tant is real time management. We have to note
that real time management is not a completely
new trend in traditional management. Indeed,
the jobs of a manufacturing process operator
or a railway freight dispatcher are examples
of well-studied practices in real time manage-
ment. When processes are stable, the response
time of an operator or dispatcher must ensure
the process’ continuity (i.e., the manufactur-
ing must be maintained at a constant pace, or
trains must move at a certain average speed).
In these cases, the procedure of real time man-
agement is determined by the speed of the pro-
cess.
How does digitalization change the concept
of real time management? As a result of digital-
ization, the manufacturing processes or railway
freight market conditions may be constantly
changing “on the fly.” No longer can we define
the change as a transition from one stable state
to another. Digitalization gives us an opportu-
nity to get a lot of new data on a manufacturing
process or the state of a railway freight system,
and we will be able to change everything on the
fly as well. As a result, we will rarely consider
the situation as a stable one; far from it, sta-
ble states may become exceptions rather than
regular practice. Moreover, the changes them-
selves are transformed and become less pre-
dictable. For example, a railway traffic jam
INFORMATION SYSTEMS AND TECHNOLOGIES IN BUSINESS
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
9
used to be a threat for a freight dispatcher, and
it caused delays and fines. However, when the
dispatcher has more information, he has new
options: first, there can be new clients requir-
ing new routes; second, instead of a client’s
own trains, the dispatcher might be able to use
a competitor’s empty trains stuck in the jam
on the same route. While this unique situa-
tion lasts, all the participants have to quickly
agree, act on, and profit from it. Therefore, in
the times of digitalization we have to deal with
a stream of unique managerial situations rather
than with regular processes.
The decision (a collaborative one!) must be
made as quickly as it was made before by a dis-
patcher alone. What determines the procedure
of RT management in this case? what does it
depend on? how is it related to digitalization?
Those are the questions we will try to answer in
this paper.
1. History of the real time
management concept
The real time management of an enterprise
(or its separate entities) is the most impor-
tant feature in a digital organization. The real
time enterprise (RTE) concept has a long and
rich history. This concept has already been dis-
cussed for some time, but in October 2002 it was
clearly defined for the first time by Gartner’s
analysts [2]. According to this definition, an
RTE is an enterprise that competes by using
up-to date information to progressively remove
delays in the management and execution of its
critical business processes.
There are three important elements in this
definition:
1. RTE is a relatively abstract objective to
strive towards rather than a particular state of
an enterprise. As Gartner’s analysts noted [2],
“It is unlikely that an enterprise will declare
itself to have become “an RTE”... Progres-
sion is asymptotic — real world organizations
will always remain inefficient in their speed of
response… Optimal RTE capability is a mov-
ing target…” In this concept, real time criteria
must be relative and varying;
2. Information is necessary but not sufficient.
Using up-to date information, we can move
towards the target (RTE), but we will be need-
ing more than just the information, because its
use requires actions and other assets as well.
Analysis of RTE’s activities should be based
not only on computer capital assets, but on
other complementary assets as well [1];
3. Gartner’s experts [2] distinguished two
areas where the RTE concept may be used: exe-
cution of operational processes and activities
management. They note that at the beginning,
enterprises were mostly focusing on the oper-
ational processes on their way towards RTE.
However, application of the RTE concepts
to the expert activities of knowledge workers,
as well as to management problems, could be
beneficial. Therefore, Gartner’s analysts state
that RTE can be used under circumstances of
both a routine issue and an emergency.
The RTE idea was welcomed by many organ-
izations and experts. We can identify the fol-
lowing two interpretations of RTE: informa-
tional and managerial.
Informational interpretation of RTE. The
RTE concept was first used by IT solution pro-
viders [3–16]. However, their understanding
of the RTE concept was limited. They defined
RTE as an organization that collects up-to-
date data and provides the necessary informa-
tion in real time to its employees, clients, sup-
pliers and collaborators. In other words, all the
information an enterprise possesses is real time
information. Usually, supporters of this inter-
pretation of RTE claim that this would happen
when manual labor is kept to a minimum, and
processes are fully automated. It is rather obvi-
ous that the informational RTE can be reached
only by a large-scale deployment of informa-
tional technologies. However, the advocates of
the informational interpretation of RTE do not
go beyond this rather obvious idea.
INFORMATION SYSTEMS AND TECHNOLOGIES IN BUSINESS
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
10
Managerial interpretation of RTE. Some
experts and organizations interpret the RTE
idea in a wider sense, claiming that the whole
cycle from making decisions to responding to
changes should function in real time [17–19].
They define RTE as an enterprise that detects
changes in operational and business conditions
and ensures a quick response to those changes.
Here, RT operation is assumed over the entire
management cycle, from capturing an event
(incident), to its analysis and decision-mak-
ing, to a response action. In addition to real
time acquisition of the information about cur-
rent events, the real time concept relies on two
more crucial stages. First, the decisions must
be “real,” i.e., we have to analyze the informa-
tion, understand the consequences, and work
out the response - all of this in real time. Sec-
ond, the proposed actions and activities should
be “real,” using and adapting the existing pro-
cesses and practices in real time. Therefore, the
real time mode must be supported by the entire
infrastructure, processes, assets and company
employees. This interpretation of real time is
deeper and closer to practical activities than
the informational interpretation.
Let us note that there are other interpreta-
tions of RTE, but they are relatively scarce and
not that important. For instance, one of them
considers RTE as a concept that gathers the
majority of new managerial ideas: information
management, big data management, knowl-
edge management, mobile enterprise, social
enterprise, etc. In our opinion, such an exces-
sive extension of the RTE concept is unjusti-
fied and impractical.
2. The RTE concept
All the experts agree that we have just begun
to study the concept of real time in the RTE
concept. As shown in [1], digitalization can
make an enterprise very competitive. However,
to take advantage of this possibility, the man-
agers of all levels as well as the employees must
make the “right” decisions. This means:
the decisions must obey a certain set of
requirements that satisfy both the solution
developers and customers; such decisions must
be implementable;
the decisions must be timely;
the decisions must be cost-effective: in
their implementation, the management sys-
tem must account for the costs of coordination
between the decision makers and participants.
The coordination costs can be calculated as the
number of man-hours that participants with a
certain level of proficiency spent to make and
implement the decision. These costs are sim-
ilar in nature to transaction costs in manage-
ment [20, 21].
Digitalization provides powerful tools for
making high-quality decisions and makes it
possible to drastically decrease the coordina-
tion costs. Digitalization also helps to make
decisions in a mode close to real time. How-
ever, even if decisions are made faster than
before, this does not mean that they are neces-
sarily timely. This problem is especially impor-
tant when an organization and/or its external
conditions are highly volatile. Let us discuss
what is real time, and how this concept is con-
nected with timeliness of the decisions.
The concept of real time characterizes a pro-
cess of management resolution in an organi-
zation, i.e., when an event requires a mana-
gerial response. As we mentioned above, such
events may constitute either a routine issue or
an emergency. It takes time to resolve such a
situation, and the amount of time should be
appropriate to prevent a routine situation from
becoming an emergency, or an emergency from
becoming a crisis or even a catastrophe:
,
where – real time of the management resolu-
tion cycle;
– the acceptable timeframe for the resolu-
tion of the managerial issue.
Note that we are talking here not only about
INFORMATION SYSTEMS AND TECHNOLOGIES IN BUSINESS
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
11
acquiring and processing the information and
decision-making; we consider the complete
cycle of resolving a managerial emergency,
which includes, apart from the steps mentioned
above, the implementation of the decisions as
well as the corresponding changes. Therefore,
we think that there is a need to study the man-
agerial interpretation of the real time manage-
ment concept more thoroughly as a complete
real time resolution of a managerial incident at
an enterprise.
When conditions are stable and predicta-
ble, the majority of managerial situations can
be easily resolved through the following steps:
information acquisition – information classi-
fication – known solution – quick response.
When conditions are unique and unpredicta-
ble, more complicated and coordinated actions
of the participants are required to resolve a
managerial issue: information acquisition –
situation evaluation – looking for and making
a coordinated decision – organization of and
control over the decision implementation. In
the latter case, all the participants must plan
their actions, hence each participant that con-
tributes to the decision has its own time scale.
The management time scale is a certain series
of time unit intervals that determine the detal-
ization level (quants) of planning and control
over the activities aimed at the resolution of
a managerial situation. A single-unit interval
should be determined by the dynamics of the
development of the managerial situation, i.e.,
by the acceptable timeframe ( ), so that the sit-
uation will not develop into a crisis or a catas-
trophe.
3. Factors that affect
the management time scale
Let us try to figure out the factors that influ-
ence a management time scale’s unit interval.
The more complex a managerial situation is
for its participants, the more complicated is the
activity aimed at its resolution. Hence, a real
managerial situation resolution cycle ( ) has
to contain more actions, while the acceptable
timeframe ( ) is fixed. Therefore, the manage-
ment time scale unit interval will be smaller in
this case. The difficulty of a managerial situ-
ation is always determined by its participants;
therefore, it has both objective and subjective
components. For the sake of simplicity, we can
state that the difficulty of a managerial situa-
tion is determined by the following four key
factors:
Scale. The difficulty of a managerial situa-
tion can depend on its scale, when there are
many interconnected factors to consider. In
this case, the time needed to find the solutions
and to resolve the situation ( ) is hardly pre-
dictable;
Information. A situation may be deemed
complicated because the participants do not
possess complete, reliable, or up-to-date infor-
mation. They will have to look for additional
information, and it is hard to predict how much
time this would take. In reality, this means that
the information should be found as quickly as
possible, and the management time scale unit
interval should be minimal;
Human capital. The situation may be consid-
ered to be complex either because it is unique,
or the personnel have never faced this situation
before (no personal experience), or they do not
know who has such an experience (for exam-
ple, they do not know that a competitor has
had such an experience, or that organization is
not willing to share it). In such situations, the
participants might “reinvent the wheel” by trial
and error, meaning that their possibilities to
plan the activities aimed at the managerial sit-
uation resolution will be very limited. In reality,
this means that the problem should be resolved
as quickly as possible, and the management
time scale unit interval should be minimal;
Organizational capital. A situation may
be difficult because the participants are not
authorized to resolve it, and escalation or del-
egation mechanisms do not work. The lack of
INFORMATION SYSTEMS AND TECHNOLOGIES IN BUSINESS
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
12
such skills as standard practices of team work,
meaningful task formulation, planning, con-
trol, and effective communications can sub-
stantially complicate the situation. In this sce-
nario, it is hard to manage the situation, once
again meaning that the management time scale
unit interval should be kept to a minimum
(everything should be done as quickly as pos-
sible to create a time leeway).
When the acceptable timeframe to resolve
a managerial situation ( ) can be decreased,
this decreases the management time scale
unit interval as well. A managerial situation is
always caused by some key reason. Most often
such reasons arise outside the organization as
impending threats or new opportunities caused
by external processes with their own dynam-
ics. In the first approximation, such dynam-
ics can be described by four key characteristics.
The first one is related to the regular course of
a process, with the remaining three reflecting
its volatility:
the speed of an external process (produc-
tivity);
the extent of variation in the external pro-
cess during a period of time (variations in the
whole process or in its subprocesses);
the number of variations in the external
process during a period of time (two variations
in the whole process or 50 variations in some
subprocesses per year);
the average speed of these subprocesses in
the external process (variations in the scope of
the whole process take three months on aver-
age; variations in the scope of a subprocess take
about a week).
It is noteworthy that key factors causing a
managerial issue may reveal themselves inside
an organization as well, for example as busi-
ness innovations or management initiatives,
with no apparent external changes. However,
they could also be characterized by the varia-
bility parameters discussed above.
In response to external changes, the enter-
prise management makes certain decisions1. We
can obtain an estimate of the acceptable time-
frame ( ) to resolve the situation in the scope
of this managerial decision. This estimate has
both objective and subjective components. In
reality, the acceptable timeframe of the situa-
tion resolution is usually decreased (this can be
described by the catchphrase “this should have
been done yesterday”). This is caused by three
main factors:
1. The increase in the speed of external pro-
cesses, their volatility, and the growth of inno-
vational activity within the organization itself
objectively require resolving any manage-
rial issues faster, meaning that the acceptable
timeframe ( ) to resolve the issue should be
decreased;
2. The acceptable timeframe ( ) to resolve a
managerial situation is decreased because of
the uncertainty in the evaluation of the situa-
tion. The participants in a managerial situation
may not possess enough knowledge or infor-
mation to correctly evaluate the scope and dif-
ficulty of the factors that caused the situation.
In this case, the participants will have to over-
estimate the required time to have a margin of
security, thus decreasing the acceptable time-
frame ( );
3. An uncertainty in the evaluation of a man-
agerial situation (for instance, an underes-
timated scale of a disaster) leads to errone-
ous estimates of the acceptable timeframe
( ). When the participants realize their mis-
take, they will need more time to correct them,
and the situation’s resolution will occur under
stricter time constraints, thus narrowing the
acceptable timeframe ( ).
For mature businesses under professional
management, emergency situations should
be rare. The majority of managerial activities
are related to routine situations, which have a
local scale, their causation is well-known, the 1 We assume that ignoring the situation and not taking action at all is also a managerial decision
INFORMATION SYSTEMS AND TECHNOLOGIES IN BUSINESS
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
13
experience of their resolution has been accu-
mulated, and all the necessary information can
be found quickly. Based on a stream of rou-
tine managerial situations, such organizations
set rules, regulations, standards, and organiza-
tional structures. In particular, they set a par-
ticular timeframe to resolve a routine manage-
rial situation, thus setting the time management
unit interval.
Unlike emergency managerial situations, the
routine ones do not cause a strong pressure to
decrease the management time scale unit inter-
val. Nevertheless, we have to remember that all
the managerial situations in an organization
are intertwined; it is hard to predict which situ-
ation might escalate or defuse; a routine situa-
tion may become an emergency, and vice versa.
Therefore, the management time scale unit
intervals must be constant for the entire stream.
Since a management system should always be
ready for emergency events, the management
time scale unit interval should be determined
by the resolution cycle ( ) of the most compli-
cated situation the organization has ever dealt
with. This does not mean that routine issues
must be resolved at the same speed as emer-
gency situations. Of course, different mana-
gerial situations should have different time-
frames. However, we think that a system that
manages an entire stream of events should have
the same timescale, and the resolution of all
the situations should be planned accounting
for the management time scale unit intervals.
This leads us to the definition of the real time
management scale.
4. The real time management scale
The real time management scale is a scale
where a single-unit interval is sufficient to
resolve the most complicated managerial sit-
uation the organization has ever dealt with. A
single-unit interval on this scale is determined
as the time necessary for the resolution of this
managerial situation ( ) divided by the number
of stages in the resolution cycle.
Sometimes a complicated managerial situa-
tion can be resolved easily and elegantly. How-
ever, this does not mean that the unit inter-
val of the real time management scale must
be increased. First, the real time management
scale describes the entire stream of managerial
situations. Therefore, for this elegant solution
to increase the unit interval on the scale, such
elegant solutions must become a regular man-
agement practice. Second, when the solution
has not been found yet, a good manager should
base any decisions on the most pessimistic sce-
nario.
We can say that an organization resolves all
the managerial situations they are aware of in a
timely manner when a routine situation never
becomes a crisis. Therefore, this scale reflects
the organization’s real time. In this context
“real” means that the time corresponds to a
certain external reality, is appropriate to the
environment, and reflects the external condi-
tions. We can define the term real time only
in the context of a link between the process
of object management and the object’s envi-
ronment. We have to emphasize that this only
concerns the environment known to the man-
agement and the external conditions they are
aware of. This relates to the note mentioned
above that the real time criteria are relative
and volatile. It is impossible to develop an
real time management scale in advance for
all unknown future managerial situations. Of
course, the unit interval of an real time man-
agement scale can be decreased proactively to
respond to more complicated managerial sit-
uations. However, there is no way of knowing
if this response would correspond to the real
time criteria.
We can say that the organization’s real time
management scale is a time parameter that
reflects how the organization adapts to the
dynamics and complexity of its environment.
A unit interval on this scale corresponds to the
limit where the management starts to lose con-
trol over the organization.
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5. The real time scale and digitalization
Classical automation of regular stable busi-
ness processes helped to make the majority of
routine situations standard and exclude them
from the overall stream of managerial situa-
tions. This created conditions for the manage-
ment to reduce the timeframe of the resolu-
tion cycle ( ). However, this timeframe was not
reduced substantially, because the actual prac-
tice of the implementation of the resolution
decisions – even though the decisions them-
selves were made faster – remained the same;
also, strong incentives to decrease the time-
frame of the management resolution cycle ( )
were scarce. This is why classical automation
had practically no effect on the real time man-
agement scale. Effects were noticeable when
automation led to a productivity increase in
standard operational processes.
Digitalization drastically changes the stream
of managerial situations and gives the manage-
ment strong incentives to substantially reduce
the acceptable timeframe of resolution of man-
agerial situations ( ).
Increase in the density of the stream of inci-
dents and emergency situations. As digitaliza-
tion expands, the number of digital twins of real
objects increases. These twins serve as big data
sources. The data appear as soon as an event is
automatically registered, and the data volume
increases substantially. The sensitivity of an
enterprise’s management to external changes
grows; as the volume of information increases,
the participants can see risks and possibilities
they have not seen before. Now they need to
adequately respond to them ( ). This means
that the number of managerial issues, as well
as the percentage of complicated emergency
situations, will increase; this calls for the unit
interval of the RT management scale to be
decreased.
Increased complexity of an enterprise infor-
mational model. As data volume increases,
digitalization provides the participants with
efficient tools for intellectual analytics, which
allows them to find new connections and
trends. However, these new connections and
trends can only be revealed if the participants
improve their skills and use more complicated
decision-making models. For example, when
a switch is made from the business processes
scale to the scale of value chains, all the par-
ticipants must update their way of thinking to
embrace this new scale. In this case, the num-
ber of participants in managerial situations will
increase, and the situations will become more
complicated. The growth in complexity and
uncertainty once again leads to a smaller value
of the acceptable timeframe ( ) of managerial
situations.
The increase in the number of internal initi-
atives on changes. As digitalization expands,
more participants will be involved into activ-
ities related to business innovations or man-
agement initiatives. This means that the vola-
tility initiated by the organization or a value
chain must increase. The more local the ini-
tiative is, the easier it is to manage it, and the
closer it will be to a routine managerial situ-
ation. We have to comprehend that new ini-
tiatives on different scales will join the gen-
eral stream of managerial situations. This will
expand the stream and can potentially lead to
an increase in the number of complex emer-
gency situations due to the complexity of the
connections between the elements. Again,
there is a trend to decrease both the accept-
able timeframe ( ) and the unit interval of the
real time management scale.
