1. Integrating Model Management Concept & Planning Process 2001. 8. 17. ( 金 ) 서울大學校 産業工學科 製造統合自動化硏究室 梁 榮 哲

2. 2/29 What is Model? 정의 ?  컴퓨터가 이해할 수 있는 형태의 자료형  Data  Mathematical Relationship b/w Data

3. 3/29 Model Management Systems Functions  Interfacing models with users  Integrating models or components of models with each other  Constructing models or components of models  Integrating models or components of models with solver (engine)  Reporting the results of model instance

4. 4/29 Literatures Modeling  Model Representation (Geoffrion, 1987, 1988; Muhanna, Pick, 1994; Dolk, 1988)  Model Data & Storage Structure (Huh, Chung, 1995)  Model Integration (Tsai, 1998) Execution  Engine Selection Rule  Distributed Systems (Huh, Kim, Chung, 1998; Dolk, 2000) Analysis  System Performance (Mayer, 1998) Application  Simulation (Bley, Oltermann, Wuttke, 2000)  GIS (Bennett, 1997)

5. 5/29 Structured Modeling Characteristics to represent models  Hierarchically organized  Partitioned  Attributed acyclic graph Frameworks  Elemental structure  Generic structure  Modular structure Geoffrion (1987)

6. 6/29 Elemental Structure – structured modeling Aims at  Capturing all the definitional detail of a specific model instance NUTRITION TEST NUTRITION LEVELS TOTAL COST MIN DAILY REQTSANAYLYSIS QUANTITYUNIT COSTS NUTRIENTSMATERIALS Primitive entity Attribute Function Test

7. 7/29 Generic Structure – structured modeling Aims  To capture the natural familial groupings of elements T : NLEVEL NLEVEL TOTCOST MINANAYLYSIS Q UCOST NUTRMATERIAL

8. 8/29 Modular Structure – structured modeling Aims  To organize generic structure hierarchically according to commonality or semantic relatedness &FEEDMIX &NUT_DATA &MATERIALS Q NLEVEL T : NLEVEL TOTCOST NUTR MIN MATERIAL UCOST ANALYSIS Module Decision Maker 에 의해 Drill Up & Down 을 할 수 있다.

9. 9/29 Integrating Models and Engines Arguments solve() GAMS LP Model Type SML LP Model Type AMPL LP Model Type Simplex Algorithm Branch-and-Bound Algorithm Network Simplex Algorithm AMPL IP Model Type AMPL Transportation Model Type Interface Huh, Chung, (1995)

10. A Structured Modeling Based Methodology to Design Decision Support Systems S. Raghunathan Dept. of Accounting and MIS, Bowling Green State Univ. USA Decision Support Systems, Vol 17. (1996)

11. 11/29 DSS Design Procedure 현실의 문제  기존에 사용하는 DB 또는 데 이터 저장 시스템이 존재하고, 그 스키마도 현재의 시스템에 맞춰져 있는 상태이다.  따라서, 데이터 시스템과 Planning System 이 연동하기 위해서는 별도의 노력이 투여 되어야 한다.

12. An object relational approach for the design of decision support systems - Theory and Methodology - Ananth Srinivasan, David Sundaram Dept. of Mgmt Sci. and Info. Sys., The Univ. of Auckland, New Zealand EJOR, Vol. 127 (2000)

13. 13/29 Introduction Realistic Problems  Individual items of data  Combinations of such data to reflect the structure of specific problems (models)  Rules of manipulation whereby new data item values are created as per specified rules of computation Objective of Paper  To describe a systematic approach to the design of systems that provide decision support for a particular class of complex organizational problems

14. 14/29 Common criticism (Muhanna, Pick 1994)  There is no guiding theory or set of design principles Empirical Survey  Users are reluctant to use such systems if they do not provide relatively seamless connections to existing and familiar modeling environments

15. 15/29 Conceptual Foundation Structured Modeling (Geoffrion, 1987)  Model representation and manipulation without sacrificing the rigor of the conceptualization ORDBMS (Stonebraker, 1996)  Useful features Abstract data typing Linking with a procedural language Predicate calculus based access language Function specification Event driven manipulation Full DBMS functionality Provide a variety of interfaces for multiple classes of users

16. 16/29 Layered Framework for Modeling Unsuccessful implementation of MMS  The lack of a comprehensive general framework for conceptual modeling Implementation have tended to be domain specific Hence not applicable in a variety of application settings  Constraints imposed by technology Decision Support Modeling Structured Modeling OR Modeling Interaction Implementation Conceptualization OR-DBMS implementation Multiple mode implementation (ex. predicate calculus visualization)

