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www.elsevier.com/locate/autcon
Automation in Constructio
An agent-based framework for supply chain
coordination in construction
Xiaolong Xuea, Xiaodong Lia, Qiping Shenb,*, Yaowu Wanga
aSchool of Management, Harbin Institute of Technology, PR ChinabDepartment of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong
Received 1 February 2004; received in revised form 1 July 2004; accepted 1 August 2004
Abstract
Supply chain coordination has become a critical success factor for supply chain management (SCM) and effectively
improving the performance of organizations in various industries. Coordination refers to the integration of different parts of an
organization or different organizations in supply chain to accomplish a collective set of tasks and to achieve mutual benefits.
This paper defines the concepts of construction supply chain (CSC) and construction supply chain management, especially
regards construction supply chain management as the coordination of interorganizations decision making in construction supply
chain and the integration of key construction business processes and key members involved in construction supply chain. Much
research and practice indicate that there still are many problems in construction, most of which are supply chain problems. The
research analyzes the problems in construction supply chain. In order to resolve these problems and improving the performance
of construction, an agent-based framework for construction supply chain coordination is designed based on the agent
technology and multiattribute negotiation and multiattribute utility theory (MAUT). The framework, which integrates the
construction organizations in construction supply chain and multiattribute negotiation model into a multiagent system (MAS),
provides a solution for supply chain coordination in construction through multiattribute negotiation mechanism on the Internet.
Finally, the prototype of the framework is developed and tentatively run based on an imaginary construction project. The trial
run reveals the feasibility to implement the agent-based framework for coordination in construction.
D 2004 Elsevier B.V. All rights reserved.
Keywords: Construction; Coordination and management; Intelligent agent; Multiattribute negotiation; Multiagent systems; Supply chain
1. Introduction
In recent years, the application of supply chain
management (SCM) philosophy to the construction
0926-5805/$ - see front matter D 2004 Elsevier B.V. All rights reserved.
doi:10.1016/j.autcon.2004.08.010
* Corresponding author.
E-mail address: [email protected] (Q. Shen).
industry has been widely investigated as an effective
and efficient management measure and strategy to
improving the performance of construction, which has
suffered from high fragmentation, large waste, poor
productivity, cost and time overruns, and conflicts and
disputes for a long time [1–3], and to address
adversarial interorganizational relationship of organ-
n 14 (2005) 413–430
X. Xue et al. / Automation in Construction 14 (2005) 413–430414
ization by increasing number of construction organ-
izations and researchers [1,4–12]. SCM can be
considered as the coordination of distributed decision
making of organizations or participants on material
flow, information flow, human flow, and cash flow in
supply chain from systems perspective.
According to Lau et al. (2004) [48], SCM is
defined as bcoordination of independent enterprises in
order to improve the performance of the whole supply
chain by considering their individual needsQ. This
definition describes the main function and principle of
SCM, i.e., coordination. Swaminathan and Tayur [13]
classify SCM issues into two broad categories:
configuration (design-oriented) issue that relates to
the basic infrastructure on which the supply chain
executes, and coordination (execution-oriented) issues
that relate to the actual execution of supply chain.
Schneeweiss and Zimmer [14] also regard SCM as a
management activity that has to do with the coordi-
nation of logistic process being locally controlled by
various independent organizations (decision-making
units) in the environment of internationalization and
globalization of markets together with an increased
focus on organizations’ core competence. Up to now,
supply chain coordination has become a very popular
research topic among the research community in SCM
and a vital management issue of organizations in the
collaborative–competitive business environment.
Coordination is bmanaging the dependencies
between activitiesQ [15]. It is defined as a mutually
beneficial and well-defined relationship entered into
by two or more organizations to achieve common
goals. It also refers to the integration of different parts
of an organization or different organizations in supply
chain to accomplish a collective set of tasks and to
achieve mutual benefits. It involves more formal
relationships, objectives and actions which are
mutual, compatible and common, not necessary a
centralized authority [16].
Multiagent systems (MAS) technology offers new
means and tools for supply chain coordination [17,18].
According to Wooldridge and Jennings (1995) [49], an
agent is a self-contained program capable of control-
ling its own decision making and acting based on its
perception of its environment, in order to one or more
goals. An agent must possess any two of the following
three behavioral attributes: autonomy, cooperation, and
learning [19]. MAS comprises a number of intelligent
agents, which represents the real world parties and co-
operate to reach the desired objectives. In MAS, each
agent attempts to maximize its own utility while
cooperating with other agents to achieve their goals
[20]. The main advantage of MAS is its responsibilities
for acting various components of the engineering
process or participants of the business process which
is delegated to a number of agents. MAS is suitable for
domains that involve interactions between different
organizations with different objectives and proprietary
information [21]. Based on these discussions, we can
clearly see that SCM system is a typical MAS, where
the participants are delegated to different agents.
