Integrating Model Management Concept & Planning Process
2001. 8. 17. ( )
Slide 2
2/29 What is Model? ? Data Mathematical Relationship b/w
Data
Slide 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
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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)
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
Slide 7
7/29 Generic Structure structured modeling Aims To capture the
natural familial groupings of elements T : NLEVEL NLEVEL TOTCOST
MINANAYLYSIS Q UCOST NUTRMATERIAL
Slide 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 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 .
Slide 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)
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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)
Slide 17
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
Slide 26
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)