Escalation of market competition. The phase
when the digitalization leaders on the market
are “skimming cream” will be short. Solution
developers and consultants will quickly intro-
duce new technologies to the competitors. This
will lead to a management “arms race” aimed
at decreasing the market-average values of
and . This, in turn, will create a new power-
ful incentive to decrease the unit interval of the
RT management scale.
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BUSINESS INFORMATICS Vol. 13 No 1 – 2019
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Digital organizations are just starting to
appear, and we can assume that, as the digitali-
zation scope and depth expand, the unit inter-
val of the real time management scale will be
decreased. This means that when the digital
economy becomes a reality the operational and
managerial processes, as well as the pace of life,
will accelerate.
Conclusion
The real time management scale is a char-
acteristic time parameter that shows how an
organization adapts to the dynamics and com-
plexity of its environment. The unit interval of
the real time management scale sets limits to
the unit intervals of incoming signals and the
acceptable lag of a response to changes. There-
fore, the unit interval of the real time manage-
ment scale determines the limits of the possi-
bilities of managing situations.
Figuratively speaking, under classical auto-
mation, the clock in the central control room
of an enterprise reflects the real manage-
ment time of manufacturing processes. In a
digital enterprise, the real management time
is reflected by the clocks in the negotiation
rooms where decisions are made. The negoti-
ation rooms may be real as well as virtual. It is
of utmost importance though that the clocks in
those rooms be synchronized.
We can assume that the expansion of digitali-
zation in scope and depth will cause a decrease
of the unit time interval of the real time man-
agement scale.
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About the authors
Vladimir I. Ananyin
Senior Lecturer, Department on Business Processes Management,
Russian Presidential Academy of National Economy and Public Administration,
82, Prospect Vernadskogo, Moscow 119571, Russia;
E-mail: [email protected]
Konstantin V. Zimin
Editor-in-Chief, Information Management Journal;
Member of the Board, The Russian Union of CIO, 34, Seleznevskaya Street, Moscow 123056, Russia;
E-mail: [email protected]
INFORMATION SYSTEMS AND TECHNOLOGIES IN BUSINESS
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
17
Rinat D. Gimranov
Head of IT Department, PJSC Surgutneftegaz,
1 block 1, Grigoriya Kukuevitskogo Street, Surgut 628415, Russia;
E-mail: [email protected]
Mikhail I. Lugachev
Dr. Sci. (Econ.), Professor;
Head of Department of Economic Informatics, Lomonosov Moscow State University,
1 build. 46, GSP-1, Leninskie Gory, Moscow 119991, Russia;
Academic Supervisor, IBS Corporate University;
E-mail: [email protected]
Kirill G. Skripkin
Associate Professor, Department of Economic Informatics, Lomonosov Moscow State University,
1 build. 46, GSP-1, Leninskie Gory, Moscow 119991, Russia;
E-mail: [email protected]
INFORMATION SYSTEMS AND TECHNOLOGIES IN BUSINESS
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
18
Developing a distributed linguistic decision making system
Alexander V. DemidovskijE-mail: [email protected]
Eduard A. Babkin E-mail: [email protected]
National Research University Higher School of Economics Address: 25/12, Bolshaya Pecherskaya Street, Nizhny Novgorod 603155, Russia
Abstract
In this paper, a new approach to multi-criteria decision making is proposed based on linguistic information taken from a group of autonomous experts. This approach provides an opportunity to better analyze and fi nd solutions for poorly structured problems with consideration of their multidimensionality and uncertainty of context. One of the key components of the proposed methodology is the hierarchy of abstractions proposed by John van Gigch, which presents the levels of alternative solutions and criteria for assessing them. By integrating this hierarchy, it is claimed that the problem situation can be comprehensively analyzed. Therefore, we call our approach multi-level multi-attribute linguistic decision making (ML–MA–LDM).
Our approach includes a methodology that is the particular sequence of steps and the mathematical model, as well as the method to automatically distribute weights of experts’ assessments depending on their confi dence level. Furthermore, this novel approach supports both qualitative and quantitative assessments that are strictly propagated through the complete decision making process across all hierarchical levels of abstraction. Finally, we demonstrate a prototype of a multi-agent expert system for solving poorly structured models with regard to their context uncertainty and multiple aspects. This prototype plays the role of simulation engine for competitive solutions and for verifi cation purposes of the proposed methodology.
Capabilities of the developed approach and the prototype were demonstrated in a practical case of solving a complex confl ict problem of strategic management, as well as rigorous analysis of the proposed approach strengths and weakness that defi nes the direction for further research.
Key words: linguistic decision making; multi-criteria choice; meta-decisions; multi-agent systems;
fuzzy logic; poorly structured problems; decision support systems.
Citation: Demidovskij A.V., Babkin E.A. (2019) Developing a distributed linguistic decision making system.
Business Informatics, vol. 13, no 1, pp. 18–32.
DOI: 10.17323/1998-0663.2019.1.18.32
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Introduction
In the modern world, there is a huge num-
ber of very complex and intricate prob-
lems, such as global warming, hunger,
poverty, unemployment. These problem situa-
tions can be divided into two groups: structured
and poorly structured situations [1]. The latter
are characterized by uncertainty, environmen-
tal variability etc. A large subset of poorly struc-
tured problems can be characterized by a huge
number of stakeholders (or experts), alterna-
tive solutions and criteria, which are used by
decision makers. It is proposed that selection of
one of these alternatives lets a decision maker
solve a problem situation and satisfy a major-
ity of stakeholders. Therefore, creation of new
decision making models and the software design
of expert systems for multi-criteria choice is a
highly topical scientific and social problem.
Moreover, such problem situations frequently
have multiple analysis aspects (or dimensions),
like political (e.g. political tension), econom-
ical (e.g. benefit), ethical (e.g. conformity to
morality) etc. In this way a case of multi-cri-
teria decision making problem appears [2, 3].
The search for the solution of the problem
that has an impact on multiple stakehold-
ers requires mathematical models, algorithms
and a methodology which allow one to analyze
subjective experts’ evaluations from different
aspects. We may note that frequently different
problems’ aspects are hierarchically structured.
In our approach, for multi-criteria choice we
propose to use the framework of meta-deci-
sions suggested by J. van Gigch [4]. We adopt
his main idea of extracting eight abstraction
levels which characterize the principal aspects
of the problematic situation.
There are numerous attempts to elaborate
new decision making approaches or adopt
existing ones to real-life cases, like healthcare
[5], performance evaluation of partnerships
[6], fiber composites optimization [7], reverse
logistics selection and evaluation [8], project
resources scheduling [9], supplier selection
[10], aircraft incident analysis [11]. Usually
traditional approaches like TOPSIS, ELEC-
TRE, VIKOR are used. The considerable
drawback is that these methods rely mostly on
quantitative evaluations, even given in a form
of fuzzy sets [12]. On the other hand, estima-
tions that are given by experts during problem
discussion can be both quantitative and qual-
itative. Qualitative evaluations become more
and more preferable in complex situations
because compared to quantitative evaluations,
qualitative ones have the serious advantage of
their ability to express fuzzy information (e.g.
hesitation). However, according to our rigor-
ous analysis of the field, there is an emerging
trend of combining traditional decision mak-
ing approaches with methods of processing
qualitative evaluations. The combination of
TOPSIS methodology and 2-tuple model for
analyzing qualitative assessments represents a
bright example [13].
Reliable and flexible means for analysis of
qualitative evaluations are provided within the
scientific area of "linguistic decision making"
[2, 3, 14–17] and "linguistic multi-attribute
decision making" [2]. These and other meth-
ods of processing qualitative evaluations now
are generally called "computing with words"
[16–20]. The three most popular approaches
used for calculation in linguistic terms [21]
are:
linguistic computational model based on
membership functions;
linguistic symbolic computational model
based on ordinal scales;
max-min operators, linguistic symbolic
computational model based on convex combi-
nations.
In many cases, information that comes from
the experts is heterogeneous due to its multi-
granularity and there are approaches which
provide methods to work with such informa-
tion: the fusion approach for managing mul-
tigranular linguistic information [22], the lin-
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20
guistic hierarchy approach [23] and the method
of extended linguistic hierarchies [14].
This paper presents results of the develop-
ment of a new approach to multi-criteria lin-
guistic decision making in the presence of mul-
tiple hierarchically ordered problem aspects.
Our approach includes a methodology and pro-
totype of a multi-agent expert system for solv-
ing poorly structured models with regards to
their context uncertainty and multiple aspects.
The main contribution in the development of
methods of multi-criteria problem analysis is
development of new scientific principles for
integrating linguistic decision making and the
meta-decision framework of J. van Gigch. This
integration provides stakeholders with a struc-
tured method to analyze the problem from
multiple aspects so that the solution found is
more likely to be objective and optimal than
one that is taken without considering its influ-
ence on all aspects of our life.
This paper has the following structure. In Sec-
tion 1, we provide necessary background infor-
mation that contains a description of basic ele-
ments of the proposed methodology. Then, in
Section 2, we give a detailed description of the
proposed approach which defines the process of
decision making. In Section 3, we demonstrate
the applicability of the proposed approach to the
real case of complex conflict situation in the rice
industry. Section 4 covers details on the design
of a multi-agent system (MAS) that was built for
demonstrating the work of the proposed meth-
odology. Finally, the Conclusion displays the
analysis of the proposed approach and potential
directions of further research.
1. Background and related research
Modeling, analysis and solving poorly struc-
tured problems on the basis of linguistic esti-
mation use several important mathematical
structures.
Definition 1. The linguistic variable is char-
acterized by the tuple:
(H, T (H), U, G, M),
where H – the name of the variable;
T (H) or just T – a set of notions H, i.e. a set
of names of linguistic values H, where each
value is a variable which is denoted in general
case as X and gets values from the set of terms
of the subject area U, which is denoted as u;
G – syntax rule (often takes the form of gram-
mar) for generation of values from H;
M – semantic rule, which defines relation
between H, M (x) [24].
In order to use such linguistic evaluations, it
is important to pick up linguistic descriptors
for a set of concepts and also to define gran-
ularity of uncertainty. Usually the set of con-
cepts is denoted as S = {s0, …, s
g}. The granular-
ity degree of such a set depends on the context
of the problem situation.
On the basis of the given definitions, Herrera
et al. [25] proposed a classical model of analy-
sis of linguistic evaluations using the structure
which is called 2-tuple.
1.1. The classical model on the basis of 2-tuple structure
2-tuple includes the pair [25]:
si S = {s
0, …, s
g} – a linguistic concept;
– a numeric value, or "symbolic trans-
lation", which shows the result of the member
function, i.e. the nearest concept si S = {s
0, …,
sg}, if s
i is not the precise mapping of the given
result.
Later multiple authors proposed a huge num-
ber of operators [3], which allows us to aggre-
gate linguistic information.
1.2. The modernized 2-tuple model
The main problem of the classical model is
the necessity to define the basic scale of evalu-
ations and rules of translation of these evalua-
tions to a single scale. The selection of the scale
and translation rules in that scale becomes
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21
a separate and complex task. In their recent
paper [26] researchers proposed a model which
allows one to work with multiple scales with-
out additional transformations. The signifi-
cant difference between the classical model
[25] and the modernized one [26] is the set of
translation rules from the 2-tuple structure to
the numeric representation and vice versa. It is
important to emphasize that this model does
not imply the fact that alternatives and crite-
ria can vary across the time, since it is consid-
ered in a model with bipolar linguistic term
sets [27]. The modernized 2-tuple model [26]
is used in the approach proposed in this paper.
Definition 2. Translation function [26]. Let
S = {s0, …, s
g} be the set of linguistic concepts,
– the set of 2-tuple structures, g = + 1 – its
granularity, – a normalized result of the sym-
bolic aggregation. Then the translation func-
tion can be defined as:
(1)
where round is a function that assigns to the
nearest integer value i {0, 1, ..., g} to .
Definition 3. Reverse translation function
[26]. Let S = {s0, …, s
g} be the set of linguis-
tic concepts, – the set of 2-tuple structures,
g = + 1 – its granularity, (si , – a 2-tuple
structure on , where . Then
the function always exists, so that for the
given 2-tuple structure it returns an equivalent
numeric value [0, 1):
(2)
1.3. 2-tuple model for the comparative
linguistic information
It is reasonable to suppose that experts are
not able to estimate alternatives by a given cri-
teria equally well. When experts are not able to
give precise evaluation, they can make it com-
parative and even express it as a whole sentence
that can have the following structure: "< > is
better than | equal to | worse than < >". This
idea exactly is the basis of the approach which
is called HFLTS (hesitant fuzzy linguistic term
sets) [28].
Definition 4. HFLTS [29]. Let S = {s0, …, s
g}
be a set of linguistic concepts. Then HFLTS or
is an ordered finite set of consecutive linguistic
concepts from S:
HS = {s
i , s
i +1, …, s
j }, S
k S, k {1, ..., g} (3)
In order to avoid information loss when using
HFLTS, another approach was proposed that
is called hesitant 2-tuple set [26]. There are
also operators for aggregation and comparison
of hesitant 2-tuples sets entities: MTWA [26],
MHTWA [26], etc.
Definition 5. Hesitant 2-tuple set [26]. Let
S = {s0, …, s
g} be a set of linguistic concepts,
is a 2-tuple structure on S, i = 1, 2, ..., n. If
(bi ,
i ) < (b
j ,
j )( for any (i < j), (b
1 ,
1 ), (b
2 ,
2 ),
…, (bl ,
l ), which is denoted as T
S , is hesitant
2-tuple set for any i < j . Then HFLTS or HS is
an ordered finite subset of consecutive linguis-
tic concepts from S.
1.4. A meta-decision framework
for analysis of problem situations
from different abstraction levels
Due to the fact that during the process of
finding solutions for complex problems it is
important to analyze the situation from differ-
ent aspects, we decided to use eight abstrac-
tion levels that were initially proposed by
J. van Gigch in his meta-decision framework
[26]. These levels are used as the basic set of
aspects of any analyzed problem. More spe-
cifically, these levels are (in increasing order of
abstraction level): managerial, economic, sci-
entific, legal, political, epistemological, ethi-
cal, aesthetic.
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Definition 6. Abstraction is a mental process
in which representations of reality are defined
on different levels of conceptualization.
Definition 7. An abstraction level (a logic
level) – a perspective or a point of view from
which stakeholders are trying to solve the prob-
lem. A chosen perspective reflects historical
skills of an expert on the given abstraction level
(the logic level).
2. Proposed multi-criteria decision making approach
In the previous chapter, basic linguistic deci-
sion making (LDM) approaches were described
as well as eight levels of abstraction that are vital
for analysis of complex problems. It is impor-
tant to emphasize that existing approaches con-
centrate either on analysis of only quantita-
tive assessments or only qualitative ones. Very
few approaches focus on both types of estima-
tions. At the same time, modern methodologies
are likely to assume that there are a number of
experts without capturing the area of their exper-
tise as well as the fact that criteria also belong to
different abstraction levels, like politics, econom-
ics etc. More importantly, existing methods for
decision making are demonstrated on artificial
cases with very few experts and alternative solu-
tions. Finally, the demonstration is never made
in the dynamics of a multi-agent system (MAS),
although not only could it help to reveal draw-
backs of existing approaches but also to analyze
the behavior of agents and details of their interac-
tion. For example, it is promising to also consider
trust among experts. This brings us to the point to
propose a new methodology which could incor-
porate most of the gaps described above.
In this section, we will describe the proposed
approach for solving poorly structured prob-
lems that are capable of taking into considera-
tion multiple hierarchically ordered aspects of
the problem situation and process heterogene-
ous evaluations. We call our approach multi-
level multi-attribute linguistic decision making
(ML–MA–LDM).
2.1. Description of steps during ML–MA–LDM
The proposed approach consists of several
consecutive steps starting from defining the
estimation rules and finishing with the com-
munication stage (Figure 1). It is important to
note that these steps can be found individually
in various papers describing the decision mak-
ing process, for example in [30, 31], but never
were fused in a consistent way. The proposed
approach includes:
1. Setting up rules for providing estimations
and distribution of criteria weights. In the pro-
posed approach we make several assumptions:
а. experts give honest evaluations;
в. experts believe each other;
Definition of estimation rules
Formulating desired states
Formulating criteria
Formulating alternative solutions
Multi-Level Multi-Attribute estimating
Aggregation of estimations
Search for the best alternative solution
Communication of a solution found
Defin
ition
of i
nitia
l lin
guis
tic d
ata
Fig. 1. The proposed methodology to solve poorly structured problems in conditions
of uncertainty of context and fuzzy estimations
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с. experts choose granularity of evaluations
according to their experience and knowl-
edge about a problem;
d. experts have the same understanding of
evaluations;
2. Defining available linguistic sets, a context-
free grammar and transformation function;
3. Multi-level definition of the desired state,
criteria and alternatives.
a. analyzing the desired state on each level
of abstraction;
b. formulating criteria for each level of
abstraction;
c. formulating alternatives.
4. Giving multi-level and multi-criteria evalu-
ations.
a. aggregating information;
b. searching for the best alternative;
c. communicating the solution found.
2.2. Aggregating information
After criteria and alternatives were defined,
all experts start giving evaluations of each alter-
native for each available criterion.
Let x = {x1, x
2, ..., x
N } is the list of alternatives,
c = {c1, c
2, ..., c
M } is the list of criteria, e = {e
1,
e2, ..., e
T } is the list of experts. We assume that
each expert ek can evaluate alternatives using
different linguistic scales Sg k with granular-
ity gk. In the case of comparative evaluations,
we also have the grammar GH which can be
also used for creation of linguistic evaluations.
Moreover, the criteria are given for each level
of abstraction in the meta-decision framework,
i.e. let l = {l1, l
2, ..., l
Z } be the list of the levels of
abstraction.
The overall sequence of steps is described in
Figure 2. These steps describe pre-processing
and aggregation of evaluations collected from
experts. Therefore, as a result, one evaluation
for each given alternative is obtained and the
best alternative can be found by sorting these
evaluations according to rules of comparing
hesitant 2-tuple fuzzy sets.
Step 1. Formulating matrices of HFLTS
evaluations. Due to the fact that experts can
give evaluations in a different form, it is impor-
tant to preprocess them. More specifically,
evaluations should be translated to HFLTS as
this format is flexible enough to represent both
precise and interval evaluations. As a result, for
each expert we get a matrix of evaluations
,
where – an evaluation of the expert ek for
the i-th alternative on the j-th criterion in the
format of HFLTS on the scale Sg.