Furthermore, agent-based supply chain coordination
has been proved to be an effective mechanism to
improve the performance of SCM [22–24].
The core principles of SCM and agent technology
provide new perspectives for construction supply chain
(CSC) management. However, little research has been
conducted to investigate the application of intelligent
agent to coordination problems in CSC. The Centre for
Integrated Facility Engineering of Stanford University
established a distribution cooperative CAD environ-
ment entitled AgentCAD, which presented a frame-
work for collaborative distributed facility engineering
[25]. Anumba et al. [26] presented the key features of
an agent-based system for collaborative design of
portal frame structures and made a significant contri-
bution by allowing for peer to peer negotiation between
design agents. Pena-Mora and Wang [27] proposed a
collaborative negotiation methodology and a computer
agent named CONVINCER, which incorporates that
methodology to facilitate or mediate the negotiation of
conflicts in large-scale civil engineering projects. Min
and Bjornsson [28] presented a conceptual model of
agent-based supply chain automation, in which a
project agent gathers actual construction progress
information and sends to subcontractor agents and
supplier agents, respectively, over the Internet. They
evaluated an agent-based SCM model compared with
traditional SCM practice through simulation. Ren et al.
[29] developed multiagent system for construction
claims negotiation (MASCOT) to resolve inefficiency
problems. Kim and Paulson [30] presented an agent-
based compensatory negotiation methodology to facil-
itate the distributed coordination of project schedule
changes wherein a project can be rescheduled dynam-
ically through negotiation by all of the concerned
X. Xue et al. / Automation in Construction 14 (2005) 413–430 415
subcontractors. The methodology consists of a com-
pensatory negotiation strategy based on utility of
timing, multilinked negotiation protocols, and mes-
sage-handling mechanisms.
Whereas these researchers have addressed a
number of key issues in the field of CSC, very few
researchers have considered CSC management as the
coordination of interorganizations’ decision making in
CSC and apply the agent technology into the whole
CSC management from a systemic perspective. Most
research projects tend to look into agent-based
internal coordination of an organization, which
focuses on one of the stages in CSC, such as design
coordination, project schedule changes coordination,
and construction claims and conflicts resolving. These
approaches may lead to the optimization of a number
of subsystems; however, the sum of the optimized
subsystems may present a problem to the optimization
of the system as a whole.
This paper presents an agent-based framework for
supply chain coordination in construction (ABS3C)
based on multiattribute negotiation and utility theory,
which integrates design and construction, and organ-
izations (or participants), including owner, designer,
general contractor (GC), subcontractors, and suppliers
in CSC. This integrated coordination framework
extends the internal supply chain of general contractor
to external supply chain of designer, subcontractors,
and suppliers. In the decision-making process of
participants in CSC, the factors: cost, time, quality,
safety, and environment normally are considered as
the main decision-making variables. Here, we regard
the five factors as five attributes of decision making in
the construction process. An agent-based multiattri-
bute negotiation mechanism is designed for support-
ing the realization of the framework. The research
methodology is described. The concepts and problems
of CSC are defined and discussed. A prototype system
of the agent-based framework for supply chain
coordination in construction has been evaluated
through the use of a hypothetical construction project
and a toolkit called ZEUS.
2. Theoretical foundation
According to Pena-Mora and Wang [27], negotia-
tion theory is the study of exchanges between
participants to reconcile their differences and produce
a settlement. Negotiation is defined as one kind of
decision-making process where two or more partic-
ipants jointly search for a space of solution with the
goal of achieving consensus [31]. From this defini-
tion, it can be found that negotiation is an effective
mechanism for supply chain coordination in con-
struction. A successful negotiation needs a number of
elements in the negotiation process. For example, the
participants have a commitment to settle the matter at
hand; good communication skills are required; there is
an agreement between participants on what is actually
the matter under negotiation. There are three out-
comes of negotiation: win/win, win/lose, and lose/
lose. The win/win outcome meets the needs of both
participants [32]. One of the objectives of our research
is to achieve the win/win outcome.
Since a number of factors, such as cost, time,
quality, safety, and environment, must be considered
in the decision-making process of CSC management,
our research adopts the multiattribute negotiation
technology to coordinate the CSC. These factors are
regarded as the attributes involved in CSC decision
making. The multiattribute negotiation technology is
developed based on the multiattribute utility theory
(MAUT), which is an analytical tool for making
decisions involving multiple interdependent objec-
tives based on uncertainty and utility analyses [33]
and an evaluation scheme for estimating various
products and performance [34,35]. This research
adopts MAUT to evaluate CSC decision-making
problems. The procedure of MAUT application to
supply chain coordination in construction is shown in
Fig. 1 (adapted from Ref. [36]), where a decision is
one of a set of possible solutions to the supply chain
coordination problems in construction and an attribute
is one of a set of factors that will influence the CSC
decision-making process. The application of MAUT
will be discussed in the subsequent sections in detail.
