A Long View of Research and Practice in Operations Research and Management Science - The Past and the Future

A Long View of Research and Practice in Operations Research and Management Science - The Past and the Future

von: ManMohan S. Sodhi, Christopher S. Tang

Springer-Verlag, 2010

ISBN: 9781441968104 , 300 Seiten

Format: PDF

Kopierschutz: Wasserzeichen

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Preis: 130,89 EUR

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Mehr zum Inhalt

A Long View of Research and Practice in Operations Research and Management Science - The Past and the Future


 

Foreword

4

Acknowledgments

6

Contents

8

About Professor Arthur M. Geoffrion

16

Contributors

19

1 Introduction: A Long View of Research and Practice in Operations Research and Management Science

22

1.1 The Roots of Operations Research

22

1.2 About This Compilation

23

1.3 Part I---A Long View of the Past

23

1.3.1 Use of OR for Economic Development

23

1.3.2 The Principal Approaches for Solving Large-Scale Mathematical Programs

24

1.3.3 Efficient Distribution System Designs

24

1.3.4 Modeling and Modeling Frameworks

24

1.3.5 Distribution and Supply Chain Planning from 1985 to 2010

25

1.3.6 Insight from Application

25

1.4 Part II---A Long View of the Future

25

1.4.1 Extending Modeling Interfaces to Deal with Uncertainty

25

1.4.2 Extending Applications in the Supply Chain

26

1.4.3 Global Trade

26

1.4.4 Globally Integrated Enterprises

26

1.4.5 The Internet

27

1.4.6 Health Care

27

1.4.7 Happiness

27

1.4.8 The OR/MS Ecosystem as the Context for the Future

28

References

28

Part I A Long View of the Past

30

2 Economic Planning Models for India in the 1960s

31

2.1 Preface

31

2.2 Introduction

32

2.3 The MIT Model for India

32

2.4 The Manne--Weisskopf Model for India

34

2.5 Epilogue

37

2.6 Concluding Reflections

38

2.7 Notes

39

3 The Persistence and Effectiveness of Large-Scale Mathematical Programming Strategies: Projection, Outer Linearization, and Inner Linearization