Step 2. Aggregation of evaluations by cri-
teria. During this step, it is important to find
an accumulated evaluation for combination of
each alternative i, every level of abstraction l,
and every expert ek by aggregating evaluations
for every criterion corresponding to the given
abstraction level. Then for each expert we get a
following matrix:
, (4)
where i – the index of alternative;
j – the index of the abstraction level;
Translating estimations to hesitant 2-tuple sets
Aggregating estimations on the criteria level
Translating estimations to abstractions level
Aggregating estimations on the experts level
Aggregating estimations on the abstractions level
Fig. 2. A structure of the “Aggregating information” step of the proposed methodology
DATA ANALYSIS AND INTELLIGENCE SYSTEMS
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24
p – the vector of criteria weights,
.
Here we propose to use the MHTMA opera-
tor because each criterion has its own defined
weight. So, for each expert we get the following
decisions matrix:
,
where – the evaluation of the expert ek for
i-th alternative for j-th level of abstraction in a
form of HFLTS on the scale Sg k.
Step 3. Translation of evaluations to
abstraction levels. The next step should be
aggregation of evaluations for each level of
abstraction separately. From the previous step
we get T matrices with evaluations, each of size
N Z. In order to make aggregation for each
level of abstraction, we need to have Z matrices
with evaluations, each of the size N T, where
N is a number of alternatives and T is a number
of criteria. So, for each abstraction level we get
the following decisions matrix:
,
where – the evaluation for lu-th abstraction
level from the i-th alternative for j-th expert in
a form of HFLTS on the scale Sg k.
Step 4. Aggregation of evaluations by
expert. During this step, the total evaluation is
calculated for each level of abstraction lu, for
each i-th alternative, and for each expert given.
If w is the given vector of experts’ weights,
,
then for each level of abstraction we get the fol-
lowing matrix:
(5)
where i – the index of the alternative;
j – the index of the abstraction level.
If the vector of weights is not given, the fol-
lowing formula should be used for their calcu-
lation:
(6)
where w [0, 1) – the proportion of the first
expert’s evaluation in the weights sum.
Therefore, we get the following decisions
matrix
,
where is aggregated evaluation for i-th
alternative and for j-th level of abstraction in a
form of HFLTS on the scale Sg k.
Step 5. Aggregation of evaluation by levels
of abstraction. During this step the total evalu-
ation for each i-th alternative and for each level
of abstraction is found:
, (7)
where i – the index of alternative; q – the vector of weights of levels of abstraction,
.
So, we get the following vector of evaluations
,
where is the aggregated evaluation for i-th
alternative in a form of HFLTS on the scale Sg k.
As a result, we get assessments that draw
insights on how each alternative is measured on
each level of abstraction and a decision maker
can use this information to better understand
the scope of alternatives and their influence on
each aspect of the problem situation. It can also
be possible to customize a methodology at this
point; for example it is possible to select only
a subset of levels of abstraction which interest
the decision maker to make the final decision.
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3. Demonstration in one case
For demonstrating our approach, we use a
complex problem situation with rice produc-
tion in the state Chhattisgarh (India) [32]. Rice
is one of the main products in India in terms
of consumption. This state is the biggest pro-
vider of paddies. The first step is to give a gen-
eral description of the current situation.
3.1. Description of the current state
In the Chhattisgarh state, the rice industry
obeys the Government. There is a huge number
of farmers, the majority of whom are middle-
and small-sized households. Middle- and small-
sized households are very dependent on weather
conditions and Government politics with respect
to buying the rice left over at the end of the sea-
son for distribution among poor people. That
is why they have to take loans that often bank-
rupt households. This in turn makes the number
of working population in rice industry decline.
After the rice is ready, farmers sell rice to millers.
Millers do not rush to buy rice since the Govern-
ment buys rice at very low prices at the end of the
season. Millers clean the paddy up, produce rice
and sell it via sales agents. The miller business has
minimal profitability, and that is why the market
is decreasing and only big players are left there.
These big players define the rice price to make it
as low as possible. Rice cannot be exported due
to the use of several fertilizers that damage the
atmosphere. The overall political atmosphere is
unfavorable.
3.2. Description of a desired state
Households receive subsidies from the Gov-
ernment on their business. Rice that is left
unbought at the end of the season is bought at
the market price by either the Government or
millers. The Government prevents the crea-
tion of miller monopolies that tend to reduce
the market price. Moreover, there is an active
export policy that let millers increase their
profits. Moreover, innovative technologies
make it possible to avoid use of polluting fer-
tilizers, thus opening a door for export. Millers
have a joint logistics union that lets them con-
trol the supply chain. The poor get rice from
the Government and this, in turn, motivates
them to become farmers. Low unemployment
decreases chaos on the streets.
Due to the multidimensionality of the prob-
lematic situation, there are a large number
of alternative solutions. Alternative solutions
define the set of actions that can be later evalu-
ated by criteria defined earlier. In order to for-
mulate them there is a specific technique:
1. Definition of the desired state of industry
for each level of abstraction;
2. Definition of criteria specific for each level;
3. Definition of concrete alternative solu-
tions driven by the desired state on each level.
In the given case there are the following
experts: the representative of the Department
of Foreign and Domestic Policies (DFD), the
representative of the Department of social pol-
itics (DSP), the representative of farmers (F),
the owner of a mill (M), a sales agent (SA), a
rice transporter (RT), an ecologist (E).
We consider the experts having experience
on the following levels of abstraction (Table 1):
managerial (MLA), economic (ELA), scien-
tific (SLA), legal (LLA), political (PLA), epis-
temological (EPLA), ethical (ETLA), aes-
thetic (ALA).
3.3. Aggregating information
According to our approach, the follow-
ing actions should be taken for the reasonable
choice of the problem solution.
Step 1. Formulating matrices of evalua-
tions. As HFLTS allows to use multiple lin-
guistic scales and there is no need to translate
evaluations to a single scale, the only needed
transformation is to translate all evaluations
to the form of HFLTS. Let is suppose, that
DATA ANALYSIS AND INTELLIGENCE SYSTEMS
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26
an expert gave the evaluation ("good", "vary
good"). The evaluation can be translated to the
instance of Hesitant 2-tuple Set: ,
where S 7 = – very bad, – bad, – slightly
fair, – fair, – slightly good, –good, –
very good .
After that, all evaluations are in a united form
and it is possible to start aggregating them. It is
mandatory to define weights for criteria and the
levels of abstraction. In this case, because there
are no presuppositions on importance neither
for criteria nor for alternatives, weights are equal
among both the alternatives and the criteria.
Step 2. Aggregating evaluations for criteria.
The very first step is to find the aggregated esti-
mation for every expert, every alternative and
every level of abstraction. Aggregation hap-
pens across criteria which belong to the same
level of abstraction. In our example we assume,
that the expert of Department of Foreign and
Domestic Politics (DFD) gave following esti-
mations for the alternative A.ETLA.1 (Table 2)
on a political level of abstraction (PLA).
For example, we consider the weights of the
criteria to be equal: w = (0.33, 0.33, 0.33).
For calculating an aggregated evaluation, the
MHTWA operator is used:
aggreagated_value =
Step 3. Translation to the levels of abstrac-
tion. This is the technical transformation of
given matrices and it is described in Step 3 of
the proposed methodology.
Step 4. Aggregation of evaluations by
experts. During this step, the accumulated
evaluation for each alternative, each level of
abstraction and each expert is calculated. In
this case, experts’ weights are distributed in a
way that the expert who gives the most precise
evaluation has the bigger weight.
Table 1. Experience of experts participating in the evaluation
MLA ELA SLA LLA PLA EPLA ETLA ALA
DFD x x x x x
DSP x x x
F x x x
M x x x
SA x x x
RT x x
E x x x x
Table 2. DFD evaluations for the alternative A.ETLA.1
Criteria on PLA
C.PLA.1 C.PLA.2 C.PLA.3
A.ETLA.1
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Step 5. Aggregation of evaluations by lev-
els of abstraction. During this step, evaluations
are accumulated by each level of abstraction
to get the final evaluation for each alternative.
Table 3 shows the results of aggregation for the
described case.
Table 3. The ordered list of alternatives
and accumulated evaluations
Alternative name Estimation
A.ELA.7 Increase crop via irrigation system implementation
A.SLA.3 Decrease usage of fertilizers
A.ELA.2 Increase taxes for farmers
A.ELA.1 Increase subsidies for farmers
Step 6. Seeking the best alternative. Dur-
ing this step, the best alternative is chosen. For
that, the list of calculated evaluations should
be ordered according to the rules of compar-
ing instances of Hesitant 2-tuple Set. In the
described case, the best alternative is the one
with id A.ELA.7 "Increase crop via irrigation
system implementation".
Step 7. Communication of the solution
found. All the participants of the decision
making problem are notified about the solu-
tion found. It is important to draw attention to
the fact that to find the solution, multiple alter-
native solutions were assessed against multiple
criteria and, which is more important, each
alternative solution was analyzed separately
on different level of abstraction representing a
vital aspect of the problem situation.
4. Implementation details
4.1. MAS design and implementation
For validation of the proposed LDM multi-
level model and our approach in general, an
expert system was developed and tested for a
relevant use case. The system was originally
designed as a distributed multi-agent system
(MAS) with a belief-desire-intention (BDI)
architecture [33]. It is a promising set of prin-
ciples for designing an MAS and has practical
use in various projects, like supply chain mod-
eling [34], transport logistics [35] and time-
tabling [36]. During design and implemen-
tation, we exploited advanced features of the
MAS platform JASON1 and its extension JaC-
aMo framework2. JASON provides a power-
ful AgentSpeak interpreter and basic commu-
nication primitives, while JaCaMo offers such
environment artifacts as tasks, bids, etc. New
numerical and linguistic algorithms related to
our proposed LDM multi-level models were
implemented in Java and then were encapsu-
lated to the JASON coordinator agent using
the Java-AgentSpeak proxy. The architecture
of the MAS is presented in Figure 3. A detailed
explanation of the level of implementation is
given in Figure 4.
There are always two types of agents availa-
ble in the system: a coordinator and an expert.
While it is enough to have a single coordina-
tor to rule the whole decision process, there
are multiple expert entities that make evalua-
tions based on the problem context. A number
of experts in simulation represents one-to-one
mapping to experts in the real life.
The coordinator is an agent that has two main
goals: starting the decision making process and
accumulation and calculation of the best alter-
native solution based on the evaluations pro-
vided. At the same time, coordinator activates
the main goal of the expert by publishing the
task in the Common Environment Artifact:
giving evaluations for the given problem on
the basis of alternatives and criteria provided 1 http://jason.sourceforge.net/wp/1 http://jacamo.sourceforge.net/
DATA ANALYSIS AND INTELLIGENCE SYSTEMS
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Fig. 4. Jason implementation of MAS
Fig. 3. Multi-agent architecture for multi-attribute LDM
Subscribe
Subscribe Publisch
Legend
Find bestalternative
Bid
Task
Artifact
Bids
Estimatcs
Ecolog Farmer Transporter Miller Miller Agent Politican Social Politican Coordinator
Coordinator
Scales
Expert-specific component
Problem-specific component
Problem-agnosticcomponent
CriteriaProblem
Description
coordinator.asl
2 5
3
4
1
Winneralternative
expert.asl
Achieve:focus (Common Environment) Winner
Expert
Legend
Common Environment
start (task_name, criteria, alternatives)
decide (task_name, criteria, alternatives)
sove_tasts (name, criteria, alternatives)
Bid
Task
Goal Publisch Communicate Belog Agent Artifact
Alternatives
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by coordinator. Once all the needed evalua-
tions are made, the coordinator tries to achieve
his second goal – finding out what alterna-
tive is best according to our LDM multi-level
model. As for an expert agent, its only goal is to
give evaluations by publishing in the Common
Environment artifact. Both coordinator and
expert agents are subscribed to the entity of the
winner in the Common Environment artifact
and get notified when it appears after all calcu-
lations are done.
4.2. Description of decision making in the MAS expert system
The algorithm of the decision making in our
multi-agent expert system follows the formal
methodology of our approach. During an ini-
tialization phase, the coordinator provides
experts with information on the common envi-
ronment (CE) where they will work together.
Experts also get prepared by subscribing to the
task to be notified when it is published. When
experts get notification about the new task,
they start providing their evaluations of the
given problem situation. Moreover, experts
subscribe to the winner alternative (WA) to
be aware of the best alternative. It is chosen
based on the evaluations of all agents. When
all preparations are done and experts are wait-
ing for the task to appear, the coordinator
publishes the task. All tasks contains the prob-
lem description, alternatives and criteria –
all necessary information for experts to ana-
lyze the problem and evaluate every alterna-
tive by given criteria.
After experts evaluate every alternative solu-
tion of the given problem, they publish bids
that contain these evaluations alongside the
description of scales that were used during
the decision process. These bids are handled
and stored in the common environment. The
coordinator either waits for all experts to pro-
vide evaluations or waits for a certain, explic-
itly defined period and then closes the admis-
sion. As soon as the admission is closed, the
coordinator initiates accumulation of all the
evaluations that is performed according to
the formal algorithm proposed in this paper.
When the calculations are finished, the win-
ning alternative (WA) is published and every
expert is notified about it. This appears to be
the end of the simulation, however the sys-
tem can be still active and waiting for a new
request.
The implementation of algorithms of aggre-
gation of heterogeneous estimations was
aligned with corporate enterprise standards
of software development. Furthermore, the
authors elaborated the input/output format
for describing the important parameters (crite-
ria, alternatives, levels, experts). The software
implementation of the prototypes is available
publicly on GitHub3 and contains the com-
plete system described in Figure 3. It can be
further extended for a more general case.
Conclusion
In the framework of current research, we
have made a broad investigation of the field
and aligned research with design science [37]
methodology. Rigorous analysis of existing
approaches to linguistic multi-criteria deci-
sion making revealed their disunity and inferi-
ority if applied to problems with heterogene-
ous information and uncertainty of context.
On the one hand, there are classical decision
making approaches that instruct each expert
to find the best alternative, however quanti-
tative estimations are not taken into consid-
eration. On the other hand, methods of LDM
are supposed to tackle heterogeneous estima-
tions, though they are hardly applied to real
life problems due to lack of unified method-
ology for searching for the best alternative.
More importantly, poorly structured prob-
lems are characterized by a huge number of
stakeholders. 3 https://github.com/demid5111/lingvo-dss-bdi
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About the authors
Alexander V. Demidovskij
Doctoral Student, Department of Information Systems and Technologies,
National Research University Higher School of Economics,
25/12, Bolshaya Pecherskaya Street, Nizhny Novgorod 603155, Russia;
E-mail: [email protected]
Eduard A. Babkin
Cand. Sci. (Tech.), PhD (Computer Science);
Professor, Department of Information Systems and Technologies,
National Research University Higher School of Economics,
25/12, Bolshaya Pecherskaya Street, Nizhny Novgorod 603155, Russia;
E-mail: [email protected]
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Developing parallel real-coded genetic algorithms for decision-making systems of socio-ecological and economic planning
Andranik S. Akopov a,b
E-mail: [email protected]
Armen L. Beklaryan a,b
E-mail: [email protected]
Manoj Thakur c
E-mail: [email protected]
Bhisham Dev Verma c
E-mail: [email protected]
a National Research University Higher School of Economics Address: 20, Myasnitskaya Street, Moscow 101000, Russia b Central Economics and Mathematics Institute, Russian Academy of Sciences Address: 47, Nakhimovky Prospect, Moscow 117418, Russia c Indian Institute of Technology Mandi Address: Mandi, Himachal Pradesh 175005, India
Abstract
This article presents a new approach to designing decision-making systems for socio-economic and ecological planning using parallel real-coded genetic algorithms (RCGAs), aggregated with simulation models by objective functions. A feature of this approach is the use of special agent-processes, which are autonomous genetic algorithms (GAs) acting synchronously in parallel streams and exchanging periodically by the best potential decisions. This allows us to overcome the premature convergence problem in local extremums. In addition, it was shown that the combined use of diff erent crossover and mutation operators signifi cantly improves the time effi ciency of RCGAs, as well as the quality of the decisions obtained (proximity to optimum), providing a more diverse population of potential decisions (individuals).
In this paper, several suggested crossover and mutation operators are used, in particular, a modified simulated binary crossover (MSBX) and scalable uniform mutation
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Introduction
Currently, there is a need to design
decision-making systems for socio-
economic and environmental plan-
ning using simulation models aggregated with
genetic optimization algorithms for solving
large-scale optimization problems.
For the first time, a similar approach was pro-
posed in [1–3] in which the developed multi-
agent genetic optimization algorithm MAG-
AMO was presented. MAGAMO is aggregated
through objective functions with a simulation
model of a distance trading enterprise. In [1],
operator (SUM), which is based on quantization of the feasible region of the search space (dividing the feasible region on small subranges with equal lengths) while taking into account the common amount of interacting agent-processes and the maximum number of internal iterations of GAs forming potential decisions through selection, crossover and mutation. Such a functional dependence of the parameters of heuristic operators on the corresponding process characteristics, aggregated with the combined probabilistic use of various crossover and mutation operators, makes it possible to get maximum effect from the multi-processes architecture. As a result, the computational possibilities of RCGAs for solving large-scale optimization problems (hundreds and thousands of decision variables, multiple objective functions) become dependent only on the physical characteristics of the existing computing clusters. This makes it possible to efficiently use supercomputer technologies.
An important advantage of the proposed system is the implemented integration between the developed parallel RCGA (implemented in C++ and MPI) and the simulation modelling system AnyLogic (Java) using JNI technology. Such an approach allows one to synthesize real world optimization problems in decision-making systems of socio-economic and ecological planning, using simulation methods supported by AnyLogic. The result is an eff ective solution to single-objective and multi-objective optimization tasks of large dimension, in which the objective functionals are the result of simulation modeling and cannot be obtained analytically.
Key words: real-coded genetic algorithms; multi-objective optimization; Pareto front; simulation
modeling; AnyLogic.
Citation: Akopov A.S., Beklaryan A.L., Thakur M., Verma B.D. (2019) Developing parallel
real-coded genetic algorithms for decision-making systems of socio-ecological and economic
planning. Business Informatics, vol. 13, no 1, pp. 33–44
DOI: 10.17323/1998-0663.2019.1.33.44
the objective functions, in particular, were the
accumulated profit, the size of the active client
base and the inventory turnover. At the same
time, a similar model included five product cat-
egories, six cities and three customer segments,
which, taking into account multiple restric-
tions and temporal granularity, characterized it
as a large-scale optimization problem.
Note that MAGAMO [3] uses the dynamic
interaction of synchronized intelligent agents,
each of which is an autonomous genetic algo-
rithm (GA) that implements an internal pro-
cedure for the formation of an archive of
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non-Pareto solutions, for example, SPEA2
(Strength Pareto Evolutionary Algorithm)
[4]. In MAGAMO, the dimensionality of the
solved optimization problem is reduced by
splitting the initial set of decision variables
into small groups with their subsequent distri-
bution between process agents (autonomous
GAs) to minimize the size of local populations
and the number of necessary (resource-inten-
sive) recalculations of fitness function values,
respectively. The MAGAMO algorithm was
previously used, in particular, for the rational
control of environmental modernization of
enterprises that are stationary sources of harm-
ful emissions [5], to determine the best geolog-
ical and technical activities on wells [6], etc. In
[7], some modification of this heuristic algo-
rithm through inclusion of an adaptive mecha-
nism provided improving values of GA param-
eters on the individual level of agent-processes
depending on the optimization results (values
of minimized target of functions, the rate of
convergence, the hypervolume metric of the
Pareto front, etc.).