According to Gupta et al. [17], agent technology
provides a convenient tool to enhance communication
and coordination in a distributed system to ensure
efficient decision making. Kwon and Lee [22] also
believe that MAS, where multiple agents work
collectively to solve specific interorganization prob-
lems, provides an effective platform for coordination
across organizations in the supply chain. This research
adopts multiagent technology to improve the coordi-
Fig. 1. Procedure of using MAUT in supply chain coordination in construction.
X. Xue et al. / Automation in Construction 14 (2005) 413–430416
nation of CSC decision making. According to the
definition of MAS, CSC management system is a
typical MAS. In this kind of MAS, the partners, i.e.,
general contractor (GC), owner, designer, subcontrac-
tors, and suppliers are delegated to corresponding
agents, i.e., GC agent, owner agent, designer agent,
subcontractor agents, and supplier agents. These agents
are designed and realized in ZEUS, an advanced
developing environment for distributed multiagent
systems and has been used to develop agent-based
coordination systems in construction [26,29,37,38].
Multiagent technology is integrated to negotiation and
multiattribute utility theory in the research. Each agent
possessing own preferences and utilities autonomously
negotiates to other agents. Thus, agents can act
collectively and efficiently as a society and cooperate
to achieve their own goals as well as the common goals
of the whole CSC management.
3. Concepts and problems of CSC
3.1. The concepts of CSC
CSC consists of all construction processes, from
the initial demands by the client/owner, through
design and construction, to maintenance, replacement
and eventual demolition of the projects. It also
consists of organizations involved in the construction
process, such as client/owner, designer, GC, subcon-
tractor, and suppliers. CSC is not only a chain of
construction businesses with business-to-business
relationships but also a network of multiple organ-
izations and relationships, which includes the flow of
information, the flow of materials, services or
products, and the flow of funds between owner,
designer, GC, subcontractors, and suppliers [8,9,39–
41]. According to Muya et al. [42], there are three
types of CSC: the primary supply chain, which
delivers the materials that are incorporated into the
final construction products; the support chain, which
provides equipment and materials that facilitate
construction, and the human resource supply chain
which involves the supply of labor. In this paper, CSC
is considered from the stage of owner demands to the
stages of design, construction, and handover. A
typical model of CSC is shown in Fig. 2. In the
model of CSC, GC is the core of the CSC. And the
owner and designer are the other two main partners in
CSC. Excepting the direct suppliers of GC, subcon-
tractors are also regarded as the suppliers of GC;
meanwhile, subcontractors have their own suppliers.
Although a number of researchers have provided
definitions for CSC management (e.g., Refs. [12,43]),
for consistency, this research defines CSC manage-
ment as follows: CSC management is the coordination
of interorganizations’ decision making in CSC and the
integration of key construction business processes and
key members involved in CSC, including client/
owner, designer, GC, subcontractors, suppliers, etc.
CSC management focuses on how firms utilize their
suppliers’ processes, technology and capability to
enhance competitive advantage. It is a management
philosophy that extends traditional intra-enterprise
activities by bringing partners together with the
common goal of optimization and efficiency. CSC
management emphasizes on long-term, win/win, and
cooperative relationships between stakeholders in
systemic perspective. Its ultimate goal is to improve
construction performance and add client value at less
cost.
We have identified eight key construction busi-
ness processes that are implemented within the CSC
across organizational boundaries. They are: project
management, client service management, supplier
relationship management, demand management,
order fulfillment, construction flow management,
environment management, and research and develop-
ment. Fig. 3 presents a schematic view of CSC
management.
Fig. 2. Model of construction supply chain.
X. Xue et al. / Automation in Construction 14 (2005) 413–430 417
3.2. Problems in CSC
Although there have been many changes in the
construction industry as a result of the development of
technology and culture over the last decades, CSCs do
not seem to have changed much. Many problems still
exist in CSC. According to Ref. [40], the major
Fig. 3. Problems in CSC (ad
problems originate at the interfaces of different
participants or stages involved in the CSC, as shown
in Fig. 3. The problems are caused by myopic and
independent control of the CSC.
Love et al. [12] and Mohamed [44] noted the
highly fragmented characteristics of the construction
industry. For example, the separation of design and
apted from Ref. [40]).
