Challenges to model solving software in the internet era Adrie J.M. Beulens Huub Scholten (Wageningen University, Information Technology Group)

Challenges to model solving software in the internet era

Adrie J.M. BeulensHuub Scholten

(Wageningen University, Information Technology Group)

August, 2001 AB/HS CSM 2

Contents

Which problems to solve? Present situation Problems and Challenges Questions associated with modeling paradigms Combining models in Hybrid MBDSS Two approaches Discussion


Which problems to solve? (a)

Many organisations (industry, banking, insurance primary production, public administration, business consultants, knowledge institutes, et.c) use Model Based Decision Support Systems (next to ERP, etc.)

MBDSS: planning, logistics, marketing, design and development of products, environmental system analysis, telecommunications


Which problems to solve? (b) Model based decision making:

– requires a (set of) model(s) representing relations between possible decisions and expected results

– each problem requires a specific (set of) model(s)– each type of model requires a specific technique (solver/analysis tool)

Problem:

using several models for one problem requires a lot of effort, resources and knowledge on these ‘other model types’ to transfer a problem to another modelling paradigm with a different representation format, necessary for that type of model


Which problems to solve? (c)

Performance requirements to MBDSS:– effectively and efficiently support of user– integrated with information infrastructure of organisation to use

and exchange (business data)– user friendly– developed, upgraded, maintained in environment, governed by

dynamics of:• problem domain

• organisation

• ways of working

• infrastructure

– in a cost effective manner


Problems / challenges

User: Functional and performance Requirements are currently not satisfied.

Builder/designer: No tools for adequate and efficient building of dynamic systems available (Current tools and knowledge prohibit this)

Technical problems for developer: – Lack of knowledge of different types of models with ass. Solvers– Applying different tools is cumbersome– Combining models of different types is difficult.


Present situation: reflection

Challenges to MBDSS are based on increased insights and changing technologies and may further be discussed along 4 dimensions”– managing development and exploitation– the human factor– modelling paradigms– hybrid DSS environment

In this paper we address the modelling paradigm dimension.


Modelling paradigms (a)

Paradigm =

a worldview of thinking, using a specific type of models and associated solvers/analysis tools with an ontology describing the paradigm

Examples: simulation, single-criterion optimisation, multi-criteria model analysis, LP-modelling, soft simulation, etc.

Which paradigm to use (each paradigm has a certain representation power, fitness to use)


Questions about Modelling paradigms (1)

Which ones to use?? Fitness for use in relation to problem (domain) Alternative paradigm(s) can be used??? Combination of (sub)models belonging to various paradigms

exchange of information shared data model If some paradigms have equal representation power choices have

to be made (with which are you familiar, which generator/solver is available, which is the best for the problem)

How to deal with a body of semantic knowledge in a specific paradigm?


Questions about Modelling paradigms (2)

Combination of (sub)models belonging to various paradigms exchange of information shared data model

How to manage and control relationships and data exchange between sub models (taking into account a variety of precedence relationships between them).

How to use a variety of data sources with their DM (with associated ontology and semantics).


Combining Models in Hybrid MBDSS Requires Modelling infrastructure that allows:

– transfer from one paradigm to another– couple models from various paradigms– manage the generating and solving/analysing process of such

models or network of models– Models to share and exchange data sources taking into account

transparency and precedence requirements.


Transparent Access and Exchange: 2 Approaches

ESML approach: Common model and data representation format for all models. – Models of different types in same format are interfaced with

associated tools and databases. – DM of model M is part of DM of System.

Pragmatic approach.– Extend known model with std Rel DM.– RDM of model is part of DM of system.– Ensure precedence relationships while using data.

Practice: Cater for both options.


Proposed MBDSS (c)

MMS:model management system

model 1

model i

model n AM

UR

mo

de

l to

olb

ox solverl 1

solverl i

solver n

AM

UR

solve

r too

lbo

x

user 1 user i user n


Discussion and concluding remarks Many unsolved questions, problems, challenges Some answers on which direction to follow


Combination of (Sub) Models: Manage and control Relationships. Relationships:

– Parts of OS are (re)represented: Objects and attributes.– Temporal, I/O relationship while using models. Network of models

with precedence relations that need to be governed by the Process Model Manager.

– Use variety of data sources (+DM) with associated ontology and semantics. Ensure that objects and attributes are exactly the same when exchanged. Especially look at aggregates (along object and time dimension). Ex.: Product and product group, sales per day, per week, etc..


Prerequisites for solution (using AM): Shared Reference Data Model (RDM) that is used to

describe all necessary entity types and attributes in the OS of interest.

Precisely define these and the temporal aspects. Is it in terms of states of objects at specific time points and

or transfers between states in the OS. Some state of the OS(t) --> OS(t+1/t+f(t))

DM of each (Sub) Model is Part of RDM.


Model Management: MMS MMS functions for user/modeler (Symbolic Level):

Gather available “symbolic” models from a model base (source) belonging to different model types. that are important for the (sub) problem and associated solvers.

Gather or define available data sources with their data models that are available or needed.

To adapt/change these symbolic models for the specific model situation. To define relationships between the models (precedence network) for

the problem instances. To define and attach the solvers to be used in a process model, Etc.


Model Management: MMS (2)MMS functions for user/modeler (Scenario level):Using data for problem instance (governed by PM)

To define a scenario with associated process model, scenario database, relationships with previous scenario’s and consequently the relationships with symbolic models, their solvers and subsequently the whereabouts.

Adapt models on symbolic level Adapt data to be used (+ storage in db) Instantiate adapted models using the data and subsequent

solution using solvers. Combine results and report. Reruns until satisfied.


Model Management: MMS (3)Remarks on definitions:

Generic symbolic AM of a particular type for a particular purpose (as you see them in textbooks). For example a generic transport problem.

Symbolic models adapted to the DM of actual OS. So we talk about a model in which we have:

• A reference DM with precise definitions of the entity types and attributes.• Algebraic relationships of the symbolic model expressed using these entity

types and attributes.• A problem scenario is a problem instance to be considered. That is, we

study some object system with a history defined in a database, with assumptions about symbolic models describing some parts of the behavior of that OS and assumptions about the environment of it.

Instantiated models in a scenario are derived from the symbolic ones by filling in appropriate values of ID’s of entities and values of associated attributes (vars).

User interaction + model solvers --> Results (using entity ID’s and vars)

Documents

Challenges to model solving software in the internet era Adrie J.M. Beulens Huub Scholten (Wageningen University, Information Technology Group)