17. 17/29 Forecasting for Production Planning IHPP* approach  Forecasting  Aggregate Production Planning  Disaggregate Production Planning Forecasting Module  Historical Data 를 사용, 현재고 정보 사용 Effective Demand 산출을 목표  Aggregate Forecasting Model Product Type Qty 에 대한 예측  Disaggregate Forecasting Model Product Item Qty 에 대한 예측

18. 18/29 SM for Forecasting Model

19. 19/29 Object-relational Schema

20. 20/29 Execution of the Model

21. 21/29 Modification of the Model

22. 22/29 Supply Chain Planning 의 특징 다양한 주체의 참여  거대한 Supply Chain 을 구성하는 각 주체들이 참여하는 Network 형태의 조직  어떠한 하나의 주체에 의해 지배되지 않고, 각 주체가 만족스 러울 수 있는 Feasible(or Balanced) Plan 을 산출해내어야 하 는 어려움을 안고 있다.  각 주체는 나름의 Planning Procedure 또는 Rule 을 가지고 있 다.  이 주체들의 제반 여건을 Planning 에 반영시키는 작업은 상당 기간의 Survey 가 필요.  하지만, 현재까지의 Planning System 은 위의 Survey 의 결과 로 Data Integration 과 Plan Interaction 만 가능.

23. 23/29 문제 상황 ( 예 ) Supply Chain 내의 Facility Outsourcing PlantWarehouse[i] DB & MB data plan [ 목적함수 ] 생산 및 분배 비용 최소화 [ 제약식 ] 1. 생산 Resource 2. Work Calendar 3. Resource Breakdown 4. Demand Satisfaction 5. Leadtime Reduction ….. [ 목적함수 ] Warehouse 운영비용 최소화 [ 제약식 ] 1. 공간 Capacity 2. 재고 회전율 …

24. 24/29 Prerequisites General (Common) Model Representation Principles Model Schema Maintenance Model Integration Technology Inference Engine Network Environment Model Base Management System

25. 25/29 이전의 Framework Model Manager LP interfaceGA interfaceLR interface Result document Result Data DB Model DB data model data LPGALR

26. 26/29 가능한 Framework Model Directory P/S 1 P/S n P/S 2 P/S 1 P/S 2 P/S 3 P/S 4 P/S n-1 P/S n

27. 27/29 해야 할 일 Supply Chain 환경을 고려한 General Mathematical Model Representation Model Schema Storage Structure 분산 환경에서의 통신 방법  Tightly Coupled  Loosely Coupled XML 를 통한 통신 XML DTD ( 또는 Schema) 구축이 필요 Integration of Model Schema  Integration 시에 필요한 Rule 의 Guideline 을 제시 Legacy System 이 존재할 경우, Model 과 Data 를 통합해야 하는 문제에 대한 고려.

28. 28/29 References Arthur M. Geoffrion, “An Introduction to Structured Modeling”, Management Science, Vol. 33, (1987) Daniel R. Dolk, “Model Management and Structured Modeling : The Role of an Information Resource Dictionary System”, Management Science, Vol. 31, (1988) Daniel R. Dolk, “Integrated model management in the data warehouse era”, EJOR, Vol. 122, (2000) David A. Bennett, “A framework for the integration of geographical information systems and modelbase management”, Int. J. of Geographical Information Science, Vol. 11, (1997) H. Bley, R. Oltermann, C.C. Wuttke, “Distributed model management system for material flow simulation”, J. of Materials Processing Tech., Vol. 107, (2000) Margeret K. Mayer, “Future Trends in Model Management Systems : Parallel and Distributed Extensions”, Decision Support Systems, Vol. 22 (1998) Richard G. Ramirez, Chee Ching & Robert D. St. Louis, “Independence and mappings in model-based decision support systems”, Decision Support Systems, Vol. 10, (1993)

29. 29/29 References Robert W. Blanning, “Model management systems : An Overview”, Decision Support Systems, Vol. 9, (1993) S. Y. Huh, Q. B. Chung, “A Model Management Framework for Heterogeneous Algebraic Models : Object-oriented Database Management Systems Approach” Int. J. Mgmt. Sci., Vol 23, (1995) S. Y. Huh, H. M. Kim, Q. B. Chung, “Framework for Change Notification and View Synchronization in Distributed Model Management Systems”, Int. J. Mgmt. Sci., Vol 27, (1999) Waleed A. Muhanna, Roger Alan Pick, “Meta-modeling Concepts and Tools for Model Management : A Systems Approach”, Mgmt. Sci., Vol. 40, (1994) Yao-Chuan Tsai, “Model integration using SML”, Decision Support Systems, Vol. 22, (1998) Yao-Chuan Tasi, “Comparative analysis of model management and relational database management”, Int. J. of Mgmt. Sci., Vol. 29, (2001)