X. Xue et al. / Automation in Construction 14 (2005) 413–430418
construction, lack of coordination and integration
between various functional disciplines, poor commu-
nication, etc., are the important impact factors causing
performance-related problems, such as low produc-
tivity, cost and time overrun, conflicts, and disputes.
Palaneeswaran et al. [11] revealed the weak links in
CSC as follows:
! Adversarial relationships between clients and
contractors;
! Inadequate recognition of the sharing of risks and
benefits;
! Fragmented approaches;
! Narrow minded bwin/loseQ attitudes and short-termfocus;
! Power domination and frequent contractual non-
commitments resulting in adverse performance
track records with poor quality, conflicts, disputes,
and claims;
! Prime focus on bid prices (with inadequate focus
on life-cycle costs and ultimate value);
! Less transparency coupled with inadequate infor-
mation exchanges and limited communications;
! Minimal or no direct interactions that foster
sustainable long-term relationships.
In order to overcome the shortcomings (weak
links) of CSC and resolve the problems in CSC, and
to further improve the performance of the whole CSC,
this research presents a solution which integrates the
agent technology and multiattribute negotiation tech-
nology. This solution will be explained in details in
the following sections.
4. Design of agent-based framework for supply
chain coordination
4.1. Integrated design
CSC involves multiple agents that delegate
organizations to autonomously perform tasks
through exchanging information. It has increasing
number of participants due to the increasing scale
and complexity of construction projects. As a
result, the coordination among the participants
becomes a challenge that is vital to the perform-
ance of CSC and the value to the client. These
require an agent-based framework which has an
appropriate structure and effective coordination
mechanism to promote efficient communication
among all parties. The framework should also be
stable, flexible, and user-friendly. Based on these
considerations, we have designed the framework as
shown in Fig. 4.
In this framework, the domain agents include both
dserviceT agents (i.e., coordinator agent, monitor
agent, and name server agent) and dspecialtyT agents(i.e., owner agent, design agent, GC agent, subcon-
tractor agents, and supplier agents). The suppliers
include both GC’s suppliers and subcontractors’
suppliers. Here, we assume that all materials and
human resources are arranged by GC or subcontrac-
tors and there are no owner’s suppliers in this
framework. All agents communicate and cooperate
through the Internet.
All specialty agents advertise their abilities, knowl-
edge, and preferences in the acquaintance database
maintained by of the coordinator agent, and their
addresses in the address book maintained by the name
server agent. The specialty agents use the coordinator
agent to identify agents with the required abilities,
knowledge, and preferences. They also use the name
server agent to determine the addresses of the
identified agents.
The coordinator agent has a mailbox and message
handler for receiving and responding to queries from
agents about the abilities and preferences of other
agents, and an acquaintance database for storing the
abilities and prefernces of the agents. It functions by
periodically querying all the agents in the society
about their abilities and preferences, and storing the
returned information in its acquaintance database. The
monitor agent is used to view, analyse societies of
agents. It functions by querying other agents about its
states and processes, and then collating and interpret-
ing the replies to create an up-to-date model of the
agents’ collective behavior. This model can be viewed
from different perspectives through visualization
tools. The name server agent provides a look-up
service for agents’ addresses and has a mailbox and
message handler, the component needed for receiving
and responding to agents’ requests for the addresses of
other agents.
The specialty agents, which are independent and
autonomous in making decisions, are designed to
Fig. 4. An agent-based framework for supply chain coordination in construction.
X. Xue et al. / Automation in Construction 14 (2005) 413–430 419
delegate corresponding behaviors of CSC partici-
pants. The GC agent stands for GC to perform its
responsibilities, including cost control, schedule,
quality, safety, and environment performance of the
project through coordination with subcontrctors,
suppliers, owner, and designers. The subcontractor
agents simulate the decision-making process of
subcontractors to negotiate with GC and their
suppliers. The owner agent tracks the real schedule,
cost, and quality of the project and duly send the
information of design changes (new demands) to the
designer agents and the GC agent. The designer
agents will respond to the demands and send
information and design change drawings to the
owner and the GC. The GC agent reschedules the
project, makes new decisions, and sends the infor-
mation to subcontractor agents, supplier agents, and
owner agent. In the framework, all coordination
processes are based on multiattribute negotiation
(bM-NegotiationQ as shown in Fig. 4) between
sepcialty agents. According to Andreas [45], agent-
based automated negotiation provides an effective
mechanism to coordinate the decision-making activ-
ities in supply chain. The multiattribute negotiation
mechanism for coordinating the interorganizations’
decsion making in CSC is the significant compo-
nents of ABS3C. The following section describes the
design process of the multiattribute negotiation
mechanism in detail.