43

3.1 Introduction

43

3.2 Projection

44

3.2.1 Projection in Interior Point Methods

44

3.2.2 Projection in Discrete Optimization

45

3.3 Outer Linearization

46

3.3.1 Nonlinear Mixed-Integer Programming Methods

47

3.3.2 Outer Approximation for Convex, DynamicOptimization

48

3.4 Inner Linearization

49

3.4.1 Inner and Outer Approximations for ConvexOptimization

50

3.4.2 Linearization in Approximate Dynamic Programming

51

3.5 Conclusions

52

References

52

4 Multicommodity Distribution System Design by Benders Decomposition

54

4.1 Introduction

55

4.1.1 The Model

55

4.1.2 Discussion of the Model

56

4.1.3 Plan of the Paper

59

4.2 Application of Benders Decomposition

60

4.2.1 Specialization of Benders Decomposition

61

4.2.2 Details on Step 2b

62

4.2.3 The Variant Actually Used

64

4.2.4 Re-Optimization

65

4.3 Computer Implementation

66

4.3.1 Master Problem

66

4.3.2 Subproblem

67

4.3.3 Data Input and Storage

67

4.4 Solution of a Large Practical Problem

68

4.4.1 Overview

68

4.4.2 Eight Types of Computer Runs

68

4.4.3 Computational Performance

72

4.5 A Lesson on Model Representation

74

4.6 Conclusion

78

References

79

5 Structured Modeling and Model Management

81

5.1 Introduction

81

5.2 A Brief History of Model Management

82

5.3 Structured Modeling

86

5.3.1 Structured Model Schema

87

5.3.2 Genus Graph

89

5.3.3 Elemental Detail

90

5.3.4 Modules

91

5.3.5 Structured Modeling Language (SML)

91

5.3.6 Structured Modeling Environments

92

5.4 Structured Modeling Contributions to Model Management

94

5.5 Limitations of Structured Modeling

95

5.6 Limitations of Model Management

96

5.7 Trajectory of Model Management in the Internet Era

98

5.8 Next Generation Model Management

98

5.8.1 Enterprise Model Management

99

5.8.2 Service-Based Model Management

99

5.8.3 Leveraging XML and Data Warehouse/OLAPTechnology

100

5.8.4 Model Management as Knowledge Management

100

5.8.5 Search-Based Model Management

102

5.8.6 Computational Model Management

102

5.8.7 Model Management: Dinosaur or Leading Edge?

103

5.9 Summary

103

References

104

6 Retrospective: 25 Years Applying Management Science to Logistics

107

6.1 Where It All Began

107

6.2 The Rise of Logistics

110

6.3 The Rise of Finance

110

6.4 Globalization

111

6.5 Computer Technology

111

6.6 Optimizing Solver Technology

111

6.7 Insight Takes Off

112

6.8 Bumps in the Road

114

6.9 The View Ahead

115

6.10 In Sum

116

7 Optimization Tradecraft: Hard-Won Insights from Real-World Decision Support

117

7.1 Design Before You Build

118

7.2 Bound All Decisions

119

7.3 Expect Any Constraint to Become an Objective, and Vice Versa

120

7.4 Classical Sensitivity Analysis Is Bunk---Parametric Analysis Is Not

121

7.5 Model and Plan Robustly

122

7.6 Model Persistence

122

7.7 Pay Attention to Your Dual

124

7.8 Spreadsheets (and Algebraic Modeling Languages) Are Easy, Addictive, and Limiting

125

7.9 Heuristics Can Be Hazardous

126

7.10 Modeling Components

128

7.11 Designing Model Reports

129

7.12 Conclusion

130

References

131

Part II A Long View of the Future

133

8 Challenges in Adding a Stochastic Programming/Scenario Planning Capability to a General Purpose Optimization Modeling System

