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Integrating Model Management Concept & Planning Process 2001. 8. 17. ( )

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2/29 What is Model? ? Data Mathematical Relationship b/w Data

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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

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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)

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

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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

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

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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 Drill Up & Down .

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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)

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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)

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11/29 DSS Design Procedure DB , . , Planning System .

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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)

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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

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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

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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

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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)

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17/29 Forecasting for Production Planning IHPP* approach Forecasting Aggregate Production Planning Disaggregate Production Planning Forecasting Module Historical Data , Effective Demand Aggregate Forecasting Model Product Type Qty Disaggregate Forecasting Model Product Item Qty

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18/29 SM for Forecasting Model

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19/29 Object-relational Schema

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20/29 Execution of the Model

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21/29 Modification of the Model

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22/29 Supply Chain Planning Supply Chain Network , Feasible(or Balanced) Plan . Planning Procedure Rule . Planning Survey . , Planning System Survey Data Integration Plan Interaction .

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23/29 ( ) Supply Chain Facility Outsourcing PlantWarehouse[i] DB & MB data plan [ ] [ ] 1. Resource 2. Work Calendar 3. Resource Breakdown 4. Demand Satisfaction 5. Leadtime Reduction .. [ ] Warehouse [ ] 1. Capacity 2.

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

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

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26/29 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

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27/29 Supply Chain General Mathematical Model Representation Model Schema Storage Structure Tightly Coupled Loosely Coupled XML XML DTD ( Schema) Integration of Model Schema Integration Rule Guideline Legacy System , Model Data .

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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)

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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)