4.2. Multiattribute negotiation model
Although Kim and Paulson [30] present an agent-
based compensatory methodology to improve the
coordination of project schedule changes, it only
considers the utility of timing and cost and is not
suitable for the coordination of decision making in
CSC. We present an agent-based multiattribute
negotiation model for supply chain coordination in
construction, which creatively extends the general
negotiation model for A/E/C [27] from SCM and
utility theory perspectives and integrates the composi-
tional multiattribute negotiation model [46], as shown
in Fig. 5. The model consists of three elements: CSC
participants, multiattribute negotiation process, and
the outcome.
Each of these elements plays a role in a generic
coordination problem within the domain. All partic-
ipants reveal their attributes and preferences in the
negotiation process with due regard to their parent
organizations and relationships to the issues. Here,
each of the CSC participants is represented by a
corresponding agent in the framework. The outcome
will be the negotiation results determined by the
Fig. 5. Multiattribute negotiation model for supply chain coordination in construction.
X. Xue et al. / Automation in Construction 14 (2005) 413–430420
interactive process between participants in CSC. The
multiattribute negotiation process is the interactive
exchange and elicitation of participants’ preferences.
The following section will focus on the negotiation
process.
4.3. Multiattribute negotiation process
According to Jonker and Treur [46], the multi-
attribute negotiation process includes the following
five steps:
(1) Evaluation of the attributes of the initial sol-
utions made by the participants;
(2) These evaluations are aggregated into overall
utilities of these initial solutions;
(3) Provision of the target utility;
(4) Based on the target utility and the distribution of
attributes, the values of the target attributes are
determined, which lead to a new round of
decision making;
(5) For each of the target attributes, an attribute
value is chosen that has an evaluation value as
close as possible to the target evaluation value
for the attribute.
In the agent-based multiattribute negotiation
model, these five steps are simplified to three
processes: attributes evaluation, utility determination,
and attribute planning.
4.3.1. Attributes evaluation
As shown in Fig. 5, many attributes are involved in
the process of supply chain coordination in construc-
tion. Attributes evaluation is a process whereby the
value of the attributes are evaluated, based on the
preferences of the participants in the CSC. All
attributes are classified into two categories: quantita-
tive and qualitative. For the quantitative attributes,
such as cost and time, the value of these attributes can
be directly calculated according to certain principles.
For the qualitative attributes, such as quality, safety,
Fig. 6. Multiattribute negotiation protocol.
X. Xue et al. / Automation in Construction 14 (2005) 413–430 421
and environment, it is necessary to construct a scale to
measure the levels of these attributes. In this paper, a
scale, from 0 (worst) to 10 (best), serves as the
measure of the evaluation. The most frequently used
five levels are: 1—worse, 3—bad, 5—normal, 7—
good, and 9—better. When a compromise is needed,
0, 2, 4, 6, 8, 10 are intermediate values between the
two adjacent judgments.
After evaluating the attributes in a table form, an
evaluation matrix that includes m participants and n
attributes is obtained:
X ¼ xij� �
m�n
where xij is the value of the jth attribute evaluated by
the ith participant in a decision-making process.
Next, the matrix X is translated into a unitary
normalization matrix of vector:
Y ¼ yij� �
m�n
where 0VyijV1, and
yij ¼ xij
, ffiffiffiffiffiffiffiffiffiffiffiffiffiXmi¼1
x2ij
s
4.3.2. Utility determination
In this process, the target utility is determined. The
utility of the ith participant’s decision making (Ui) is
given by
Ui ¼Xnj¼1
wjyij
Where the wj is the weighting of the jth attributes:
wj ¼Xnj¼1
akj
� Xnk¼1
Xnj¼1
akj
WherePn
j¼1 wj ¼ 1 and akj is the value of relative
weightiness between kth attribute and jth attribute.
The value of akj is given by
akj ¼1 ; if kth attribute is more important than jth attribute
0:5 ; if kth attribute is important as jth attribute
0 ; if kth attribute is less important than jth attribute
8<:
Jonker and Treur [46] presented the formula of target
utility (TU) of participant’s decision making as
follows:
TU ¼ UBOW þ CS
where UBOW is the utility of the own decision making,
and the concession step (CS) is determined by
CS ¼ b 1� l=UBOWð Þ UBOT=UBOWð Þ
where UBOT is the utility of the other participant’s
decision making. The factor (1�l/UBOW) expresses
CS will decrease to 0 if the UBOW approximates the
minimal utility l and (UBOT�UBOW) expresses the
current utility gap. b stands for the negotiation speed.