At the same time, a significant deficiency
of MAGAMO is the use of binary coding of
decision variables values, which causes use of
classical operators of a single-point and two-
point crossover, as well as inversion (binary)
mutation. As a result, the time-efficiency of
the algorithm reduction if there is a need to
search for solutions in a continuous space of
high dimensionality, i.e. when wide values of
feasible ranges are specified for decision vari-
ables (for example, [–100, 100]), and there
are increased requirements for the precision of
computations (when the number of bits of the
mantissa is 2 or more).
Another problem is the weak mutual aggre-
gation of agent-processes in MAGAMO and
the need to synchronize their states (replica-
tion of the values of decision variables between
processes) at each GA iteration, all of which
significantly reduces the efficiency of process
parallelization.
Therefore, it is necessary to create a funda-
mentally new parallel genetic algorithm using
the mechanism of real coding, i.e. belonging
to the class of RCGA algorithms (real-coded
genetic algorithms) [8] and this is based on
using new heuristic operators of the appro-
priate type providing a mechanism of peri-
odic exchanges of the best potential decisions
between agents-processes.
The purpose of this paper is to develop a
multi-agent parallel real-coded genetic algo-
rithm for solving multi-objective optimiza-
tion problems (MA–RCGA–MO) aggregated
through the objective functions with AnyLogic
simulation models. In the result, there is provi-
sion for solving large-scale optimization prob-
lems in decision-making systems for socio-
economic and environmental planning.
It should be noted that the choice of the Any-
Logic system is mainly prompted by the impor-
tant advantages of the platform, such as sup-
porting system dynamics methods, discrete
event modeling and agent-based modeling
within one model [9]. This allows you to design
decision-making systems that require the devel-
opment and use of complex simulation models.
Examples are the system of rational control of
ecological-economic systems [10–13], the sys-
tem of modelling and optimization of the set of
investments of a vertically integrated oil com-
pany [14–15], the system of optimal distribu-
tion of flows of requests for loans at the inter-
regional underwriting center of a very large
bank [16], the system of control of intellectual
agent-rescuers behavior in the simulation of
human crowd behavior in an emergency [17–
18] and other systems.
1. Multi-agent parallel real coded genetic algorithm
Currently there is a line of well-known
research on genetic optimization algorithms
designed to solve multi-objective optimiza-
tion problems. Among the most often used
methods, the following algorithms should be
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highlighted: SPEA2 [4], MOEA/D (multi-
purpose GA based on decomposition) [19],
NSGA-II and NSGA-III (multi-objective GA
based on non-dominated sorting) [20, 21] and
some other algorithms. In addition, the appli-
cation of agent-based modelling for imple-
menting GA is known as well. In particular,
the MAGA algorithm [22] that is intended
for solving large-scale optimization problems
should be noted. Despite the multiple advan-
tages of developed GAs, most of them use the
binary coding mechanism for decision varia-
bles, which causes significant loss of time-effi-
ciency when searching for solutions in a large-
scale continuous space. Accordingly, this limits
the possibility of using such GAs in designing
decision-making systems based on simulation
modelling of the behavior of complex objects.
In order to overcome these difficulties, a new
multi-agent parallel real-coded genetic algo-
rithm is proposed. The algorithm is intended
for solving large-scale multi-objective optimi-
zation problems.
The main features of the suggested algorithm
are the following:
using well-known crossover and mutation
operators designed for real-coded genetic algo-
rithms (RCGAs), such as SBX crossover (sim-
ulated binary crossover) [23], Laplace crosso-
ver (LX) [24], power mutations (PM) [25] and
others;
using new (modified) heuristic crosso-
ver and mutation operators, the characteris-
tics of which functionally depend on the indi-
vidual number of the associated agent-process
(i.e., the process in which they are performed).
This makes it possible to significantly improve
their efficiency, in particular, to achieve bet-
ter diversity of potential decisions, to provide
splitting (quantization) of search ranges into a
larger number of short intervals and, thus, to
use maximally the capabilities of a multi-clus-
ter (multiprocessor) computing system while
increasing the time-efficiency of GAs;
combined use of various heuristic opera-
tors (both existing and proposed) at the indi-
vidual level of interacting agents-processes for
the formation of new potential decisions (off-
spring-individuals);
adding internal iterations to the GA, pro-
viding the generation of a larger number of
offspring-individuals and potential decisions,
respectively;
providing the mechanism for periodi-
cally exchanging the best potential decisions
between agents-processes to avoid the jam-
ming problem of the GA at local extremes and
achieve an acceptable rate of population evolu-
tion for large-scale optimization problems.
An abstract description of the multi-agent
parallel genetic algorithm so developed is given
below.
Here,
i = 1, 2, ..., n – the index of decision variables
defining the values of the objective functions;
{ pi1, p
i 2 } – the pair of parent decision vari-
ables (parent-individuals) formed in the result
of the selection procedure (for example, using
tournament selection) for all i-ths decision
variables (i = 1, 2, ..., n);
– the pair of descendants (offspring-
individuals) formed by parents for all i-ths
decision variables (i = 1, 2, ..., n);
u (a, b), l (a, b), s (a, b) – random numbers
evenly distributed on the range of [a, b];
(k = 1, 2, ..., K ) – the index of parallel agent-
processes (GA), where K is the maximum
number of agent-processes in parallel GA;
gk
= 1, 2, ..., Gk – the index of internal itera-
tions belonging to the k-th agent-process.
The following new heuristic operators are
suggested for the real-coded genetic algorithm:
modified simulated binary crossover
(MSBX), provided the generation of potential
decisions in the continuous search space:
(1)
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(2)
(3)
(4)
N = gk + 2, (5)
i = 1, 2, ..., n, k = 1, 2, ..., K, gk
= 1, 2, ..., Gk,
where , are coefficients (parameters of a
crossover), N is the parameter simulated the
number of bits in GAs with a binary coding
(N [2, Gk ]);
modified discrete SBX-crossover
(DMSBX), provided the generation of poten-
tial decisions in the discrete search space:
(6)
(7)
(8)
i = 1, 2, ..., n.
scalable uniform mutation operator
(SUM), provided quantizing of the feasible
ranges of decision variables into uniform inter-
vals to obtain potential solutions outside the
area of local extremes:
(9)
(10)
i = 1, 2, ..., n, gk
= 1, 2, ..., Gk, k = 1, 2, ..., K.
Note that all considered heuristic operators
are executed with a given probability. At the
same time, the probability of the execution of a
crossover operator at each iteration of the GA
is close to one (that is, the crossover is the most
important GA operator with real coding). The
probability of a mutation operator is selected
taking into account the relief of the objective
functions of the solved problem and, as a rule,
it is at the range [0.001, 0.1] while minimizing
the objective functions having relatively simple
relief, and in the range [0.1, 0.5] while minimiz-
ing complex objective functions with multiple
local extremes located near the global optimum.
Here,
tk
= 1, 2, ..., Tk – index of the external itera-
tions of the k-th agent process of GA, where Tk
is the number of external iterations;
gk = 1, 2, ..., G
k – the index of internal itera-
tions of the k-th agent process (GA), where Gk
is the number of internal iterations;
{LX, SBX, MSBX, DMSBX} – the set of pos-
sible crossover operators chosen with equal
probability at each tk-th step of GA, where LX
is the Laplas crossover, SBX – the standard
SBX-crossover, MSBX – the modified SBX-
crossover, DMSBX – the modified discrete
SBX-crossover;
{PM, UM, DUM, SUM} – the set of possible
mutation operators chosen with equal proba-
bility at each tk-th step of GA, where PM is the
power mutation operator, UM – the standard
operator of a uniform mutation, DUM – dis-
crete operator of a uniform mutation, SUM –
a scalable operator of a uniform mutation;
– the frequency of exchanging the best
potential decisions between all k-th process
agents (k = 1, 2, ..., K ).
Thus, the aggregated block diagram of the
proposed multi-agent parallel real-coded GA
developed for multi-objective optimization
(MA–RCGA–MO) can be presented in the
following form (Figure 1).
Note that the proposed GA is implemented
for the each parallel agent-process that peri-
odically exchanges the best (non-dominant
Pareto) potential decisions through the global
archive with all other agent- processes. Such
an approach can significantly increase the rate
of searching the Pareto-optimal solutions and
overcome the problem of a premature con-
vergence associated with frequent jamming
of GA at local extremes. Figure 2 shows the
aggregated architecture of the developed deci-
sion-making system in which the AnyLogic
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Fig. 1. The block diagram of the developed multi-agent parallel real coded GA
Clearing the parent pool of potential decisions and the archive of non-dominated Pareto solutions.
Selection of a crossover operator from the set
Selection of a mutation operator from a set
Conducting tournament selection to form a pool of the most adapted parent-individuals.
Probabilistic selection of a pair of parents from the parent pool:
Execution of crossover and mutation operators to generate new potential decisions (offspring-individuals).
Computation of objective and fitness functions using AnyLogic for offspring-individuals:
Updating the population of the most adapted (non-dominant Pareto) individuals.
Updating the global population of the best (non-dominated) solutions, with a given frequency, i.e. if the following condition is performed:
Updating the local population of the -th agent-process by the best solutions from the global population with a given periodicity.
Stopping the GA when the required level of the rate of convergence is reached (the degree of stabilization of the fitness function values) for the global population.
YES
YES
NO
NO
Initialization of agent-process parameters (GA).
Generation of the initial population.
Calculation of the values of the objective functions in AnyLogic for each initial vector of decision variables.
The end
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simulation models are integrated with the
developed genetic algorithm (MA–RCGA–
MO). The algorithm is implemented in C++
programming language using the MPI (Mes-
sage Passing Interface) technology, which
makes it possible to provide an efficient pro-
cedure of data exchange between all agents-
processes. Different software tools (for exam-
ple, GIS maps, graphs, tables, etc.) can be
used to provide the presentation of optimiza-
tion results previously written in the database
of the system (Oracle).
An important advantage of the suggested
architecture is the integration of the developed
parallel GA with AnyLogic simulation mod-
els (implemented on Java) using the JNI tech-
nology (Java Native Interface). Note that cur-
rently there are parallelization technologies for
the Java platform, for example, MPJ1, which
can also be applied to the AnyLogic models.
However, when solving large-scale optimiza-
tion problems, the most important factor is
the performance of the corresponding com-
putational procedures that can be significantly
improved only by using C++ and MPI tech-
nologies.
Fig. 2. The aggregated architecture of the decision-making systemfor socio-economic and ecological planning
1 http://www.mpjexpress.org/
JDBC
JDBC
Exporting the model as an executed JAR-file
AnyLogic simulation with implementation using Java
programming language (JAR file)
Oracle Call Interface (OCI LIB)
Runtime.getRuntime().exec()
JNI
Datasets for AnyLogic models, values of agent-processes parameters
and optimization results
Control Panel for the AnyLogic model
Multi-agent parallel genetic algorithmfor multi-objective optimization
(C++ and MPI)
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2. An example of the practical implementation of the system for support
of decision-making
An example of a decision-making system
implemented for ecological and economic plan-
ning tasks related to the rational greening of the
city is considered further. Earlier we developed an
agent-based simulation model of the distribution
of harmful emissions in the city (in the AnyLogic
system) on the example of Yerevan, Republic
of Armenia [10]. Initially searching for the best
solutions in the model was conducted using a
parallel genetic algorithm with binary coding,
which required a lot of time for the generation
of a subset of the Pareto optimal solutions (sev-
eral hours of iterative calculations) on the server
HP ProLiant DL 380 GB with two 6 core proces-
sors Intel Xeon CPU E5645, 2.4 GHz and 64GB
of RAM, due to the large dimensionality of the
optimization problem being solved.
It should be noted that in the simulation model
two minimized objective functions were defined.
The first is the average daily pollution concen-
tration estimated in protected urban areas (in
particular, in the areas of kindergartens), as well
as the budget needed for greening the city to
ensure the natural protection of socially impor-
tant objects from harmful emissions produced by
enterprises and transport. At the same time, 111
kindergartens were previously selected for pro-
tection by trees at the individual level, taking into
account the variability of such parameters as the
type of trees (for example, poplar, maple, oak,
spruce, elm), the distance between the clusters
of trees (from 5 to 60 meters), the radius of the
planting zone (from 30 to 100 meters) and the
geometry of planting trees around kindergartens
(for example, a simple circle, an arithmetic spi-
ral, a double circle, etc.).
In the result of the application of the devel-
oped multi-agent parallel real-coded genetic
algorithm (MA–RCGA–MO), the time-effi-
ciency of the search procedure for optimum
solutions to the considered problem of complex
ecological and economic system of the city was
significantly improved. Using MA–RCGA–
MO, the best scenario was found for a polyno-
mial time, providing almost fourfold reduction
in the concentration of harmful emissions in
the atmosphere in protected urban areas with
an acceptable level of greening expenses. The
optimization results, previously saved on the
Oracle DBMS, were visualized on the map of
Yerevan using the AnyLogic system (Figure 3).
Conclusion
This paper presents a new multi-agent paral-
lel real-coded genetic algorithm MA–RCGA–
MO, which provides an effective procedure for
finding Pareto optimal solutions in large-scale
multi-objective optimization problems.
An important feature of the suggested genetic
algorithm is use of new heuristic crossover and
mutation operators, the characteristics of which
functionally depend on the number of the associ-
ated agent-process, as well as providing a mech-
anism for periodic exchange of the best potential
decisions between all agents-processes to avoid
a premature convergence (caused by potential
jamming the GA at local extremes) and increase
the rate of search for optimal solutions.
The important advantage of the suggested
multi-agent GA is its aggregation with the
AnyLogic simulation models through objective
functions. At the same time, C++ program-
ming language and MPI technology provide an
effective procedure for periodically exchanging
the best potential decisions, and the JNI tech-
nology provides the ability to integrate the GA
with the AnyLogic models.
In future work, it is planned to implement dif-
ferent approaches to the generation of Pareto
optimal solutions (for example, NSGA-III) for
the developed multi-agent parallel genetic algo-
rithm with studies of the effectiveness of appro-
priate modifications. Moreover, we expect the
implementation of multi-agent genetic algo-
rithm using technology CUDA (Compute Uni-
fied Device Architecture).
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Fig.
3. V
isua
lizat
ion
of th
e re
sults
of m
inim
izatio
n of
har
mfu
l em
issi
ons
in th
e ci
ty u
sing
the
prop
osed
gen
etic
alg
orith
m
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About the authors
Andranik S. Akopov
Dr. Sci. (Tech.);
Professor, Department of Business Analytics, National Research University Higher School of Economics,
20, Myasnitskaya Street, Moscow 101000, Russia;
Chief Researcher, Laboratory of Dynamic Models of Economy and Optimization,
Central Economics and Mathematics Institute, Russian Academy of Sciences,
47, Nakhimovky Prospect, Moscow 117418, Russia;
E-mail: [email protected]
Armen L. Beklaryan
Cand. Sci. (Tech.);
Associate Professor, Department of Business Analytics, National Research University Higher School
of Economics, 20, Myasnitskaya Street, Moscow 101000, Russia;
Senior Researcher, Laboratory of Social Modeling, Central Economics and Mathematics
Institute, Russian Academy of Sciences, 47, Nakhimovky Prospect, Moscow 117418, Russia;
E-mail: [email protected]
Manoj Thakur
PhD;
Associate Professor, School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, Himachal
Pradesh 175005, India;
E-mail: [email protected]
Bhisham Dev Verma
Doctoral Student, School of Basic Sciences, Indian Institute of Technology Mandi, Mandi, Himachal
Pradesh 175005, India;
E-mail: [email protected]
MODELING OF SOCIAL AND ECONOMIC SYSTEMS
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45
Development of strategic management tools for heat supply enterprises in the Donetsk region
Mariya A. Myznikova a
E-mail: [email protected]
Larisa N. Brazhnikov b
E-mail: [email protected]
a Donetsk National University Address: 24, Universitetskaya Street, Donetsk 283001, Ukraineb Academy of Economic Sciences of Ukraine Address: 2, Zhelyabova Street, Kiev 03057, Ukraine
Abstract
Raising the eff ectiveness of strategic management in conditions of high complexity and dynamic change of modern management systems requires the development of an appropriate mathematical toolkit. The task of raising eff ectiveness of strategic management is especially topical for heat supply enterprises of the Donetsk region, where operations have been complicated by a number of general system problems, and by the presence of substantial external challenges. At the same time, the question of using mathematical apparatus to raise the eff ectiveness of strategic management of enterprises in the sphere of residential-communal services appears not to have been widely studied. In this regard, the objective of this study is raising the eff ectiveness of strategic management of heat supply enterprises of the Donetsk region by developing a respective toolkit of mathematical modeling. To achieve the goal we have set, in this work we carried out an analysis of the viability of the system using the methodology proposed by S. Beer; we made an analysis of the elements of the market of heat supply, and also developed system dynamic models based on the approach of J.W. Forrester.
As a result of our research, we discovered the basic problems infl uencing the viability of the system at the strategic level. It was established that the problems revealed are the consequence of the imperfections of the methodological base, including absence of timely information on the dynamics of the external environment, forecasting of the key parameters, a toolkit for making decisions, etc. For the purpose of fi nding a toolkit to improve the methodological base, we performed an analysis and forecast of the heat supply market in the Donetsk region as part of the external environment which exerts a very signifi cant infl uence on the activity of the heat supply enterprises of the Donetsk region.
In the course of this market analysis, we established that the off er of heat supply services is not constant and depends on the tariff setting costs. Due to this, we proposed an approach to forecasting tariff setting costs based on the methodology of A.G. Ivakhnenko but distinguished from that by the
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Introduction
The complexity of contemporary eco-
nomic systems resulting from the
high agility of the processes occur-
ring in them, by the presence of external chal-
lenges, by the large quantity and non-linearity
of links of elements in such systems, leads to
reduced effectiveness of traditional methods of
management and makes it very topical to use
a complex approach based on the toolkit of
mathematical modeling.
The desirability of raising the effectiveness
of strategic management by developing a com-
plex toolkit acquires special relevance for heat
supply enterprises of the Donetsk region, due
to several factors: the primary importance of
heat supply services to protect the lives of the
population and ensure the functioning of the
region’s companies, as well as the scale of the
regional market of residential communal ser-
vices (including heat supply services). Thus,
the residential communal entities of the region
cover the needs of around 2.5 million people, as
well as more than 180 major enterprises. More
than 8% of the working population is engaged
in this sphere.