134

8.1 Introduction

134

8.1.1 Tribute

135

8.2 Statement of the SP Problem

135

8.2.1 Applications

136

8.2.2 Background and Related Work

137

8.3 Steps in Building an SP Model

137

8.3.1 Statement/Formulation of an SP Model in LINGO

138

8.3.2 Statement/Formulation of an SP Model in the What'sBest! Spreadsheet System

139

8.3.3 Multi-stage Models

141

8.4 Scenario Generation

144

8.4.1 Uniform Random Number Generation

144

8.4.2 Random Numbers from Arbitrary Distributions

144

8.4.3 Quasi-random Numbers and Latin Hypercube Sampling

145

8.4.4 Generating Correlated Random Variables

146

8.5 Solution Output for an SP Model

148

8.5.1 Histograms

148

8.5.2 Expected Value of Perfect Information and Modeling Uncertainty

149

8.6 Conclusions

151

References

151

9 Advances in Business Analytics at HP Laboratories

153

9.1 Introduction

153

9.1.1 Diverse Applied Research Areas with High Business Impact

155

9.2 Revenue Coverage Optimization: A New Approachfor Product Variety Management

156

9.2.1 Solution

157

9.2.2 Results

165

9.2.3 Summary

166

9.3 Wisdom Without the Crowd

166

9.3.1 Mechanism Design

167

9.4 Experimental Verification

169

9.5 Applications and Results

170

9.6 Modeling Rare Events in Marketing: Not a Rare Event

171

9.6.1 Methodology

173

9.6.2 Empirical Application and Results

176

9.7 Distribution Network Design

180

9.7.1 Outbound Network Design

180

9.7.2 A Formal Model

182

9.7.3 Implementation

184

9.7.4 Regarding Data

185

9.7.5 Exemplary Analyses

185

9.8 Collaborations and Conclusion

188

References

188

10 Global Trade Process and Supply Chain Management

190

10.1 Introduction

191

10.2 Supply Chain Design and Trade Processes

193

10.2.1 Supply Chain Design

193

10.2.2 Trade Process Uncertainties and Risks

196

10.2.3 Postponement Design

196

10.3 Improving Global Trade Processes in Supply Chains

198

10.3.1 Logistics Efficiency and Bilateral Trade

198

10.3.2 Cross-Border Processes for Supply Chain Security

200

10.3.3 IT-Enabled Global Trade Management for Efficient Trade Process

202

10.3.4 Empirical Analysis of Trade Processes

205

10.4 Concluding Remarks

207

References

207

11 Sustainable Globally Integrated Enterprise (GIE)

209

11.1 Introduction

209

11.2 An Overview of GIEs and the Challenges they Face

211

11.3 The Evolution of Supply Chains and the Sense-and-Respond Value Net

213

11.4 A Case Study

218

11.4.1 Extended Enterprise Supply-Chain Management

220

11.4.2 Innovative Business Models and Business Optimization

221

11.4.3 Adaptive Sense-and-Respond Value Net

222

11.4.4 Sense-and-Respond Demand Conditioning

222

11.4.5 Value-Driven Services and Delivery

224

11.5 Sustainability of the Globally Integrated Enterprise

225

11.6 Conclusion

229

References

229

12 Cyberinfrastructure and Optimization

232

12.1 Cyberinfrastructure and Optimization

233

12.2 COIN-OR

235

12.3 The NEOS Server

235

12.4 Optimization Services

236

12.5 Intelligent Optimization Systems

238

12.6 Advanced Computing

239

12.7 Prospects for Cyberinfrastructure in Optimization

240

References

241

13 Perspectives on Health-Care Resource Management Problems

243

13.1 Introduction

243

13.2 A Multi-dimensional Taxonomy of Health-Care Resource Management

245

13.2.1 Who and What of Health-Care Resource Management

245

13.2.2 Decision Horizon

246

13.2.3 Level of Uncertainty

248

13.2.4 Decision Criteria

248

13.3 Operations Research Literature on Resource Management Decisions in Healthcare

249

13.3.1 Nurse Scheduling

250

13.3.2 Scheduling of Other Health-Care Professionals

252

13.3.3 Patient Scheduling

252

13.3.4 Facility Scheduling

253

13.3.5 Longer Term Planning

254

13.4 Summary, Conclusions, and Directions for Future Work

255

References

256

14 Optimizing Happiness

260

14.1 Introduction

260

14.2 Time Allocation Model

263

14.2.1 Optimal Allocation

266

14.3 Income--Happiness Relationship

269

14.4 Predicted Versus Actual Happiness

271

14.5 Higher Pay---Less Satisfaction

275

14.6 Social Comparison

278

14.7 Reframing

279

14.8 Conclusions

281

References

282

15 Conclusion: A Long View of Research and Practice in Operations Research and Management Science

285

15.1 Introduction

285

15.2 The OR/MS Ecosystem

286

15.3 Strengths

288

15.3.1 Problem Orientation

288

15.3.2 Generality or Non-domain Specificity

289

15.3.3 Multidisciplinary Nature

289

15.3.4 Grounding in Mathematical Theory

289

15.3.5 Ability to Add Value to Information Technology

290

15.4 Weaknesses

290

15.4.1 The Imbalance in OR/MS Journals

290

15.4.2 Unclear Identity

291

15.4.3 Excessive Tools Orientation

292

15.4.4 The Makeup of Professional Societies

292

15.5 Opportunities

293

15.5.1 Improving Enterprise IT Applications

293

15.5.2 Extending Applications from One Industry to Another

293

15.5.3 New Sectors

294

15.5.4 New Computing Platforms

295

15.5.5 Globalization

295

15.5.6 The Environment

295

15.5.7 AACSB's Reversal Regarding the MBA Curriculum

296

15.6 Threats

296

15.6.1 Rapidly Disseminating OR/MS Tools

296

15.6.2 Decreasing Native-Born Student Population in OR/MS

296

15.6.3 Dispersion of OR/MS Practitioners

297

15.6.4 Shaky Position in Business Schools

297

15.6.5 Slow Growth in Visible Employment

298

15.7 What Academics, Practitioners, Universities, and Funding Agencies Should Do

298

15.7.1 Increase Opportunities for Practice

299

15.7.2 Improve Research

301

15.7.3 Improve Education

302

15.8 What Next?

304

References

304