4.3.3. Attribute planning
The attribute planning process refers to target
evaluation and configuration determination [46]. The
target evaluation of jth attribute TEj is given by
TEj ¼ 1� sð ÞBTEj þ sEBOT; j
where BTEj is the basic target evaluation of jth
attribute, which is determined in such away that
AwjBTEj=TU. EBOT, j is the jth attribute evaluation
value of other agent. s stands for the configuration
tolerance. BTEj is determined as the below format:
BTEj ¼ EBOW; j þ aj=N� �
TU� UBOWð Þ
where EBOW,j is the jth attribute evaluation value of
decision agent, N is a normalization factor, which is
the weighted sum of a ’s with the relative importance
factors being the weightings: N=P
wjaj,. aj=(1�wj)
(1�EBOW, j).
The configuration determination for the next
decision making includes three steps. Firstly, attribute
values are determined with an evaluation that is as
close as possible to the target evaluation value.
Fig. 7. Roles represented in ABS3C.
X. Xue et al. / Automation in Construction 14 (2005) 413–430422
Secondly, a partial configuration (excepting the
quantitative attributes, such as cost and time) is
selected from the closest value of attribute. The final
step is to reevaluate the quantitative attributes.
4.4. Multiattribute negotiation protocol
Negotiation protocol controls the interactions
among agents by constraining the way the agents
interact [30]. It also specifies the kinds of deals that
the agents can make, as well as the sequence of offers
and counteroffers that are allowed [29]. In our
framework, multiple attributes are considered in the
process of negotiation between agents. For example,
GC agent needs to negotiate with subcontractor
agents, supplier agents, designer agents, and owner
agent regarding different attributes of solutions, such
as time, cost, safety, and quality, based on the overall
utility. The negotiation protocol is named as multi-
Fig. 8. Interactions of a
attribute negotiation protocol in this paper, which is
shown in Fig. 6.
Each agent offers its current best solution that is
also saved locally. Then, the solution is sent to
other agents. The sender waits for a message from
other agents. If the message is acceptance of the
offer, it indicates that the sent solution is consistent
with the other agents’ solution, and the negotiation
is successful. If the message is NoMoreSolution, it
means that the other agent has run out of solutions.
If the same is true for the initiating agent as well,
then the negotiation ends unsuccessfully; otherwise,
the agent will continue to generate new solutions. If
the message includes a changed solution from the
other agent, this solution is checked for compati-
bility with any of the past solutions generated by
this agent. If an intersection is found, it presents a
mutually acceptable solution, which determines the
negotiation successfully. Otherwise, this indicates
gents in ABS3C.
Table 1
Summary of Interactions
Collaboration Explanation
1 Registration Agents notify the NS of their
presence
2a Resolve query A request for the network
location of a named agent
2b List query A request for all agents of a
particular type
3a Location response The location of the agent
previously in question
3b List response The list of agents previously
in question
4a Answer agent name NS answers the inquiries
about the agent name
4b Inquire agent name Agent inquires from NS
about the other agent name it
needs to communicate
with them
5a Ability and preference
response
Information about an agent’s
current abilities and
preference
5b Ability and preference
request
Ask for information about
recipient’s abilities and
preference
6 Information request Asks all activity be forwarded
7 Activity notification A copy of ant message sent
8 Find request Asks for agents with
particular abilities and
preference
6 Find response A list of agents matching the
desired criteria
10 Inquire A agent inquire from
facilitator about the other
agent’s abilities and
preference
11 Answer The facilitator answers the
inquires from agents
12 Negotiate with
Subcontractors
GC negotiates the relative
construction solutions with
Subcontractors
13 Sent solution (decision
making)
Subcontractors send their
decision making to GC
14 Send demands information GC sends its demands plan to
suppliers
15 Response GC demands Suppliers send their supply
plan to GC
16 Inquire project information
and send change
The owner inquires from GC
about the project information
and sends demands change
17 Report and response change GC reports the project
information to Owner and
response the demands change
18 Require GC requires for relative
problems of the design
drawings
Collaboration Explanation
16 Explain Designer explains the design
drawings
20 Put forward new demands The owner puts forward his
new demands, i.e., design
change
21 Offer design drawings The designer offers drawings
to meet the owner’s demands
Table 1 (continued)
X. Xue et al. / Automation in Construction 14 (2005) 413–430 423
that utility gap remains between the initiating agent
and other agents. This loop can be repeated. The
main advantage of this protocol is that it guarantees
the discovery of the Pareto optimum [47].
5. Implementation of ABS3C
The prototype is developed by using the ZEUS
agent building toolkit. ZEUS is an advanced devel-
opment toolkit for constructing distributed multi-
agent applications. It defines a MAS approach and
supports it with a visual environment for capturing
user specification of agents that are used to generate
Java source code of the agents. ZEUS toolkit
proposes five main technologies to enable agents to
coordinate at the knowledge level: information
discovery, communication, ontology, coordination,
and legacy software problems. ZEUS is a culmina-
tion of a careful synthesis of established agent to
provide an integrated environment for the rapid
software engineering of collaborative agent applica-
tions [37].
