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

2. August, 2001 AB/HS CSM2 Contents n Which problems to solve? n Present situation n Problems and Challenges n Questions associated with modeling paradigms n Combining models in Hybrid MBDSS n Two approaches n Discussion

3. August, 2001 AB/HS CSM3 Which problems to solve? (a) n 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.) n MBDSS: planning, logistics, marketing, design and development of products, environmental system analysis, telecommunications

4. August, 2001 AB/HS CSM4 Which problems to solve? (b) n 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

5. August, 2001 AB/HS CSM5 Which problems to solve? (c) n 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

6. August, 2001 AB/HS CSM6 Problems / challenges n User: Functional and performance Requirements are currently not satisfied. n Builder/designer: No tools for adequate and efficient building of dynamic systems available (Current tools and knowledge prohibit this) n 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.

7. August, 2001 AB/HS CSM7 Present situation: reflection n 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 n In this paper we address the modelling paradigm dimension.

8. August, 2001 AB/HS CSM8 Modelling paradigms (a) n Paradigm = a worldview of thinking, using a specific type of models and associated solvers/analysis tools with an ontology describing the paradigm n Examples: simulation, single-criterion optimisation, multi- criteria model analysis, LP-modelling, soft simulation, etc. n Which paradigm to use (each paradigm has a certain representation power, fitness to use)

9. August, 2001 AB/HS CSM9 Questions about Modelling paradigms (1) n Which ones to use?? n Fitness for use in relation to problem (domain) n Alternative paradigm(s) can be used??? n Combination of (sub)models belonging to various paradigms  exchange of information  shared data model n 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) n How to deal with a body of semantic knowledge in a specific paradigm?

10. August, 2001 AB/HS CSM10 Questions about Modelling paradigms (2) n Combination of (sub)models belonging to various paradigms  exchange of information  shared data model n How to manage and control relationships and data exchange between sub models (taking into account a variety of precedence relationships between them). n How to use a variety of data sources with their DM (with associated ontology and semantics).

11. August, 2001 AB/HS CSM11 Combining Models in Hybrid MBDSS Requires n 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.

12. August, 2001 AB/HS CSM12 Transparent Access and Exchange: 2 Approaches n 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. n Pragmatic approach. –Extend known model with std Rel DM. –RDM of model is part of DM of system. –Ensure precedence relationships while using data. n Practice: Cater for both options.

13. August, 2001 AB/HS CSM13 Proposed MBDSS (c)

14. August, 2001 AB/HS CSM14 Discussion and concluding remarks n Many unsolved questions, problems, challenges n Some answers on which direction to follow

15. August, 2001 AB/HS CSM15 Combination of (Sub) Models: Manage and control Relationships. n 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..

16. August, 2001 AB/HS CSM16 Prerequisites for solution (using AM): n Shared Reference Data Model (RDM) that is used to describe all necessary entity types and attributes in the OS of interest. n Precisely define these and the temporal aspects. n 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)) n DM of each (Sub) Model is Part of RDM.

17. August, 2001 AB/HS CSM17 Model Management: MMS n 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.

18. August, 2001 AB/HS CSM18 Model Management: MMS (2) MMS functions for user/modeler (Scenario level): Using data for problem instance (governed by PM) n 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. n Adapt models on symbolic level n Adapt data to be used (+ storage in db) n Instantiate adapted models using the data and subsequent solution using solvers. n Combine results and report. Reruns until satisfied.

19. August, 2001 AB/HS CSM19 Model Management: MMS (3) Remarks on definitions: n Generic symbolic AM of a particular type for a particular purpose (as you see them in textbooks). For example a generic transport problem. n 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. n 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). n User interaction + model solvers --> Results (using entity ID’s and vars)