Furthermore, the problems with which
heat supply enterprises of the Donetsk region
encounter bear a deep and general systemic
character. These include a high degree of dete-
rioration of the basic assets, insufficient vol-
umes of financing, low solvency of the con-
sumers, large amounts of receivables, etc.
Solutions to the enumerated problems
require high effectiveness of strategic manage-
ment, which is difficult to achieve when the
respective toolkit is missing.
A whole series of both foreign and domestic
works has been devoted to the issues of find-
ing more effective approaches to management
(including strategic management) of heat sup-
ply enterprises, among them by E.Yu. Adzh-
agulov [1], D.L. Bakieva [2], E.V. Baland-
ina [3], Е.Е. Vorobieva [4], А.V. Darbasov
[5], Т.А. Makarenya [6]. Such scholars as
А.V. Allakhverdyan [7], L.N. Brazhnikova
[8, 9], S.G. Kulikov [10], R.N. Lepa [11],
Ya.A. Lyashok [12], E.A. Perkova [13],
V.P. Poluyanov [14], and I.A. Yurchenko [15]
are among those who have dedicated their
works to the specifics of heat supply companies
of the Donetsk region.
At the same time, despite the attention
researchers have given to questions of the
effectiveness of management of companies in
the residential communal sphere the problem
presence of a training sample and two test samples. In addition, in the course of analyzing the market we discovered new forms of demand for heat supply services – lost demand and unpaid demand. On the basis of the dependencies established, we built a model for forecasting the behavior of consumers of a heat supply company oriented to the level of marketing. With the help of this model, by means of supplements to it and modifi cations, we built a complex model of strategic management of heat supply enterprises of the Donetsk region allowing us to analyze the eff ectiveness of using one or another lever of strategic management on the basis of scenario analysis.
Key words: strategic management; system dynamic modeling; sustainability; tariff setting costs;
heat supply enterprise.
Citation: Myznikova M.A., Brazhnikova L.N. (2019) Development of strategic management tools
for heat supply enterprises in the Donetsk region. Business Informatics, vol. 13, no 1, pp. 45–58.
DOI: 10.17323/1998-0663.2019.1.45.58
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of developing an effective toolkit of strategic
management of heat supply enterprises remains
unresolved. Thus, works devoted to application
of the apparatus of economic-mathematical
modeling and modern tools for raising effec-
tiveness of strategic management in the sphere
of residential communal services have focused
on solving the problems at the regional and
municipal levels. Meanwhile, the questions of
developing a toolkit for mathematical mode-
ling to raise the effectiveness of strategic deci-
sions at the level of management of individual
heat supply enterprises remain among the least
developed, all of which predetermine the time-
liness of the line of research we have chosen.
In connection with the foregoing, the objec-
tive of this research is to raise the effectiveness
of strategic management of heat supply enter-
prises of the Donetsk region by developing a
respective toolkit of mathematical modeling.
In accordance with the goal of the research,
we formulated a methodological base which is
comprised of the works of a number of authors
devoted to the questions of raising effectiveness
of management of business systems by using
mathematical apparatus [16–27].
1. Analysis of the viability
of a heat supply enterprise
of the Donetsk region
Raising the effectiveness of strategic manage-
ment of a heat supply enterprise of the Donetsk
region requires that we carry out a retrospec-
tive analysis of the functioning of the system, as
well as that we reveal and systematize the basic
problems of enterprises in the given sphere.
For this, in the framework of a heat supply sys-
tem, we can distinguish six subsystems relating
to various levels of management - strategic, tac-
tical and operative. In particular, at the strategic
level we find the subsystem for decision making
and the subsystem providing information. At the
tactical level, we find the subsystem for distribu-
tion of resources in short supply, the subsystem
of internal audit and the subsystem for resolving
current problems. At the operative level, there is
the “operational element” subsystem.
Let us examine the characteristics of the enu-
merated subsystems and the causes of problems
arising inherent in the current state of the heat
supply system.
The subsystem for decision making is char-
acterized by the fact that the basic functions
of decision making relating to the entire heat
supply system are accorded to the bodies of
departmental control. The reasons why prob-
lems arise are the economic and social ground-
lessness of the tariffs, as well as the imperfec-
tions of the methodological base which enables
one to react to deviations which arise.
The subsystem of information manage-
ment is supposed to send to the decision-mak-
ing subsystem information on the state of the
external world and basic trends of its change, as
well as information about the necessary action
in response to these changes. At the same
time, the de facto fulfillment by the informa-
tion management subsystem of its functions
is limited exclusively to stating the actual val-
ues over the preceding periods. The sources of
the problems – the lack of up-to-date infor-
mation about the functioning of the real sec-
tor of the economy, significant time lags, and
also the lack of formalization of threshold val-
ues of deviations below which it is necessary to
take control.
The function of the subsystem for distrib-
uting resources in short supply in the system
of heat supply is carried out by various insti-
tutions: distribution of subsidies is provided
from local budgets, other subsidies come from
the budget of the republic, investments from
all subjects of the market. Moreover, the dis-
tribution of investment flows at the micro
level is performed by the operational elements
independently. The sources of the problems –
ineffectiveness of the distribution of resources
in short supply, and also growth of expenses
for the bureaucratization of processes of issu-
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48
ing subsidies, financing and regulating the
system.
To provide for the functions of internal audit,
there are specialized commercial organiza-
tions whose services are paid by the enterprise.
However, the selection of these organizations
is done, as a rule, by local self-government
authorities. The causes of the problems exist-
ing in this area arise beyond the boundaries of
the given research.
The subsystem for resolving current problems
is represented by laws and sublegal documents
regulating the activity of enterprises from the
sectoral ministry and local authorities of self-
administration. Here among the sources of the
problems we can mention the legally nonreg-
ulated nature of the monopoly status of the
enterprises, the forms and methods of republic
tariff policy, as well as the forms and methods
for recovering accounts receivable.
Finally, the “operative element” subsystem is
represented in the form of enterprises of various
kinds of ownership and functional affiliation.
Here the causes of the problems which arise are
the deteriorated state of the basic assets, the use
of outdated technologies, high expenses and
low efficiency, an ineffective innovation and
investment policy, the unsatisfactory financial
condition of the enterprise, an ineffective pol-
icy on price formation, an ineffective system
of managing expenses, an imperfect system
of managing the receivables and credit policy,
the low quality of services provided, and low
attractiveness for investment.
Due to the specific external conditions of the
functioning of heat supply enterprises of the
Donetsk region (as with many other systems in
depressed territories) one of the most impor-
tant tasks of their operations is to achieve a sys-
tem of viability, which is taken to mean “the
ability of the system to independently support
its autonomous existence for as long as pos-
sible” [16]. This characteristic of the system
has been given the name “viability” and was
described in the works of S. Beer [16], as well
as by a broad circle of scholars in the context of
systemic and cyber approaches.
In connection with the foregoing, it is inter-
esting to analyze the problems of the function-
ing of heat supply enterprises of the Donetsk
region at various levels of a viable system. The
basic problems of strategic management of
a viable system of heat supply are related to
shortcomings of the subsystems of information
management and management decision mak-
ing.
These problems are the consequence of
shortcomings of the methodological base,
including lack of up-to-date information about
the dynamics of the external environment, an
ineffective approach to forecasting key param-
eters, as well as lack of a toolkit to support the
adoption of strategic decisions.
Due to the fact that one of the key prob-
lems of managing heat supply enterprises
of the Donetsk region is lack of up-to-date
information on the dynamics of the exter-
nal environment, and also proceeding from
the goals of raising the effectiveness of strate-
gic management of heat supply enterprises of
the Donetsk region, it is worthwhile to do an
analysis of and forecast of the external envi-
ronment. Moreover, in the context of a cyber
approach it is customary to separate out the
parts of the external environment which exert
the most significant impact on the subject of
the research (external supplement [16]). For
the enterprises we analyzed, the market of
heat supply in the Donetsk region can be seen
as the external supplement.
2. Analysis of the market elements
of heat supply: demand, supply, price level,
market conditions
We take the heat supply market to mean
the exchanges which develop between its par-
ticipants based on the sale-purchase of spe-
cific benefits (hot water supply and provision
of heating) which can be measured quantita-
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49
tively and described in the form of characteris-
tic behavior of market participants.
Analysis of the specialized literature made it
possible to reveal the basic common elements
of the market for residential communal ser-
vices. They comprise: offer of services by the
residential-communal companies, demand for
residential-communal services, tariffs for res-
idential-communal services and the market
conditions.
Given the limitation and complexity of influ-
ence on the market conditions, what is of prac-
tical interest for the purposes of management is
to review the categories of demand and supply
on the market of residential-communal ser-
vices, as well as the processes of price forma-
tion.
Development of the model basis for evalu-
ating the behavior of producers of heat sup-
ply services (i.e. evaluation of the offer) has
been described in the work [28]. In particu-
lar, it was established that offer by heat sup-
ply enterprises in the Donetsk region is not a
fixed amount, i.e., it changes under the influ-
ence of several factors. At the same time it
was shown that the use of price (tariffs) as the
main factor of the offer with respect to heat
supply services is wrong, insofar as the tariff
is a conditional value. In this connection, we
proposed to use as the basic factor tariff set-
ting costs. The dependency shown raises the
relevance of applying up-to-date methods of
forecasting tariff setting expenses of heat sup-
ply enterprises of the Donetsk region. For
determination of the character and closeness
of the bond, we analyzed the existing meth-
ods of selecting parameters and established
that their application is difficult under condi-
tions of the economic shocks which the econ-
omy of the Donetsk region has been experi-
encing ever since 2014. In this respect, in [28]
we see further development of the inductive
method of organizing models of complex sys-
tems proposed by A.G. Ivakhnenko [18]. The
approach to constructing a model for forecast-
ing expenses underlying the tariffs of heat sup-
ply enterprises is distinguished by the exist-
ence of training and two test samples, all of
which allows us to analyze the suitability of the
basic model under conditions of the economic
shocks of 2014–2015, and also the suitability of
the refined model for forecasting the following
trend of the readings.
Analysis of the system of tariff formation and
its interdependence with consumer behav-
ior (demand) in heat supply enterprises of the
Donetsk region allowed us to establish that low
solvency of the population causes a high level
of accounts receivable and serves as an impedi-
ment to establishment of economically justified
tariffs. In this connection, in [29] a method is
proposed for calculating the critical maximum
tariffs for services of residential-communal
service enterprises.
Study of the behavior of consumers (demand)
is described in the works [30, 31], where it is
proposed to examine lost demand, which is
expressed as refusal to buy services of central-
ized heat supply and as unpaid demand, which
takes the form of consumer debt (accounts
receivable).
Revealing the factors influencing the dynam-
ics of market elements of heat supply and
behavior of its subjects lay at the basis of our
building system dynamic modeling of strategic
management.
3. Constructing system dynamic models
of strategic management of a heat supply
enterprise of the Donetsk region
An original toolkit for system dynamic mod-
eling of strategic management of a heat sup-
ply enterprise consists of two basic models: an
simulation model of forecasting the behavior of
the enterprise’s consumers [32] and a complex
model of strategic management of a heat sup-
ply enterprise.
It should be noted that the simulation model
of forecasting the behavior of a heat supply
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50
enterprise’s consumers is oriented at the level
of marketing and therefore has a number of
limitations and simplifications. The complex
model of strategic management of a heat sup-
ply enterprise is based on a model of forecast-
ing the behavior of consumers but is geared to
the level of top leadership of the enterprise, and
therefore presupposes a larger number of man-
agement levers, as well as a smaller number of
limitations and simplifications.
3.1. The simulation model of forecasting the behavior of a heat supply
enterprise’s consumers
Changes in the behavior of consumers exert
an influence on the financial results of the
activities of heat supply enterprises. More-
over, it should be noted that rejection of the
services of heat supply enterprises exerts an
influence on the functioning of the enterprise
both in the short term and as regards long
term prospects.
Under conditions in which the heat sup-
ply enterprises of the Donetsk region operate,
consumer arrears (accounts receivable) have a
short term influence on the financial perfor-
mance of the enterprise, and also have a per-
sistent tendency to turn into hopeless, frozen
assets for the long term, changing the nature
of this influence from short term to long term.
Thus, one may conclude that an increase in
lost demand, as well as the growth in unpaid
demand exert an influence both on short term
and on long term financial results from opera-
tions in heat supply enterprise. It follows from
this that research into the behavior of consum-
ers has high theoretical and practical signifi-
cance for developing a toolkit to raise the effec-
tiveness of strategic management.
On the basis of the analysis carried out, the
equation of dependency of indicators of the
increased level of accounts receivable from the
population on the correlation of the level of
tariffs and the level of salaries can be presented
in the following manner:
(1)
where a01
, a11
– regression coefficients of the
model;
– the level of tariffs for the population of
heat supply services on the heat supply market
at moment in time t;
M t – the average level of salaries in the region
at moment in time t.
At the same time, the interest of enterprises,
unlike the population, is formed under the
influence of two factors – the quality of the ser-
vices offered and the system of material incen-
tives. There is practically no system of mate-
rial incentives in the heat supply market of the
Donetsk region. Due to this, it is worthwhile
reviewing the dependence of increased level of
consumer arrears (accounts receivable) on the
quality of services provided.
Tariffs exert an influence on the possibil-
ity and ability to pay for heat supply services.
Thus, growth in accounts receivable of heat
supply companies may be presented in the fol-
lowing way:
(2)
where – growth in the level of accounts
receivable of the k category of consumers for
the period [t0; t];
k – categories of consumers, k [1; 4];
W t – the quality of heat supply services at
moment in time t;
a0k
, a1k
, a2k
– regression coefficients of the
model.
Lost demand is also an indicator which to a
certain extent depends on the level of tariffs
and quality of services. Quality of heat sup-
ply services is an aggregate indicator which is
calculated from information about the quality
of boilers, networks and communications, as
well as the quality of the accompanying ser-
vice.
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Evaluation of the quality of the service for
heat supply services is made using a calculation
of an integral reading which changes within
a given range [0; 10] and is calculated using
the sum of partial coefficients determined by
expert evaluations.
The model obtained for forecasting the
behavior of consumers of a heat supply enter-
prise in its most simplified form can be pre-
sented in the form of a diagram of cause and
effect links (Figure 1).
3.2. The complex model of strategic
management of a heat supply enterprise
On the basis of our analysis of the model of
forecasting the behavior of heat supply enter-
prise consumers, one can conclude that the
problems of managing the behavior of the
enterprise’s consumers are in direct depend-
ency relations with effectiveness of the pol-
icy of tariff formation, as well as policy in the
sphere of quality of services provided.
At the same time, based on the orientation
of the model towards the level of the market-
ing department, it presupposes the following
assumptions and simplifications:
a change in the level of tariffs, as well as in
the level of quality over time, and not under the
influence of management decisions;
it does not presuppose analysis of the cost
and effectiveness of such management deci-
sions;
Accounts receivable
(population)
Accounts receivable
(enterprises)
Accounts receivable
(state owned entities)
Quality of boilers
Heat sales
Quality of final services
Average salary
Tariff setting costs
Current tariffs
Income received in liquid form
Lost income
Lost demand
Tariff level for
enterprises
Tariff level for
population
Level of dismantled personal accounts
Quality of networks and
communications
Quality of accompanying
service
Fig. 1. Model of forecasting the behavior of heat supply enterprises’ consumers in the form of causal links
Accounts receivable
Accounts receivable (municipal
entities)
Income
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52
it does not consider the investment policy;
the model does not allow us to trace the
influence of the indicators being analyzed on
the financial results of the enterprise’s activi-
ties.
In this connection, and also to improve the
methodology of managing a heat supply enter-
prise as a whole, it is interesting to see mod-
ification of the previously developed model
of forecasting the behavior of the enterprise’s
consumers by changing it and supplementing it
to reach a complex model of strategic manage-
ment of a heat supply enterprise.
Based on the assumptions and simplifica-
tions described above, it is proposed to make
the following changes in the complex model of
strategic management:
to establish the mutual dependency between
the level of tariffs and the quality of services, as
well as with the management decisions adopted;
analysis of the elasticity of the level of
quality and service in relation to the expenses
needed to raise it;
analysis of the effectiveness of manage-
ment decisions and, in particular, decisions on
the investment policy;
inclusion in the model of the indicator of
financial results from the enterprise’s activities
as a resulting element.
In this regard, in the complex model the qual-
ity of boilers, equipment, networks and com-
munications depends on the total investments
of the enterprise, with a delay in two periods
(quarters), and the level of service depends on
the total investments or the enterprise in the
current period.
The effectiveness of the management deci-
sions adopted is determined according to the
formula:
(3)
where – the effectiveness of the m-th man-
agement decision at moment in time t;
– the financial result (profit/loss) at
moment in time t under conditions of the
implementation of the m-th management
decision;
– the financial result (profit/loss) at
moment in time t under conditions of the
implementation of the basic scenario (keeping
the current dynamics of the indicators);
– total expenses of the heat supply enter-
prise at moment in time t under conditions of
the implementation of the m-th management
decision;
– total expenses of the heat supply enter-
prise at the moment in time t under conditions
of implementation of the basic scenario (keep-
ing the current dynamics of the indicators).
It should be mentioned that the calcula-
tion of the financial result at moment in time t
presupposes inclusion of the accounts receiv-
able. At the same time, the accounts receiv-
able of heat supply enterprises of the Donetsk
region have very low liquidity. Due to this, we
introduced the term “absolutely liquid finan-
cial result,” meaning the financial result of
a heat supply enterprise without considering
accounts receivable. Thus, there is enhanced
interest in the calculation of the indica-
tor of effectiveness of management decisions
expressed in the increment of absolutely liq-
uid financial results:
(4)
where – the effectiveness of the m-th man-
agement decision expressed as the increase of
absolutely liquid financial results at moment in
time t (liquid effectiveness);
– absolutely liquid financial results
(profit/loss) at moment in time t under condi-
tions of the implementation of the m-th man-
agement decision;
– absolutely liquid financial results
(profit/loss) at moment in time t under condi-
tions of the implementation of the basic sce-
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53
nario (maintaining the current dynamic of the
indicators).
Thus, liquid effectiveness is the liquid effect
correlated with costs for achieving it.
On the basis of the foregoing, and also on
the basis of the previously described model
of forecasting the behavior of consumers of a
heat supply enterprise we developed a complex
model of strategic management of a heat sup-
ply enterprise of the Donetsk region. The main
elements and interconnections of the model in
the form of a diagram of cause and effect are set
out in Figure 2.
By share of dismantled personal accounts we
mean the correlation of the number of disman-
tled personal accounts (i.e., by those who have
rejected using the services of centralized heat-
ing) and the overall share of personal accounts
served by the heat supply enterprise.