The implementation process consists of the follow-
ing steps: role modelling, application design, and trial
run.
5.1. Role modeling in ABS3C
ZEUS agent building toolkit adopts role model-
ing to address the specification, analysis, design,
implementation, and maintenance of agents. Role
models formalize the definition of an agent role and
provide a comprehensible means of analyzing the
problem in question. The role models are grouped
into domains. The domains provide a context that
enables developers to compare their planned system
with existing applications. Role models, which
Fig. 9. Schedule of HCP building.
Fig. 10. Ontology in ABS3C.
X. Xue et al. / Automation in Construction 14 (2005) 413–430424
describe the dynamic interaction between roles, are
architectural patterns that depict the high-level
similarities between related systems, i.e., the prob-
lems inherent to each domain, but not how they
were solved. The role models of ABS3C are
illustrated in Fig. 7. The interactions between these
roles are shown in Fig. 8. Table 1 summarizes the
collaboration relationships.
5.2. Application design
This process is illustrated based on a hypothetical
construction project: HCP building. The HCP
involves the following participants: owner, designer,
general contractor, groundwork subcontractor, civil
and structure subcontractor, building services subcon-
tractor, finishing works subcontractor, concrete sup-
plier, and finishing materials supplier. The project
schedule is shown in Fig. 9.
5.2.1. Ontology creation
Ontology is a set of declarative knowledge re-
presenting every significant concept within a partic-
ular application domain. The significance of a
concept is easily assessed, if meaningful interaction
cannot occur between agents without both parties
being aware of it, then the concept is significant and
must be modeled. Ontology contains the key con-
cepts within the specific application domain, the
attributes of each concept, the types of each attribute,
and any restrictions on the attributes. In ZEUS, an
individual domain concept is described by using the
term dfactT. ZEUS provides two kinds of fact: abstract
and entity. In ABS3C, all the concepts refer to the
entity. The ontology created in ABS3C is shown in
Fig. 10.
5.2.2. Agent creation
Agent creation includes three steps: agent defi-
nition, agent organization, and agent coordination.
Agent definition determines the planning parameters,
task and initial resources allocation. Agent organ-
ization illustrates the relationship between the own
agent and other agents and acquaintance abilities
from other agents. Agent coordination defines the
coordination protocols and strategies between the
own agent and other agents. Multiattribute negotia-
tion and multiattribute negotiation protocol as
Fig. 11. Relations of agents in ABS3C.
Table 2
Agent created in the ABS3C
Agent name Roles played
Specialty
agents
GC agent CSC head (Negotiation
initiator, Manager and
constructor)
Owner agent Client
Designer agent CSC participant
(Negotiation partner,
supplier, designer)
Groundwork
subcontractor agent
CSC participant
(Negotiation partner,
supplier, constructor)
Civil and structure
subcontractor agent
CSC participant
(Negotiation partner,
supplier, constructor,
consumer)
Building services
subcontractor agent
CSC participant
(Negotiation partner,
supplier, constructor)
Finishing works
subcontractor agent
CSC participant
(Negotiation partner,
supplier, constructor,
consumer)
Concrete supplier
agent
CSC participant
(Negotiation partner,
supplier)
Finishing material
supplier agent
CSC participant
(Negotiation partner,
supplier)
Service
agents
ANS agent Agent name server
Monitor agent Monitor (to view, analyse or
debug societies of agents)
Construction
coordinator agent
Coordinator (receive and
respond to queries from
agents about the abilities
and preferences of other
agents)
X. Xue et al. / Automation in Construction 14 (2005) 413–430 425
previously mentioned are integrated into the proto-
type of ABS3C as the coordination protocols and
strategy in ZEUS. Table 2 lists the agents in the
prototype of ABS3C.
GC agent as the CSC head possesses the core
position comparing with other agents. GC is the
superior of subcontractors and suppliers. The owner
supervises the activities of GC and designer. The
relationships of GC and owner, subcontractor and
subcontractor are cooperative partnering. In HCP case,
the finishing material supplier and concrete supplier
are considered as the direct supplier of corresponding
subcontractors, so the suppliers are considered as the
subordinates of subcontractors. The relations between
agents in ABS3C are shown in Fig. 11.
5.2.3. Service and specialty agents configuration
The service agents, i.e., agent name server, monitor
and coordinator, are named as dutility agentsT in
ZEUS. The monitor agent is given the dvisualizersTtitle, and the coordinator agent is labeled as
dfacilitatorsT. Specialty agents, such as GC agent,
civil and structure subcontractor agent (as seen in
Table 2) are called task agents.
