Details

Advances in DEA Theory and Applications

With Extensions to Forecasting Models
Wiley Series in Operations Research and Management Science 1. Aufl.

von: Kaoru Tone
75,99 €
Verlag:	Wiley
Format:	PDF
Veröffentl.:	12.04.2017
ISBN/EAN:	9781118946701
Sprache:	englisch
Anzahl Seiten:	576

In den Warenkorb

Als Gutschein

DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.

Beschreibungen

Titelbeschreibung

A key resource and framework for assessing the performance of competing entities, including forecasting models Advances in DEA Theory and Applications provides a much-needed framework for assessing the performance of competing entities with special emphasis on forecasting models. It helps readers to determine the most appropriate methodology in order to make the most accurate decisions for implementation. Written by a noted expert in the field, this text provides a review of the latest advances in DEA theory and applications to the field of forecasting. Designed for use by anyone involved in research in the field of forecasting or in another application area where forecasting drives decision making, this text can be applied to a wide range of contexts, including education, health care, banking, armed forces, auditing, market research, retail outlets, organizational effectiveness, transportation, public housing, and manufacturing. This vital resource: <ul> <li>Explores the latest developments in DEA frameworks for the performance evaluation of entities such as public or private organizational branches or departments, economic sectors, technologies, and stocks</li> <li>Presents a novel area of application for DEA; namely, the performance evaluation of forecasting models</li> <li>Promotes the use of DEA to assess the performance of forecasting models in a wide area of applications</li> <li>Provides rich, detailed examples and case studies</li> </ul> Advances in DEA Theory and Applications includes information on a balanced benchmarking tool that is designed to help organizations examine their assumptions about their productivity and performance.