By element of the model of ‘aggregated qual-
ity’ we mean the indicator reflecting the overall
level of quality of heat supply services, includ-
ing the quality of the boilers and networks, as
well as the quality of the accompanying ser-
vice. The numerical values of the indicator are
obtained by polling the users of the services.
The model we developed allows us to analyze
the effectiveness of one or another lever for pur-
poses of obtaining economic results expressed
as the change of the financial result or of the
absolutely liquid financial result of the activi-
ties of heat supply enterprises of the Donetsk
region.
The following are used as management levers
(managing the parameters) in the model:
the level of tariffs for enterprises;
the level of tariffs for the population;
the volume of investments in moderniza-
tion of the networks;
the volume of investments in moderniza-
tion of the boilers;
the volume of investments in improving the
service;
the volume of other costs.
Thus, the model allows us to raise the effec-
tiveness of the investment policy, the tariff pol-
icy and the policy in the sphere of managing
costs.
The block of evaluation of the effectiveness
incorporated in the model is intended for cal-
culating the effectiveness of management deci-
sions expressed as the incremental growth of
both the financial result and the absolutely liq-
uid financial result.
As a constant we used the discount rate and
the volume of state investments. All other indi-
cators of the model are calculated and obtained
by applying a modification of the inductive
method of self-organization of the models of
complex systems, the method of lowest quad-
rates, spline-interpolation, etc.
3.3. Results
of simulation modeling
On the basis of the complex model of stra-
tegic management of heat supply enterprises
described above, using scenario analysis, we
carried out a series of experiments allowing us
to determine the most effective management
levers. The results of the numerical experi-
ments are shown in Figure 3.
For evaluation of the effectiveness of apply-
ing various management levers we carried out
the following experiments:
Scenario 1: Increasing the level of tariffs for
enterprises by 10%;
Scenario 2: Increasing the level of tariffs for
the population by 10%;
Scenario 3: Increasing the volume of invest-
ments for modernization of the networks by
10%;
Scenario 4: Increasing the volume of invest-
ments for modernization of the boilers by 10%;
Scenario 5: Increasing the volume of invest-
ments for improving the service by 10%.
The results of the numerical experiments are
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Fig. 2. The complex model of strategic management of a heat supply enterprise in the form of a causality diagram
MODELING OF SOCIAL AND ECONOMIC SYSTEMS
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e
Net f
inan
cial
resu
lt
Acco
unts
rece
ivab
le (e
ntiti
es
finan
ced
from
loca
l bud
gets
)Ac
coun
ts re
ceiv
able
(b
udge
t ent
erpr
ises
) Ac
coun
ts re
ceiv
able
(o
ther
con
sum
ers)
Acco
unts
rece
ivab
le
(pop
ulat
ion)
Lost
dem
and
Shar
e of
dis
man
tled
pers
onal
acc
ount
s
Qual
ity o
f ac
com
pany
ing
serv
ice
Unpa
id d
eman
d
Aver
age
sala
ry
Shar
e of
em
erge
ncy
com
mun
icat
ions
Shar
e of
em
erge
ncy
boile
rs
Othe
r cos
ts
Inve
stm
ents
Tarif
f to
aver
age
sala
ry ra
tio
Tarif
f for
en
terp
rises
Tarif
f for
po
pula
tion
Tota
l tar
iff s
ettin
g co
sts
Aggr
egat
ed q
ualit
y
Wei
ghte
d av
erag
e ta
riff
Taxe
s an
d fe
es
Tota
l cos
ts
Qual
ity o
f boi
lers
and
co
mm
unic
atio
ns
Liqu
id e
ffect
Disc
ount
rate
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
55
compared with the forecast values of the opera-
tions of the system (Scenario 0).
Due to the fact that reducing other costs in
the long-term perspective is a precondition for
lowering the quality of services, the given man-
agement lever cannot be viewed as an effective
instrument of strategic management.
The economic effect expressed in the change
of financial result obtained taking into account
implementation of various scenarios is shown
in Table 1. We note that the first step of mod-
eling corresponds to the first quarter. Insofar
as the model reflects both retrospective data
which cannot be changed by implementation
of the scenarios (modeling steps 1–19) and
the forecast values of indicators assuming the
implementation of one of the scenarios (steps
20–30), the evaluation of the economic effect
is seen as worthwhile beginning only from the
20-th period of modeling.
The graphic illustration of calculated val-
ues of the economic effect obtained expressed
in the change of financial result following the
implementation of the indicated scenarios is
shown in Figure 4.
As we see in Figure 4, implementation of sce-
nario 5 is the most justified, i.e., increasing the
volume of investments in improving the service.
Conclusion
Thus, we propose an approach to increasing
the effectiveness of strategic management of
heat supply enterprises of the Donetsk region
based on the development of a respective
toolkit of mathematical modeling. Use of the
proposed approach assumes step-by-step solu-
tion of a number of tasks, namely:
analysis of the viability of the object of stra-
tegic management with a view to revealing the
most significant problems which bear on the
ability of the system independently to main-
tain its autonomous existence;
analysis of the market elements as the most
significant part of the external environment
where the enterprise operates;
development of approaches to raising the
effectiveness of forecasting the behavior of sub-
jects of the market as the methodological basis
of the system of information management serv-
ing the strategic management of the enterprise;
Fina
ncia
l res
ult,
‘000
rub.
Fina
ncia
l res
ult,
‘000
rub.
Fina
ncia
l res
ult,
‘000
rub.
Fina
ncia
l res
ult,
‘000
rub.
Fina
ncia
l res
ult,
‘000
rub.
Fina
ncia
l res
ult,
‘000
rub.
Fig. 3. The results of numerical experiments with the complex model of strategic management of heat supply enterprises using PowerSim software
Time, quarters
Time, quarters
Time, quarters
Time, quarters
Time, quarters
Time, quarters
MODELING OF SOCIAL AND ECONOMIC SYSTEMS
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
56
building a model basis to support decision
making, taking into account the main correla-
tions we discovered and allowing us to perform a
scenario analysis of the effectiveness of manage-
ment levers;
carrying out numerical experiments with
the model, as a result of which we established
that the most effective management lever is
raising the volume of investments directed into
improvements to the accompanying service.
Use of the given lever allows us to receive an
economic effect of 17,071,830.7 rubles in the
first quarter. Moreover, we forecast a growth of
the economic effect given systematic applica-
tion of this management lever.
As regards the direction of further research,
we can mention adaptation of the results
obtained to a wide range of objects, as well as
the programmatic realization of a system for
supporting the decisions taken.
Table 1.Calculation of the economic effect expressed in change
of the financial result obtained as a result of implementing the scenarios, ‘000 rub.
Step of modeling, quarter Scenario 1 Scenario 2 Scenario 3 Scenario 4 Scenario 5
20 15,022,278.2 –37,039,787.0 66,88,398.2 6,773,671.3 17,071,830.7
21 31,365,227.5 –24,636,261.0 6,992,516.1 7,121,654.8 17,390,125.1
22 49,158,587.6 –11,162,374.0 7,389,110.1 7,498,771.3 17,941,160.3
23 68,523,705.6 3,457,305.0 7,888,227.4 7,913,766.8 19,433,125.6
24 –30,659,758.0 –46,881,056.0 8,304,901.1 8,383,596.3 21,051,172.1
25 –14,493,133.0 –33,234,769.0 8,882,913.3 8,879,167.0 20,523,925.8
26 3,032,885.7 –18,447,471.0 9,323,873.7 9,432,300.9 21,828,511.6
27 22,052,699.9 –2,420,088.9 9,928,832.2 10,024,012.6 23,745,262.4
28 –40,777,450.0 –61,262,349.0 10,609,403.7 10,687,335.9 26,426,168.4
29 –22,017,860.0 –45,712,459.0 11,245,107.8 11,397,205.4 28,554,870.3
30 –1,696,379.3 –28,885,661.0 12,114,398.9 12,162,980.9 30,962,618.7
Fig. 4. The economic effect expressed in the change of financial result obtained as a result of implementing the scenarios
of the complex model of strategic management of heat supply enterprises, ‘000 rubles
MODELING OF SOCIAL AND ECONOMIC SYSTEMS
Scenario 1 Scenario 2 Scenario 3Scenario 4 Scenario5
Time, quarters
Åconomic effect, ‘000 rubles
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
57
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About the authors
Mariya A. Myznikova
Cand. Sci. (Econ.);
Senior Lecturer, Department of Economic Cybernetics, Donetsk National University,
24, Universitetskaya Street, Donetsk 283001, Ukraine;
E-mail: [email protected]
Larisa N. Brazhnikova
Dr. Sci. (Econ.), Professor;
Academician, Academy of Economic Sciences of Ukraine,
2, Zhelyabova Street, Kiev 03057, Ukraine;
E-mail: [email protected]
MODELING OF SOCIAL AND ECONOMIC SYSTEMS
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Research into the dynamics of railway track capacities in a model for organizing cargo transportation between two node stations
Nerses K. KhachatryanE-mail: [email protected]
Gayane L. BeklaryanE-mail: [email protected]
Svetlana V. BorisovaE-mail: [email protected]
Fedor A. BelousovE-mail: [email protected]
Central Economics and Mathematics Institute, Russian Academy of Sciences Address: 47, Nakhimovsky Prospect, Moscow 117418, Russia
Abstract
The article deals with a model for organizing railway transportation on a long stretch of road between two node stations connected by a large number of intermediate stations. Between two arbitrary neighboring stations, there is a railway track for temporary storage of cargo. The movement of cargo is carried out in one direction. To ensure the smooth movement of cargo, two technologies are used which are common for all stations. The fi rst technology is based on the procedure of interaction of a station with both neighboring stations and adjacent railway tracks. The second technology uses the technical capabilities of the station itself and is based on the interaction of the station with neighboring railway tracks. For cargo transportation, a simple control system is used which provides for measuring the volume of transported goods at neighboring stations with a single time lag.
This work is devoted to describing and studying the dynamics of the number of roads involved in the railway tracks. For this purpose, a system of diff erential equations is formed, the right parts of which are functions of variables describing the dynamics of the number of roads involved in the stations. The starting point for this study is previously obtained results from studying the dynamics of the number of tracks involved in the stations (a brief description of these results is given in the Introduction). What follows is the description of the dynamics of the number of roads involved in the railway tracks.
MODELING OF SOCIAL AND ECONOMIC SYSTEMS
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60
i – 1 i + 1i
Introduction
Transport is one of the main branches of any
state and performs the connecting, commu-
nication and supply functions. For the cor-
rect organization of traffic in the transport
network, control systems are used. Their algo-
rithms are based on mathematical models, one
of the main functions of which is the modeling
of traffic flows. A large number of publications
are devoted to mathematical modeling of traf-
fic flows. Works [1–3] describe “analog mod-
els” in which the movement of the vehicle is
similar to any physical flow (hydro- and gas-
dynamic models). There are a large number of
models designed to optimize the functioning of
transport networks [4–7]. This class of models
solves the problems of optimization of trans-
portation routes, development of optimal con-
figuration of the transport network, etc. One
of the approaches to modeling and research
of traffic flows is based on the theory of com-
petitive non-coalition equilibrium [8–11]. It
allows us to describe a fairly adequate mecha-
nism for the functioning of road networks. We
also note the approach associated with the use
of simulation and cellular automata described
in [12–15]. Recently, an alternative theory of
transport flows has been actively developed,
called the theory of three phases (classical the-
ories consider two phases: free flow and dense
flow) [16–20]. This theory can predict and
explain the empirical properties of the transi-
tion to dense flow and the resulting space-time
structures in the transport flow.
A number of publications are devoted to the
modeling of rail traffic and related transport
flows [21–27]. In particular, in works [24–27]
a model of organization of rail freight between
two node stations connected by a railway line
which contains a certain number of interme-
diate stations is investigated. It is assumed that
between stationary stations there is interex-
change railway track, where part of the cargo
can be temporarily stored (in a special storage
area). The movement of goods is carried out
in one direction. The traffic flow diagram is
shown in Figure 1.
Fig. 1. Scheme of freight traffic of railway transport
Possible variants of the dynamics (growth of the number of the roads involved on one railway tracks and falling on others) and their dependence on parameters of the model are investigated. We also study the dependence of the rate of change in the number of involved roads on the railway tracks on the model parameters. We then fi nd the parameter of control by which it is possible to provide arbitrarily small speed of growth (fall) of the number of the roads involved on all railway tracks.
Key words: station; railway track; organization of cargo transportation; mathematical model; differential
equations; dynamics; numerical realization.
Citation: Khachatryan N.K., Beklaryan G.L., Borisova S.V., Belousov F.A. (2019) Research into the
dynamics of railway track capacities in a model for organizing cargo transportation between two node
stations. Business Informatics, vol. 13, no 1, pp. 59–70.
DOI: 10.17323/1998-0663.2019.1.59.70
MODELING OF SOCIAL AND ECONOMIC SYSTEMS
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
61
In this figure, the circles indicate the stations,
and the squares indicate the railway tracks. As
can be seen from the figure, cargo can arrive
at an arbitrary intermediate station both from
the previous station and from the railway
track, after which the cargo can be sent either
to the next station or to the railway track. Let
the number of intermediate stations be equal
to m. Denoting by 0 and m + 1 respectively the
numbers of the initial and final nodal stations,
we obtain the following set of station numbers:
{0, 1, …, m, m + 1}. Each station at any time
is characterized by the number of involved
roads. Denote by zi (t), i = 0, 1, ..., m + 1 num-
ber of roads involved in the i-th station at time
t. The maximum number of involved roads at
the stations, at which the mode of increasing
the number of roads at the expense of goods
from the railway track, is functioning, we
denote by . If the number of paths involved
exceeds the maximum value, then part of the
cargo is temporarily sent to the storage area.
The organization of cargo traffic is carried
out using two technologies.
The first technology is based on the inter-
action procedure of neighboring stations.
The following rule applies here: an arbitrary
station can send cargo to the next station if
the number of involved roads is greater than
at the next station. In this case, the inten-
sity of shipment is proportional to the dif-
ference in the number of involved roads at
these stations. Note that sending goods from
an arbitrary station (except the last) with
a certain intensity is equivalent to receiv-
ing goods with the same intensity at the next
station. Thus, each station with a number i
(1 i m) can take the cargo from the previous
station with an intensity equal to (zi – 1
– zi ),
if zi – 1
> zi and send the cargo to the next sta-
tion with an intensity equal to (zi – z
i + 1),
if zi > z
i + 1. If the first condition is violated,
the station with number i sends the cargo to
the railway track with intensity (zi – z
i – 1),
and if the second one is violated, it receives
the cargo from the railway track with intensity (z
i + 1 – z
i ). Initial node station (i = 0) takes
a cargo with intensity 1(t ) and sends it to the
next station with intensity (z 0 – z
1 ) if z
0 > z
1.
Otherwise, the initial node station additionally
takes the cargo with the intensity (z 1 – z
0 ).
Final node station (i = m + 1) accepts the
load from the previous station with intensity (z
m – z
m + 1), if z
m > z
m + 1 , and distributes it with
intensity 2(t ). If z
m < z
m + 1, then the final sta-
tion additionally distributes the cargo with
intensity (zm + 1
– zm).
The second technology is designed to use the
infrastructure capabilities of the stations and
to ensure uninterrupted movement of cargo. It
is based on the procedure of interaction of the
station with neighboring railway track located
on opposite sides of it. The second technology
for all stations, except the initial one, allows
us to increase the number of involved roads
(if it does not exceed ), and to reduce it (if it
exceeds ). The function (.), setting the speed
of change of number of involved roads within
this technology has the following properties:
on a half-line (– , 0] it is identically equal to
zero, on an interval (0, xopt
) is increasing, in
a point xopt
accepts the maximum value, on a
half-line (xopt
, + ) is decreasing, in a point
accepts zero value, and on a half-line ( , + )
is linear. For the initial node station (i = 0) the
second technology is used only for unloading.
The function 0(.), setting the speed of change
of the number of involved roads at this station
within this technology, has the following prop-
erties: on a half-line (– , ] it is identically
equal to zero, and on a half-line ( , + ) it is
linearly decreasing.
For cargo transportation, a simple control
system is used: the quantity of involved roads at
any station has to coincide with the quantity of
involved roads at the following station, with a
time log which is uniform for all stations.
Thus, the dynamics of numbers of the
involved roads at stations is set by the system of
the differential equations
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BUSINESS INFORMATICS Vol. 13 No 1 – 2019
62
(1)
(2)
(3)
and a control system – by nonlocal linear
restrictions
(4)
The constant will be called a characteristic
of the control system.
Definition 1. The family of absolutely con-
tinuous functions , defined on [0 , + ),
is called the solution of the traveling wave
type with characteristic (soliton solution), if
almost all functions zi (.) satisfy the
system (1) – (3) and nonlocal restrictions (4).
The class of soliton solutions is extremely
narrow. This leads to the need to properly
extend the class of soliton solutions to the class
of soliton quasi-solutions. In [24–27] two ways
of such expansion are proposed. One type of
expansion involves the assumption of discon-
tinuous soliton solutions (we call them soliton
quasi-solutions of the first type).
Definition 2. The family of absolutely contin-
uous functions , defined on [0 , + ); it
is called a soliton quasi-solution of the first type
with characteristic , if almost all t [0 , + )
functions zi (.) satisfy the system (1) – (3) and
nonlocal restrictions (4), with possible discon-
tinuities at the points
It is proved , that for any {0,
1, ... m, m + 1} system (1) – (4) with a fixed ini-
tial value at the initial time has a single
“quasi-solution” of the first type [24].
Definition 3. A soliton quasi-solution of
the first type with a characteristic is called
-quasi-solution of the first type with charac-
teristic if inequalities
,
are satisfied, for all k = 1, 2, ... .
It is proved that for any there
is -soliton quasi-solution of the first type with
a characteristic with however small > 0 [24].
The second type of expansion of soliton solu-
tions allows weakening of the control system
(implementation of nonlocal restrictions (4)
with some error). We give an exact formulation
of quasi-solutions of this type.
Definition 4. The family of absolutely con-
tinuous functions , defined on [0 , + ),
is called -soliton quasi-solution of the second
type with characteristic , if almost all
functions zi (.) satisfy the system (1) – (3) and
the condition
is satisfied.
It is proved that the solutions of the system
of differential equations (1) – (3) are limited
under the limitation of functions 1(. ) end
2(. ) [27].