Service and specialty agents configuration (as
shown in Fig. 12) determines their running param-
eters in the prototype of ABS3C, e.g., the name of
agent, the IP address of host, time grain, the name
of address file, external program connected to agent,
etc.
5.2.4. External program
ZEUS allows users to link an external java class
(program) to executing ZEUS agent program. Once
linked to the agent program, external program can
utilize the agent’s public methods to query or modify
the agent’s internal state. The internal event model in
ZEUS provides a mechanism whereby all significant
events occurring in the agent can be monitored, for
example, planning events, resource, acquaintance
database events, message events, execution events,
and coordination engine events. Using the event
model, an external program that is linked to ZEUS
agent can monitor particular events in the agent and
react to them. In the prototype of ABS3C, each
Fig. 12. Configuration of service and specialty agents.
X. Xue et al. / Automation in Construction 14 (2005) 413–430426
specialty agent is linked to an external program as
shown in Fig. 12. Each external program provides a
user interface (negotiation window) through which the
Fig. 13. A screen shot when ru
users, for example, GC, subcontractor, supplier, etc.,
input their preferences on a decision making in the
process of CSC.
ns the prototype system.
Fig. 14. Negotiation window (GCUI).
Fig. 16. Dialogue for new goal of agents.
X. Xue et al. / Automation in Construction 14 (2005) 413–430 427
5.3. Trial run and discussions
The commands of running the prototype of ABS3C
can be generated through the dCode GeneratorT panelin ZEUS. Fig. 13 gives a screen shot of the prototype
system. When running the prototype, the negotiation
Fig. 15. Information exchange of agents in ABS3C.
windows will display on the screen waiting for the
decision makers input the preferences and relative
weightiness of attributes. In the HCP case, we only
consider five negotiation attributes: cost, time, quality,
safety, and environment, which present the decision
maker’s different preferences and utility, as shown in
Fig. 14. The above information will be sent to the
coordinator agent and corresponding negotiation
agent. Fig. 15 displays the structure of HCP CSC
and the process of information exchange and commu-
nication between service agent and specialty agent.
When a new round of negotiation starts, the new goal
of agent can be created through the dEnter Goal
DialogT (Fig. 16).The prototype provides tool (monitor agent) to
dynamically monitor the traffic volume of each agent
and interagent traffic volume, as shown in Figs. 17
and 18. User can analyze and evaluate the perform-
ance and activities of agents. From Figs. 17 and 18, it
can be concluded that the running process is stable.
Although the frequency of communication of con-
struction coordinator is much higher than other
agents, this is not a problem. The phenomenon rightly
reflects the hub role of the coordinator agent in the
framework.
6. Conclusions
Many researches and practices have proved that
supply chain coordination has become the crucial
strategy for successful SCM and effectively improv-
Fig. 17. Traffic volume of each agent.
X. Xue et al. / Automation in Construction 14 (2005) 413–430428
ing the performance of organizations in various
industries. This paper defines that concepts of CSC
and CSC management, especially regards CSC
management as coordination of interorganizations’
decision making in CSC and the integration of key
construction business processes and key members
involved in CSC. The research finds that there still are
many problems in CSC. In order to resolve the
problems and improve the performance of construc-
tion, an agent-based framework for construction
supply chain coordination is designed based on the
agent technology and multiattribute negotiation and
utility theory. The framework, which integrates the
organizations in CSC into an agent system, provides a
solution for the supply chain coordination in con-
struction through the application of multiattribute
negotiation mechanism. The trial run reveals the
Fig. 18. Interagent t
feasibility to implement ABS3C. The development
of prototype system provides an effective platform to
simulate the coordination process in CSC. It is helpful
to reduce the duration of interorganization decision
making and enhance the quick response ability to
meet the change of demands in competitive marketing
environment.
Since this research on agent-based supply chain
coordination in construction is an initial effort, some
issues still need to be addressed. For example, the
multiattribute negotiation mechanism for supply chain
coordination in construction needs to be improved,
especially how agents can elicit user’s preferences and
utility functions relevant to negotiation-related deci-
sion making. Real construction process involves
various thousands of participants and activities, so
how to use agents to efficiently simulate the behaviors
raffic volume.
X. Xue et al. / Automation in Construction 14 (2005) 413–430 429
of decision making is a real challenge. The developed
prototype system also has some components needed
to be improved. This reveals that relative tools and
approaches needed to be further developed for the
implementation of ABS3C.
Acknowledgement
The work described in this paper was supported by
the Research Grants Council of the Hong Kong
Special Administrative Region, P.R. China.
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