Inhaltsverzeichnis

LIST OF CONTRIBUTORS xx ABOUT THE AUTHORS xxii PREFACE xxxii PART I DEA THEORY 1 1 Radial DEA Models 3 Kaoru Tone 1.1 Introduction 3 1.2 Basic Data 3 1.3 Input-Oriented CCR Model 4 1.4 The Input-Oriented BCC Model 6 1.5 The Output-Oriented Model 7 1.6 Assurance Region Method 8 1.7 The Assumptions Behind Radial Models 8 1.8 A Sample Radial Model 8 References 10 2 Non-Radial DEA Models 11 Kaoru Tone 2.1 Introduction 11 2.2 The SBM Model 12 2.3 An Example of an SBM Model 15 2.4 The Dual Program of the SBM Model 17 2.5 Extensions of the SBM Model 17 2.6 Concluding Remarks 18 References 19 3 Directional Distance DEA Models 20 Hirofumi Fukuyama and William L. Weber 3.1 Introduction 20 3.2 Directional Distance Model 20 3.3 Variable-Returns-to-Scale DD Models 23 3.4 Slacks-Based DD Model 23 3.5 Choice of Directional Vectors 25 References 26 4 Super-Efficiency DEA Models 28 Kaoru Tone 4.1 Introduction 28 4.2 Radial Super-Efficiency Models 28 4.3 Non-Radial Super-Efficiency Models 29 4.4 An Example of a Super-Efficiency Model 31 References 32 5 Determining Returns to Scale in the VRS DEA Model 33 Biresh K. Sahoo and Kaoru Tone 5.1 Introduction 33 5.2 Technology Specification and Scale Elasticity 34 5.3 Summary 37 References 37 6 Malmquist Productivity Index Models 40 Kaoru Tone and Miki Tsutsui 6.1 Introduction 40 6.2 Radial Malmquist Model 43 6.3 Non-Radial and Oriented Malmquist Model 45 6.4 Non-Radial and Non-Oriented Malmquist Model 47 6.5 Cumulative Malmquist Index (CMI) 48 6.6 Adjusted Malmquist Index (AMI) 49 6.7 Numerical Example 50 6.8 Concluding Remarks 55 References 55 7 The Network DEA Model 57 Kaoru Tone and Miki Tsutsui 7.1 Introduction 57 7.2 Notation and Production Possibility Set 58 7.3 Description of Network Structure 59 7.4 Objective Functions and Efficiencies 61 Reference 63 8 The Dynamic DEA Model 64 Kaoru Tone and Miki Tsutsui 8.1 Introduction 64 8.2 Notation and Production Possibility Set 65 8.3 Description of Dynamic Structure 67 8.4 Objective Functions and Efficiencies 69 8.5 Dynamic Malmquist Index 71 References 73 9 The Dynamic Network DEA Model 74 Kaoru Tone and Miki Tsutsui 9.1 Introduction 74 9.2 Notation and Production Possibility Set 75 9.3 Description of Dynamic Network Structure 77 9.4 Objective Function and Efficiencies 80 9.5 Dynamic Divisional Malmquist Index 82 References 84 10 Stochastic DEA: The Regression-Based Approach 85 Andrew L. Johnson 10.1 Introduction 85 10.2 Review of Literature on Stochastic DEA 87 10.3 Conclusions 96 References 96 11 A Comparative Study of AHP and DEA 100 Kaoru Tone 11.1 Introduction 100 11.2 A Glimpse of Data Envelopment Analysis 100 11.3 Benefit/Cost Analysis by Analytic Hierarchy Process 102 11.4 Efficiencies in AHP and DEA 104 11.5 Concluding Remarks 105 References 106 12 A Computational Method for Solving DEA Problems with Infinitely Many DMUs 107 Abraham Charnes and Kaoru Tone 12.1 Introduction 107 12.2 Problem 108 12.3 Outline of the Method 109 12.4 Details of the Method When Z is One-Dimensional 110 12.5 General Case 113 12.6 Concluding Remarks (by Tone) 115 Appendix 12.A Proof of Theorem 12.1 115 Appendix 12.B Proof of Theorem 12.2 116 Reference 116 PART II DEA APPLICATIONS (PAST–PRESENT SCENARIO) 117 13 Examining the Productive Performance of Life Insurance Corporation of India 119 Kaoru Tone and Biresh K. Sahoo 13.1 Introduction 119 13.2 Nonparametric Approach to Measuring Scale Elasticity 121 13.3 The Dataset for LIC Operations 128 13.4 Results and Discussion 130 13.5 Concluding Remarks 136 References 136 14 An Account of DEA-Based Contributions in the Banking Sector 141 Jamal Ouenniche, Skarleth Carrales, Kaoru Tone and Hirofumi Fukuyama 14.1 Introduction 141 14.2 Performance Evaluation of Banks: A Detailed Account 142 14.3 Current State of the Art Summarized 154 14.4 Conclusion 163 References 169 15 DEA in the Healthcare Sector 172 Hiroyuki Kawaguchi, Kaoru Tone and Miki Tsutsui 15.1 Introduction 172 15.2 Method and Data 174 15.3 Results 184 15.4 Discussion 188 Acknowledgements 189 References 190 16 DEA in the Transport Sector 192 Ming-Miin Yu and Li-Hsueh Chen 16.1 Introduction 192 16.2 DNDEA in Transport 194 16.3 Extension 200 16.4 Application 207 16.5 Conclusions 212 References 212 17 Dynamic Network Efficiency of Japanese Prefectures 216 Hirofumi Fukuyama, Atsuo Hashimoto, Kaoru Tone and William L. Weber 17.1 Introduction 216 17.2 Multiperiod Dynamic Multiprocess Network 217 17.3 Efficiency/Productivity Measurement 221 17.4 Empirical Application 222 17.5 Conclusions 229 References 229 18 A Quantitative Analysis of Market Utilization in Electric Power Companies 231 Miki Tsutsui and Kaoru Tone 18.1 Introduction 231 18.2 The Functions of the Trading Division 232 18.3 Measuring the Effect of Energy Trading 235 18.4 DEA Calculation 242 18.5 Empirical Results 243 18.6 Concluding Remarks 248 References 249 19 DEA in Resource Allocation 250 Ming-Miin Yu and Li-Hsueh Chen 19.1 Introduction 250 19.2 Centralized DEA in Resource Allocation 252 19.3 Applications of Centralized DEA in Resource Allocation 261 19.4 Extension 265 19.5 Conclusions 268 References 268 20 How to Deal with Non-convex Frontiers in Data Envelopment Analysis 271 Kaoru Tone and Miki Tsutsui 20.1 Introduction 271 20.2 Global Formulation 273 20.3 In-cluster Issue: Scale- and Cluster-Adjusted DEA Score 276 20.4 An Illustrative Example 281 20.5 The Radial-Model Case 284 20.6 Scale-Dependent Dataset and Scale Elasticity 287 20.7 Application to a Dataset Concerning Japanese National Universities 289 20.8 Conclusions 294 Appendix 20.A Clustering Using Returns to Scale and Scale Efficiency 295 Appendix 20.B Proofs of Propositions 295 References 298 21 Using DEA to Analyze the Efficiency of Welfare Offices and Influencing Factors: The Case of Japan’s Municipal Public Assistance Programs 300 Masayoshi Hayashi 21.1 Introduction 300 21.2 Institutional Background, DEA, and Efficiency Scores 301 21.3 External Effects on Efficiency 304 21.4 Quantile Regression Analysis 309 21.5 Concluding Remarks 312 Acknowledgements 312 References 312 22 DEA as a Kaizen Tool: SBM Variations Revisited 315 Kaoru Tone 22.1 Introduction 315 22.2 The SBM-Min Model 316 22.3 The SBM-Max Model 318 22.4 Observations 321 22.5 Numerical Examples 323 22.6 Conclusions 330 References 330 PART III DEA FOR FORECASTING AND DECISION-MAKING (PAST–PRESENT–FUTURE SCENARIO) 331 23 Corporate Failure Analysis Using SBM 333 Joseph C. Paradi, Xiaopeng Yang and Kaoru Tone 23.1 Introduction 333 23.2 Literature Review 334 23.3 Methodology 340 23.4 Application to Bankruptcy Prediction 343 23.5 Conclusions 352 References 354 24 Ranking of Bankruptcy Prediction Models under Multiple Criteria 357 Jamal Ouenniche, Mohammad M. Mousavi, Bing Xu and Kaoru Tone 24.1 Introduction 357 24.2 An Overview of Bankruptcy Prediction Models 359 24.3 A Slacks-Based Super-Efficiency Framework for Assessing Bankruptcy Prediction Models 366 24.4 Empirical Results from Super-Efficiency DEA 372 24.5 Conclusion 376 References 377 25 DEA in Performance Evaluation of Crude Oil Prediction Models 381 Jamal Ouenniche, Bing Xu and Kaoru Tone 25.1 Introduction 381 25.2 An Overview of Crude Oil Prices and Their Volatilities 385 25.3 Assessment of Prediction Models of Crude Oil Price Volatility 388 25.4 Conclusion 401 References 402 26 Predictive Efficiency Analysis: A Study of US Hospitals 404 Andrew L. Johnson and Chia-Yen Lee 26.1 Introduction 404 26.2 Modeling of Predictive Efficiency 405 26.3 Study of US Hospitals 408 26.4 Forecasting, Benchmarking, and Frontier Shifting 412 26.5 Conclusions 416 References 417 27 Efficiency Prediction Using Fuzzy Piecewise Autoregression 419 Ming-Miin Yu and Bo Hsiao 27.1 Introduction 419 27.2 Efficiency Prediction 420 27.3 Modeling and Formulation 423 27.4 Illustrating the Application 433 27.5 Discussion 438 27.6 Conclusion 440 References 441 28 Time Series Benchmarking Analysis for New Product Scheduling: Who Are the Competitors and How Fast Are They Moving Forward? 443 Dong-Joon Lim and Timothy R. Anderson 28.1 Introduction 443 28.2 Methodology 445 28.3 Application: Commercial Airplane Development 449 28.4 Conclusion and Matters for Future Work 454 References 455 29 DEA Score Confidence Intervals with Past–Present and Past–Present–Future-Based Resampling 459 Kaoru Tone and Jamal Ouenniche 29.1 Introduction 459 29.2 Proposed Methodology 461 29.3 An Application to Healthcare 465 29.4 Conclusion 476 References 478 30 DEA Models Incorporating Uncertain Future Performance 480 Tsung-Sheng Chang, Kaoru Tone and Chen-Hui Wu 30.1 Introduction 480 30.2 Generalized Dynamic Evaluation Structures 482 30.3 Future Performance Forecasts 484 30.4 Generalized Dynamic DEA Models 487 30.5 Empirical Study 495 30.6 Conclusions 513 References 514 31 Site Selection for the Next-Generation Supercomputing Center of Japan 516 Kaoru Tone 31.1 Introduction 516 31.2 Hierarchical Structure and Group Decision by AHP 519 31.3 DEA Assurance Region Approach 521 31.4 Application to the Site Selection Problem 522 31.5 Decision and Conclusion 527 References 527 APPENDIX A: DEA-SOLVER-PRO 529 INDEX 535