In work [27] by means of computer realiza-
tion quasi-solutions of the second type were
investigated for periodic functions
1(t ) =
2(t ) = d + cos(ωt), d γ > 0,
and also functions (.) и 0(.), defined as fol-
lows:
For this purpose, the set of all solutions of
the system of differential equations (1) – (3)
was investigated. According to the results of
numerical experiments, starting from a certain
point in time > 0 the solutions of the system
(1) – (3) begin to oscillate in some neighbor-
MODELING OF SOCIAL AND ECONOMIC SYSTEMS
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63
hood of the value , and the components of the
solution satisfy the condition z 0 (t ) > z
1 (t ) > ...>
z m
(t ) > z
m + 1(t ) for any t [
, + ).
Moreover, there is an integer 0 < m + 1
z i (t ) > if 0 i , t [
, + ), (5)
z i (t ) < if i m + 1, t [
, + ). (6)
Numerical experiments showed that the
value depends on parameters c0, c и a, but
does not depend on parameter . Dependence
on parameters c0 and c is non-increasing: with
increasing parameter c0 value decreases to
= 0, and with increasing parameter c to
= 1. Dependence on parameters a is non-
decreasing: with its increase increases to
= m.
The dependence of the solutions of the sys-
tem of differential equations (1) – (3) on the
parameter is studied. It is shown that for an
arbitrary characteristic > 0, increasing the
parameter , it is possible to make an arbitrar-
ily small error in the performance of nonlocal
restrictions (4).
In the research carried out, it was supposed
that capacities of railway tracks (number of
the involved roads on them) are unlimited as
a result of which observation of their dynamics
was not made. However, in fact this assump-
tion is unrealistic: at least, during a long period
of time capacities of railway track have to be
limited reasonably. This work is devoted to
research into the dynamics of capacities of
railway tracks and its dependence on model
parameters.
1. Description of the dynamics of the railway tracks’ capacities
We investigated the dynamics of capacities
of railway tracks within the model described in
the Introduction. Let’s begin with their num-
bering. The railway track located between sta-
tions with numbers i and i + 1 we will designate
number i. Thus, we get the following set of rail-
way tracks numbers: {0, 1, ..., m}. The num-
ber of the involved ways on i-th railway track at
the moment of time t we will designate through
yi (t). Determine with what intensity cargo
come on the railway tracks and with what
intensity leave them. Note that the cargo can
be delivered to the railway tracks and sent from
them in the framework of both the first and the
second technology.
Within the first technology, on a stage with
number i (1 i m – 1) cargo arrives from the
station with number i with intensity (zi – z
i – 1),
if zi > z
i – 1 , and goes to the station with num-
ber i + 1 with intensity (zi + 2
– zi + 1
), if
zi + 2
> zi + 1
. On an initial railway track (i = 0)
within the first technology cargo does not
arrive. At last, on a final railway track (i = m)
within the same technology cargo arrives from
the station with number i = m with intensity
(zm – z
m – 1), if z
m > z
m – 1. The cargo is not sent
from the final railway track within this tech-
nology.
Within the second technology, on a railway
track with number i (1 i m – 1) cargo arrives
from the station with number i with inten-
sity – φ(zi
), if the number of the involved ways
at the station with number i exceeds value ,
and goes to the station with number i + 1 with
intensity φ(zi + 1
) , if the number of the involved
ways at the station with number i + 1 is less than
value (the station with number i + 1 accepts
cargo from a railway track). On an initial rail-
way track (i = 0) within the second technology
cargo arrives from the initial node station with
intensity – φ0(z
0), if the number of the involved
roads at the specified station exceeds , and
goes to the station with number i = 1 with inten-
sity φ(z1), if the number of the involved roads at
the station with number i = 1 is less . At last,
on a final railway track i = m within the second
technology cargo arrives with intensity – φ(zm)
from the station with number i = m and goes to
the final node station (i = m + 1) with intensity
φ(zm + 1
), if the number of the involved roads at
the final node station is less .
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64
Thus, the dynamics of the number of the
involved roads on a railway track is described
by the following system of differential equa-
tions:
(7)
(8)
(9)
where
We investigated a system (7) – (9) on the
assumption that components of quasi-solu-
tions of the second type participate in the right
parts of the equations.
Using inequalities (5) – (6) and definitions
of functions (.) and 0(.), we will transform
the equations. In particular, from inequalities
(5) – (6) it follows that, since the moment all
composed a look (zk + 1
– zk ) sign(z
k + 1 – z
k ) in
the right part of the equations (7) – (9) will be
equal to zero. Depending on value we will
consider several cases.
The first case: = 0. It means that z 0 (t ) > ,
z i (t ) < , i = 1, ..., m + 1 for all t , and the
equation (7) – (9) take a form:
(10)
(11)
The second case: 1 < < m
(12)
(13)
(14)
(15)
The third case:
(16)
(17)
(18)
Directly from (10) – (18) it follows that in
all three cases the right parts of all equations
(except for, perhaps, equation with number )
either are positive, or are negative. Numeri-
cal experiments showed that in the first a case
( = 0) the right member of equation with
number = 0 is positive. It is connected with
the fact that this case takes place if the value
of parameter c0 is significantly more than the
value of parameter a. In the third case )
the right member of equation with number is
negative. It is connected with the fact that this
case takes place if the value of parameter c is
significantly less than value of parameter a.
Thus, in the first case the right parts of equa-
tion with number = 0 are positive, and
the right parts of other equations are nega-
tive. Therefore, in this case the number of the
involved roads on a railway track with number
= 0 will increase indefinitely, and the num-
ber of the involved roads on other railway track
will decrease indefinitely.
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In the third case, the right part of the equa-
tion with the number is negative, and
the right parts of the remaining equations are
positive. Accordingly, in this case, the number
of involved roads on the railway track with the
number will decrease indefinitely, and
the number of involved roads on the remaining
railway tracks will increase indefinitely.
In the second case, the right parts of the
equations with numbers less than are posi-
tive, the right parts of the equations with num-
bers more than are negative. The right part
of the equation with the number can be both
positive and negative, and with certain com-
binations of parameters can be equal to zero.
Therefore, in this case only on one railway
track the number of the involved roads can-
not change over time. The number of involved
roads on the remaining railway tracks will either
increase indefinitely or decrease indefinitely.
For example, Figure 2 shows the dynamics of
the number of involved roads in the railway
tracks in case of constant functions describing
the intensity of the supply of cargo to the ini-
tial node station and the intensity of the distri-
bution of cargo from the final node station, i.e.
1(t ) =
2(t ) = d, d > 0 (case 2, equations (12) –
(15)). The number of stations is equal to 10,
respectively, the number of railway tracks is 9
(y0, y
1, ..., y
8 – the number of involved roads on
these railway tracks). The value that deter-
mines the capacity of the stations is equal to 10,
and the parameters have the following values:
= 10, a = 0.1, c0 = c = 1, d = 3. Numerous
experiments have shown that all conclusions
regarding the dynamics of the capacity of the
railway tracks, which will be given below, are
valid for any other number of stations (railway
tracks) and values .
For periodic functions 1(t ) =
2(t ) = d +
+ cos( ), d > 0 the dynamics of the num-
ber of involved roads on the railway tracks does
not change fundamentally. For example, Figure
3 shows the dynamics for the following parame-
ter values: = 10, a = 0.1, c0 = c = 1, d = 3, = 3.
50
40
30
20
10
0
-10
-20
-30
-40
-50
y1
y0
y2
y3
y4
y5
y6
y7
y8
Fig. 2. Dynamics of the number of involved roads on the railway tracks with constant functions and
50
40
30
20
10
0
-10
-20
-30
-40
-50
Fig. 3. Dynamics of the number of involved roads on the railway tracks with periodic functions and
y1
y0
y2
y3
y4
y5
y6
y7
y8
In this regard, further research will be carried
out for the case of constant and equal functions
1(t ) and
2(t ).
2. Dependence of growth rate and falling number of involved roads
on the railway tracks of the model parameters
We investigated the dependence of the growth
rate and the fall of the number of involved roads
on the railway tracks on the model parameters.
Let’s start with the parameter с0. Recall that
this parameter determines the intensity of the
shipment of cargo from the initial node station
yi , i = 0, 1, ..., 8
yi , i = 0, 1, ..., 8
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66
at the zero railway track. Let (equations
(12) – (18)). As shown by numerical experi-
ments, an increase in this parameter leads to
an increase in the rate of growth of the num-
ber of involved roads on the zero railway track,
a decrease in the rate of growth of the num-
ber of involved roads on the railway tracks
with numbers 1, ..., , and an increase in the
rate of fall on the following railway tracks. At
the same time, both the decrease in the rate
of growth of the number of involved roads on
the railway tracks with numbers 1, ..., and
the increase in the rate of fall on the follow-
ing railway tracks weaken with the increase
in the number of railway tracks. This trend
can be seen in Figure 4, where the parameter
value с0 is increased to two, with unchanged
values of other parameters ( = 10, a = 0.1, c0 = 2, c = 1, d = 3).
50
40
30
20
10
0
-10
-20
-30
-40
-50
50
40
30
20
10
0
-10
-20
-30
-40
-50
Let us proceed to the study of the dependence
of the growth (fall) of the number of involved
roads on the railway tracks on the parameter c.
This parameter determines the intensity of the
shipment of cargo from any intermediate sta-
tion with the number i = 1, ..., m on the rail-
way tracks. Sending cargo to the railway track
is carried out if the number of involved roads
at the station is greater than the value , that
determines the capacity of the station. Accord-
ing to (5), this condition is satisfied at stations
with numbers i = 0, ..., . Thus, the station
with number i = 0, ..., sends cargo to the
railway track with number i = 0, ..., . Let’s
remember that the value depends on param-
eter c: with its increase the value decreases to
= 1. Therefore, a small increase in parame-
ter c which is not leading to reduction of value
leads to an increase in the growth rate of the
number of involved roads on railway tracks with
numbers i = 1, ..., and to reduction of growth
of the number of the involved roads on the rail-
way track with number i = 0. On railway tracks
with numbers i = + 1, ..., m an increase in
speed of fall of the number of involved roads
is observed. At the same time as an increase in
the growth rate of the number of involved roads
on railway tracks with numbers i = 1, ..., ,
and increase in speed of fall of the number of
y1
y0
y2
y3
y4
y5
y6
y7
y8
y1
y0
y2
y3
y4
y5
y6
y7
y8
Fig. 4. Dynamics of the number of the involved roads on railway tracks at increase of value of parameter (double increase)
Fig. 5. Dynamics of the number of involved roads on railway tracks with increase of value
of parameter (multiple increase)
Let’s remember that the value depends
on parameter с0: at its increase the value
decreases to = 0. Therefore, in the process
of increases to this parameter, growth of the
number of involved roads on all stages railway
tracks, except for initial, is replaced with fall-
ing numbers. This trend can be seen in Figure 5
(the equations (10) – (11)). In it the value of
parameter с0 is increased up to 60 at invaria-
ble values of other parameters ( = 10, a = 0.1, c0 = 60, c = 1, d = 3).
yi , i = 0, 1, ..., 8
yi , i = 0, 1, ..., 8
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67
involved roads on railway tracks with numbers
i = + 1, ..., m weakens at increase in number
of a railway track.
If the increase in parameter c leads to the
reduction of value , then the following trend is
observed: in the process of an increase in param-
eter c gradually on railway tracks on which there
was an increase in the growth rate of the number
of involved roads there is a reduction of growth
rate of the number of involved roads up to fur-
ther falls, except for railway track to numbers
i = 0.1 (Figure 6). Thus, since some value of param-
eter c, on all railway tracks except zero and the
first, there is a decrease in number of the involved
roads. On zero and first railway tracks there is a
growth of number of the involved roads, and the
growth rate on the first increases (Figure 7).
In Figure 6, the value of the parameter c is
increased to two ( = 10, a = 0.1, c0 = 1, c = 2,
d = 3), and in Figure 7 – to 60, with unchanged
values of other parameters ( = 10, a = 0.1, c0 = 1, c = 60, d = 3).
Let’s pass to a research of dependence of
growth (fall) of the number of involved roads on
railway tracks from parameter a. Let’s remem-
ber that this parameter determines intensity
of receipt of cargo by the second technology
(from a railway track), and this technology is
applied if the number of involved roads at the
station are less than value . According to (6),
this condition is satisfied at stations with num-
bers i = + 1, ..., m + 1. As was stated above,
within the second technology the station with
number i + 1 accepts cargo from a railway track
with number i. Let’s remember that the value
depends on parameter a: with its increase the
value increases to . Therefore, a small
increase in parameter a, which is not lead-
ing to increase in value , leads to an increase
in speed of fall on railway tracks with numbers
i = , ..., m. On railway tracks with numbers
i = 0, ..., – 1 an increase in the growth rate of
the number of involved roads is observed. At the
same time as increase in speed of fall on railway
tracks with numbers i = , ..., m, and increase
in growth rate of number of involved roads on
the previous railway tracks weakens with the
reduction of the number of railway track. If the
increase in parameter a leads to an increase in
value , then the following trend is observed: in
the process of increases in parameter a gradually
on railway tracks on which there was an increase
in the speed of fall in the number of involved
roads there is a reduction of speed of fall in the
number of involved roads up to further growth,
except for the last railway track to number
i = m (Figure 8). Thus, since some value of
parameter a, on all railway tracks except the
last, there is a growth of number of the involved
roads. On the last railway track, there is a falling Fig. 6. Dynamics of the number of involved roads on railway tracks at increases of value of parameter (double increase)
Fig. 7. Dynamics of the number of involved roads on railway tracks at increases of value of parameter (multiple increase)
50
40
30
20
10
0
-10
-20
-30
-40
-50
50
40
30
20
10
0
-10
-20
-30
-40
-50
y1
y0
y2
y3
y4
y5
y6
y7
y8
y1
y0
y2
y3
y4
y5
y6
y7
y8
yi , i = 0, 1, ..., 8
yi , i = 0, 1, ..., 8
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number of involved roads, and the speed of fall
increases (Figure 9).
In Figure 8, the value of parameter is increased
to 0.5 ( = 10, a = 0.5, c0 = c = 1, d = 3), and in
Figure 9 – to 10, with unchanged values of other
parameters ( = 10, a = 10, c0 = 1, c = 60, d = 3).
At last, we investigated the dependence of
growth (fall) of volume of railway tracks on
parameter . Let’s remember that change
of this parameter does not change value .
According to the research conducted in [27]
since timepoint , an increase in parameter
leads to a reduction as differences (zi – ),
i = 0, ..., and ( – zi ), i = + 1, ..., m. Thus,
an increase in parameter leads to reduction of
the growth rate on railway tracks with numbers
i = 0, ..., – 1, and to reduction of speed of
fall in the number of involved roads on railway
tracks with numbers i = + 1, ..., m. The same
impact is made by an increase in this parame-
ter and number of involved roads on a railway
track with number i = , only with the differ-
ence that the number of involved roads on this
railway tracks can both grow, and fall, or not
change. For example, the dynamics at the fol-
lowing values of parameters ( = 30, a = 0.1, c0 = c = 1, d = 3) is given in Figure 10.
Thus, an increase in parameter can reduce
both growth, and fall of the number of involved
roads on all railway tracks.
Fig. 8. Dynamics of the number of involved roads on railway tracks with increases of value of parameter (fivefold increase)
Fig. 9. Dynamics of the number of involved roads on railway tracks with increases of value of parameter (multiple increase)
Conclusion
This article is devoted to research into the
dynamics of capacities of railway tracks in a
model for the organization of cargo transporta-
tion between two node stations. Earlier in works
[24–27] the dynamics of capacities of stations
was investigated (number of involved roads on
them). In the research carried out, it was sup-
posed that capacities of railway tracks are unlim-
ited and for that reason observation of their
dynamics was not made. In this work, the system
of differential equations describing the number
of involved roads on railway tracks is presented
and investigated. As it appeared, from some
Fig. 10. Dynamics of the number of involved roads on railway tracks with increase of value of parameter
50
40
30
20
10
0
-10
-20
-30
-40
-50
50
40
30
20
10
0
-10
-20
-30
-40
-50
50
40
30
20
10
0
-10
-20
-30
-40
-50
y1
y0
y2
y3
y4
y5
y6
y7
y8
y1
y0
y2
y3
y4
y5
y6
y7
y8
y1
y0
y2
y3
y4
y5
y6
y7
y8
yi , i = 0, 1, ..., 8 y
i , i = 0, 1, ..., 8
yi , i = 0, 1, ..., 8
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time point, the number of involved roads on
all railway tracks, except one, either increases,
or decreases. At the same time, the quantity of
railway tracks both with increasing, and with
decreasing number of involved roads depends
on a number of parameters of the model. The
dependence of the growth rate and fall of num-
ber of the involved roads on model parameters is
investigated. We revealed the parameter, which
if increased makes it possible to achieve simul-
taneous reduction of both growth rate and speed
of fall in the number of involved roads on all
railway tracks. This parameter characterizes the
intensity of interaction of the neighboring sta-
tions within the first technology of the organiza-
tion of freight traffic.
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About the authors
Nerses K. Khachatryan
Cand. Sci. (Phys.-Math.);
Leading Researcher, Laboratory of Dynamic Models of Economy and Optimization,
Central Economics and Mathematics Institute, Russian Academy of Sciences,
47, Nakhimovsky Prospect, Moscow 117418, Russia;
E-mail: [email protected]
Gayane L. Beklaryan
Cand. Sci. (Econ.);
Senior Researcher, Laboratory of Computer Modeling of Social and Economic Processes;
Central Economics and Mathematics Institute, Russian Academy of Sciences,
47, Nakhimovsky Prospect, Moscow 117418, Russia;
E-mail: [email protected]
Svetlana V. Borisova
Cand. Sci. (Phys.-Math.);
Senior Researcher, Laboratory of Dynamic Models of Economy and Optimization,
Central Economics and Mathematics Institute, Russian Academy of Sciences,
47, Nakhimovsky Prospect, Moscow 117418, Russia;
E-mail: [email protected]
Fedor A. Belousov
Researcher, Laboratory of Dynamic Models of Economy and Optimization,
Central Economics and Mathematics Institute, Russian Academy of Sciences,
47, Nakhimovsky Prospect, Moscow 117418, Russia;
E-mail: [email protected]
MODELING OF SOCIAL AND ECONOMIC SYSTEMS
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INFORMATION SECURITY
Analysis and forecast of undesirable cloud services traffic
Marina V. TumbinskayaE-mail: [email protected]
Bulat I. BayanovE-mail: [email protected]
Ruslan Zh. RakhimovE-mail: [email protected]
Nikita V. KormiltcevE-mail: [email protected]
Alexander D. UvarovE-mail: [email protected]
Kazan National Research Technical University named after A.N. TupolevAddress: 10, Karl Marx Street, Kazan 420111 Russia
Abstract
These days one of the main problems that must be solved to ensure information security in cloud services for corporations as well as for individual clients is to correctly identify and predict hacking in the network traffi c. This paper presents statistics on information security threats, provides classifi cation of information security threats for cloud services, identifi es hackers’ goals, and proposes countermeasures.
A vital task is to develop an eff ective method that could be used to protect cloud services from various network threats, as well as to analyze the network traffi c. For these purposes, we chose a method based on an additive time series model, which allows us to predict the undesirable network traffi c. To test this method, we obtained quantitative parameters for the undesirable traffi c by simulating a network attack and collecting empirical data that describe this process. We used special software that simulates a network attack, and software that records and processes all the empirical data needed for the research.
Using the data obtained, we analyzed the effi ciency of the method based on the additive time series model. We demonstrated that this method is also applicable for research into the general dynamics of the number of network attacks in cyberspace. This method also allows us to reveal how the dynamics of the number of hacker network attacks depends on season, date, or time. The results show that, based on data describing the network traffi c, one can identify and predict the undesirable hacker threats.
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
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Introduction
Development of the infrastructure
of modern enterprises causes an
increased demand for cloud tech-
nologies, because they are convenient, reason-
ably priced, mobile, quick, and reliable. Cloud
technologies allow us to use cloud services.
A cloud service [1] is an Internet service that
makes it possible for its clients to outsource
the maintenance of some elements of IT infra-
structure [2].
According to the RightScale statistics, 95%
of organizations used one or another cloud ser-
vice model in 2017 [3]. According to Orange
Business Services experts, the market for
cloud services in Russia comes to about 24.6
Bn. roubles [4]. It was shown [2, 5] that mod-
ern IT companies are uncompetitive if they
fail to use cloud technologies, thus foregoing
profits. Cloud services have long been used in
large corporations (Google Disk, iCloud from
Apple, Cloud mail.ru).
Cloud services make it necessary to solve
information security problems, since new tech-
nologies lead to the emergence of a large num-
ber of threats and vulnerabilities in information
security systems. According to a Kaspersky
Laboratory poll [6], 13% of Russian organiza-
tions face issues related to cloud infrastructure
security at least once a year. Out of those com-
panies, 32% lost their data due to such inci-
dents. Therefore, it is crucial to ensure infor-
mation security in cloud services.
The proposed novel method for analyzing
and predicting the network traffic based on the
INFORMATION SECURITY
additive time series model and integrated into
security tools can ensure the necessary security
level for regular storage, thus protecting it from
various network attacks. This constitutes the
scientific novelty of the paper. Unfortunately,
many existing data security methods cannot
reliably predict undesirable network traffic.
1. Possibility of interpreting
the proposed method in WAF
As shown in [7], the majority of hacker
attacks are based on typical hacker methods,
which are brought to perfection. Therefore, we
need to develop methods that employ continu-
ous learning, and such methods should gradu-
ally replace the signature analysis. It was also
noted [7] that some developers of web appli-
cation firewalls (WAF) focus on renewing the
signatures rather than on the signature analy-
sis. To create a security model that ensures the
necessary security level, WAF needs an exten-
sive database of the undesirable traffic signa-
tures and actions that can be applied to all types
of web applications. The proposed method for
analyzing and predicting the network traffic
based on the additive time series model can
be integrated into complicated WAFs in the
future. Here the main goal will not be to pre-
dict the hacker’s and legitimate user’s actions,
but to create a security model based on the
URL as well as on the parameters and cookies.
After the security model is developed, it needs
to be tested, i.e., the traffic should be analyzed
to prevent a hacker’s exploiting both known
and unknown vulnerabilities.
Key words: forecasting; DDOS attack; cloud services; network traffic; modeling; additive time
series model; autocorrelation function; error estimation.
Citation: Tumbinskaya M.V., Bayanov B.I., Rakhimov R.Zh., Kormiltcev N.V., Uvarov A.D.
(2019) Analysis and forecast of undesirable cloud services traffic. Business Informatics, vol. 13,
no 1, pp. 71–81
DOI: 10.17323/1998-0663.2019.1.71.81
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
73
2. Classification of security threats
to cloud services
Let us consider the classification of security
threats to cloud services. Table 1 presents the
most common threats according to [6]. Pos-
sible hackers’ goals and security measures are
presented for each threat. No single method
alone can prevent all types of threats; there-
fore, it is impossible to block the threats com-
pletely. Statistical data for each threat that was
successful can be stored in the system and used
for future analysis and development of new
security systems.
3. Simulating a network attack
To analyze the network traffic coming into
the network nodes, information security spe-
cialists install ad hoc software at the network
nodes. In this research, Wireshark software
(v.2.6.1) was used. This software allows us to
capture and analyze the network traffic for the
most common network protocols (TCP, UDP,
HTTP, etc.).
INFORMATION SECURITY
Table 1. Types of security threats to cloud services,
hackers’ goals, and security measures
# Security threat to cloud services Hackers’ goal Security measures
1. Data theft Accessing a database (e.g., e-mail addresses of users)
Database decentralization and data encryption with an SSL certificate
2. Data loss Database modification or erasing information Data backup, restricted access
3. Account theft / hacked services
Database modification or erasing information Two-factor authentication
4. Unprotected nterfaces and API Complete access to the database Authentication, access control,
encryption
5. DDOS attacks Preventing authorized users from accessing the cloud service Access control
6. Undesirable insider Database access Access control
7. Cloud services used by hackers
Access to the cloud computing resources
Restriction of the system’s computing power
Papers [8, 9] contain the network traffic data
that describe DDOS attacks. However, there is
not enough data there for the purposes of this
research; therefore, we collected the necessary
data by simulating a network attack based on
the algorithms presented in [10]. We used two
nodes of a configured network. One of them
was used as the victim’s device, and the other
one as the hacker’s device. Virtual machines
installed on the same computer served as those
devices. Wireshark was installed on the victim’s
virtual machine, and LOIC (an open-source
code for DDOS attacks)1, which creates unde-
sirable traffic, was installed on the hacker’s vir-
tual machine.
In our research, we assumed that hackers
attack the network multiple times with vari-
ous initial configurations of the malware, and
we did not rule out the possibility that the vic-
tim could access the network as well. The net-
work stream (the number of network packets
per second) through the victim’s network node
is presented in Figure 1.
1 https://www.darknet.org.uk/2017/10/loic-download-low-orbit-ion-cannon-ddos-booter/
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
74
Number of packets, unit
Number of packets, unit
INFORMATION SECURITY
Fig. 1. Number of network packets passing through the victim’s node, per second
Fig. 2. Number of network packets from the hacker’s node, per second
1 15 29 43 57 71 85 99 113
127
141
155
169
183
197
211
225
239
252
267
281
295
309
323
337
351
365
379
393
5000
4000
3000
2000
1000
0
1 29 57 85 113
141
169
197
225
253
281
309
337
365
393
421
449
477
505
533
561
589
617
645
673
701
729
757
785
As we can see from Figure 1, we cannot dis-
tinguish the stream from a particular user from
the overall network stream; therefore it is rec-
ommended to consider network streams from
particular users.
For convenience of analysis, it is possible to
filter the network stream through the victim’s
node and separate the packets coming from the
hacker’s node. The network stream from the
hacker’s node is presented in Figure 2. It repre-
sents a physical process, the intensity of which
periodically increases by several orders of mag-
nitude and describes the actions of a particu-
lar user.
We can find out the address of the node of the
hacker attacking the network by analyzing the
parameters describing the incoming network
traffic, for example, the density of the distri-
bution of the number of packets by their size in
bits. Table 2 presents the data obtained by sim-
ulating network attacks and desirable network
traffic using Wireshark software.
The simulation results show that, when the
network traffic is undesirable, more than 92%
of the packets are 40–79 bits in size. At the
same time, when the traffic is desirable, the
percentage of the packets of this size is about
39%, while more than 42% of the packets have
the size between 1280–2559 bits, and about
11% are sized between 640–1279 bits. It is also
suspicious when the traffic is extremely inten-
sive (in terms of the number of packets per unit
of time) or it has other untypical parameters.
As a sample for analysis, we chose the number
of network packets coming from the hacker’s
node per second (Figure 2).
350030002500200015001000500
0
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
75
INFORMATION SECURITY
4. Predicting the network attacks
with time series analysis
For statistical analysis, we chose the method
based on time series analysis. According to the
Cisco annual report on cybersecurity [11], in
year 2018, 39% of organizations used auto-
mated tools to prevent hacker attacks, and the
rest of them used machine learning (artificial
intelligence) [12–15].
We solved the problem of predicting the net-
work attacks using a time series additive model.
This model assumes that each level of the time
series (F ) can be presented as a sum of three
components: a trend (T ), a seasonal compo-
nent (S ), and a random component (E ):
F = T + S + E. (1)
To determine the trend component, linear
regression was used:
у = a x + b, (2)
where y – the trend value;
x – the lag;
a and b – the regression coefficients.
In Equation (2), coefficients a and b are
determined from the previous values in the
original sample using the following equation:
(3)
a = y – b · x, (4)
where – the mean lag value;
– the mean value in the original sample.
Figure 3 presents the original data on the
number of network packets coming from the
hacker’s node alongside the trend line obtained
by Equation (2), where a = 0.973, b = 615.87.
The trend line goes up because of the increase
in the intensity of the network traffic.
Now we have to determine the seasonal
component, which is periodical and can be
obtained from the autocorrelation function
(ACF). Figure 4 presents a plot of the auto-
Table 2. The percentage of received packets by packet size for desirable traffic and during a network attack
# Packet size Percentage of packets received by packet size for desirable traffic
Percentage of packets received by packet size during
a network attack
1. 0–19 0.00% 0.00%
2. 20–39 0.00% 0.00%
3. 40–79 39.06% 92.79%
4. 80–159 3.81% 0.48%
5. 160–319 0.93% 3.30%
6. 320–639 1.35% 3.25%
7. 640–1279 11.05% 0.16%
8. 1280–2559 42.90% 0.02%
9. 2560–5119 0.82% 0.00%
10. 5120 and more 0.08% 0.00%
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
76
INFORMATION SECURITY
correlation function of. the lag number. The
dashed line corresponds to the white noise (the
boundary of the statistical significance of cor-
relation coefficients is the error of the autocor-
relation function). The autocorrelation func-
tion was calculated for a time interval of up to
30 lags.
Analysis of the autocorrelation function
shows that the original data is periodical. There
is a high correlation for lags 22 and 23. There-
fore, for the seasonal component in the addi-
tive model the period will be about 23 lags.
Thus, the length of one season is N = 23 (the
lag’s number can take values n = 1, 2, ..., N),
where one lag corresponds to one second.
The values of seasonal component Sn are deter-
mined as mean values of the differences between
current the value Fn and the trend component T
n
calculated for each lag number n:
(5)
where k – the season number;
K – the total number of seasons.
Then the total number of lags for the entire
time series is M = N K.
Using the values obtained for the trend com-
ponent (2) and the seasonal component (5), we
can calculate the predicted values for F using
Equation (1) (in this model, the random com-
ponent is not considered) [16]. Figure 5 presents
the plots for sample values Fn and predicted val-
ues F. The discrepancies between the plots for F
and Fn can be evaluated by calculating the mean
absolute percentage error (MAPE).
Value of ACF
Fig. 3. The number of network packets received from the hacker’s node per second, including the trend component
Fig. 4. Autocorrelation function with the account of the white noise
1 18 35 52 69 86 103
120
137
154
171
188
205
222
139
256
273
290
307
324
341
358
1,0000,8000,6000,4000,2000,000
-0,200-0,200-0,600
1 4 7 10 13 16 19 22 25 28
Time, sec.
Number of packets, unit
350030002500200015001000500
0
Experiment Trend
Time lag, units
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
77
INFORMATION SECURITY
This estimate cannot be used to calcu-
late the error of the prediction model used in
this research because the current test sample
includes values close to 1. Therefore, we used
the root-mean-square error (RMSE) instead;
it is equal to 353. This follows from the follow-
ing equation:
(6)
where N – the original sample size;
y – the predicted value,
– the current value.
The value obtained indicates that the pre-
diction model is less than optimal. To make
Fig. 5. Current values for the test sample and predicted values for the number of network packets per second
Fig. 6. The number of network packets coming from the hacker’s node per second, with the account of the trend component for two closest seasons from the original sample
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47Time, sec.
1600140012001000800600400200
0
our additive predicting model more accurate,
we used the trend and seasonal components
from earlier seasons (the most recent ones), as
shown in Table 3 and Figure 6.
Both the amplitude and the duration of these
seasons are close to the corresponding val-
ues for the following season, and this fact can
improve the quality of the prediction.
Figure 7 presents the plots for future actual
values for the test sample and the predicted
values, taking into account the corrections
to the components of the time series additive
model.
In this case, the estimated RMSE is 201,
which is a considerable improvement com-
pared to the earlier RSME of 353. This leads
Number of packets, unit
0 5 10 15 20 25
Forecast Experiment
Time, sec.
Number of packets, unit
2000
1500
1000
500
0
Experiment Trend
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
78
us to the conclusion that a predictive model of
network attacks is much more accurate when
it is based on recent experimental data rather
than on the entire sample.
To calculate the relative error for the pre-
diction model, we can calculate the ratio of
the RMSE estimate to the maximum value in
the test sample. Here we chose the maximum
instead of the mean value because the sam-
ple considered contains many values close to
1. This leads to a relatively small mean value,
which makes it impossible to estimate the rela-
tive error (MAPE) reliably. We found that the
ratio of the RMSE to the maximum value in
the test sample is 13%.
Therefore, the proposed prediction model
of undesirable network traffic has a reasonably
small relative error, and it can serve as an effi-
cient tool for the detection of network attacks.
If necessary, the proposed model for the pre-
Fig. 7. Actual values for the test sample and predicted values for the number of network packets per second, with corrections
16001400120010008006004002000
diction of DDOS attacks could be used to study
the general dynamics of the number of DDOS
attacks in cyberspace [17]. If we use the num-
ber of attempted DDOS attacks in each quar-
ter of years 2017 and 2018 as empirical train-
ing data, we can predict the number of DDOS
attacks in the first half of year 2019.
The analysis of the data presented in Fig-
ure 8 shows that there are two periods in the
dynamics of the number of DDOS attacks,
namely 60 and 7 days. Apparently, the activity
peaks (Feb 15, 2019; April 10, 2019, and June
5, 2019) of the envelope curve fall between rel-
atively long holidays (March, May, and June).
Short-scale periodic peaks are probably caused
by the activity during particular days of the
week. Therefore, a relatively simple prediction
model allows us to find a connection between
the periods in DDOS attacks and the calendar
features for 2019.
Fig. 8. Predicted numbers of DDOS attacks
01.01.2019 31.01.2019 02.03.2019 01.04.2019 01.05.2019 31.05.2019 30.06.2019
140012001000800600400200
0Time, days
Number of DDOS attacks, unit
0 5 10 15 20 25
Forecast Experiment
Time, sec.
Number of packets, unit
INFORMATION SECURITY
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
79
Table 3. Estimation of the trend and seasonal components
# Current values, number of packets per second
Trend component estimate, number of packets per second
Seasonal component estimate, number of packets per second
1. 65 661 –5962. 21 662 –6413. 9 663 –6544. 18 663 –6455. 1088 664 4246. 1398 665 7337. 1301 666 6358. 1363 667 6969. 1343 668 675
10. 1375 669 70611. 1283 670 61312. 1378 671 70713. 1387 672 71514. 1304 673 63115. 1276 674 60216. 1302 675 62717. 1295 676 61918. 1380 677 70319. 1391 678 71320. 1062 679 38321. 15 679 –66422. 23 680 –65723. 11 681 –67024. 10 682 –67225. 19 683 –66426. 24 684 –66027. 13 685 –67228. 36 686 –65029. 36 687 –65130. 1313 688 62531. 1342 689 65332. 1360 690 67033. 1439 691 74834. 1380 692 68835. 1290 693 59736. 1384 694 69037. 1329 695 63438. 1306 695 61139. 1315 696 61940. 1296 697 59941. 1309 698 61142. 1298 699 59943. 93 700 –60744. 37 701 –66445. 21 702 –68146. 9 703 –694
INFORMATION SECURITY
BUSINESS INFORMATICS Vol. 13 No 1 – 2019
80
INFORMATION SECURITY
Also, we should note that efficiency of the
proposed model is higher when DDOS attacks
have almost identical statistical parameters. If
each implementation of a DDOS attack differs
statistically, it is harder to detect and predict
the hacker’s actions.
Conclusion
This paper reports the results of network traf-
fic analysis aimed at predicting the threats in
cloud services. The statistics on information
security threats to data storage and transmis-
sion that we present here validate the need for
the development of new methods of data pro-
tection. Such methods typically use ad hoc
hardware and software to analyze the informa-
tion security threats. We implemented the mal-
ware that simulated network attacks, as well as
the software that captured and processed the
empirical data we needed for this study. We sim-
ulated a network attack (a DDOS attack) and
saved the necessary parameters to files conven-
ient for analysis and further processing. Out of
many prediction models, we chose the additive
time series model. The results obtained with
the help of this model show that if we know
the behavior of the statistical parameters of
different implementations of a DDOS attack,
we can detect and predict the hacker’s actions
for this type of attacks. The high efficiency of
the proposed model is proven by comparison
of the predicted values with the future actual
values. The model’s accuracy is characterized
by the RMS error, which is equal to 201. The
results of our research demonstrate that statis-
tical methods of network traffic analysis can be
employed in the tools used to protect the cloud
services from various network attacks.
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About the authors
Marina V. Tumbinskaya
Cand. Sci. (Tech.);
Associate Professor, Department of Information Protection Systems,
Kazan National Research Technical University named after A.N. Tupolev,
10, Karl Marx Street, Kazan 420111, Russia;
E-mail: [email protected]
Bulat I. Bayanov
Student, Kazan National Research Technical University named after A.N. Tupolev,
10, Karl Marx Street, Kazan 420111, Russia;
E-mail: [email protected]
Ruslan Zh. Rakhimov
Student, Kazan National Research Technical University named after A.N. Tupolev,
10, Karl Marx Street, Kazan 420111, Russia;
E-mail: [email protected]
Nikita V. Kormiltcev
Student, Kazan National Research Technical University named after A.N. Tupolev,
10, Karl Marx Street, Kazan 420111, Russia;
E-mail: [email protected]
Alexander D. Uvarov
Student, Kazan National Research Technical University named after A.N. Tupolev,
10, Karl Marx Street, Kazan 420111, Russia;
E-mail: [email protected]
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BUSINESS INFORMATICS Vol. 13 No 1 – 2019
The IEEE Conference Series on Business Informatics is the leading international forum for state-of-the-art research in Business Informatics. The 21st IEEE CBI 2019, held in huge, old and interesting city Moscow, calls for submissions in the multidisciplinary fi eld of Business Informatics, and welcomes a multitude of theoretical and practical perspectives and mind-sets on today’s challenges of the digital transformation.
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