Autorenportrait

KAORU TONE is with the National Graduate Institute for Policy Studies, Japan. His contribution to DEA has a variety of attainments. He authored a classical book Data Envelopment Analysis: A Comprehensive Text with Models, Applications, References and DEA-Solver Software under the co-authorship with Professor Cooper (University of Texas) and Professor Seiford (University of Michigan). He also published many papers on DEA in international journals. Kaoru Tone opened a new avenue for performance evaluation, called Slacks-based Measure (SBM) that is widely utilized over the world. His recent innovations include Network SBM, Dynamic SBM, Dynamic DEA with Network Structure, Congestion, Returns-to-growth in DEA, Ownership-specified Network DEA, Non-convex Frontier DEA, Past-Present-Future Inter-temporal DEA, Resampling DEA and SBM-Max.

Back cover copy

A key resource and framework for assessing the performance of competing entities, including forecasting models Advances in DEA Theory and Applications provides a much-needed framework for assessing the performance of competing entities with special emphasis on forecasting models. It helps readers to determine the most appropriate methodology in order to make the most accurate decisions for implementation. Written by a noted expert in the field, this text provides a review of the latest advances in DEA theory and applications to the field of forecasting. Designed for use by anyone involved in research in the field of forecasting or in another application area where forecasting drives decision making, this text can be applied to a wide range of contexts, including education, health care, banking, armed forces, auditing, market research, retail outlets, organizational effectiveness, transportation, public housing, and manufacturing. This vital resource: <ul> <li>Explores the latest developments in DEA frameworks for the performance evaluation of entities such as public or private organizational branches or departments, economic sectors, technologies, and stocks</li> <li>Presents a novel area of application for DEA; namely, the performance evaluation of forecasting models</li> <li>Promotes the use of DEA to assess the performance of forecasting models in a wide area of applications</li> <li>Provides rich, detailed examples and case studies</li> </ul> Advances in DEA Theory and Applications includes information on a balanced benchmarking tool that is designed to help organizations examine their assumptions about their productivity and performance.