Advances in DEA Theory and Applications: With Extensions to Forecasting Models (Wiley Series in Operations Research and Management Science)

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: 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 Presents a novel area of application for DEA; namely, the performance evaluation of forecasting models Promotes the use of DEA to assess the performance of forecasting models in a wide area of applications Provides rich, detailed examples and case studies 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. Content: Title Page Copyright Page Contents List of Contributors About the Authors Preface Part 1 DEA Theory Chapter 1 Radial DEA Models 1.1 Introduction 1.2 Basic Data 1.3 Input-Oriented CCR Model 1.3.1 The CRS Model 1.4 The Input-Oriented BCC Model 1.4.1 The VRS Model 1.5 The Output-Oriented Model 1.6 Assurance Region Method 1.7 The Assumptions behind Radial Models 1.8 A Sample Radial Model References Chapter 2 Non-Radial DEA Models 2.1 Introduction 2.2 The SBM Model 2.2.1 Input-Oriented SBM 2.2.2 Output-Oriented SBM 2.2.3 Non-Oriented SBM 2.3 An Example of an SBM Model. 2.4 The Dual Program of the SBM Model2.5 Extensions of the SBM Model 2.5.1 Variable-Returns-to-Scale (VRS) Model 2.5.2 Weighted-SBM Model 2.6 Concluding Remarks References Chapter 3 Directional Distance DEA Models 3.1 Introduction 3.2 Directional Distance Model 3.3 Variable-Returns-to-Scale DD Models 3.4 Slacks-Based DD Model 3.5 Choice of Directional Vectors References Chapter 4 Super-Efficiency DEA Models 4.1 Introduction 4.2 Radial Super-Efficiency Models 4.2.1 Input-Oriented Radial Super-Efficiency Model 4.2.2 Output-Oriented Radial Super-Efficiency Model. 4.2.3 Infeasibility Issues in the VRS Model4.3 Non-radial Super-Efficiency Models 4.3.1 Input-Oriented Non-Radial Super-Efficiency Model 4.3.2 Output-Oriented Non-Radial Super-Efficiency Model 4.3.3 Non-Oriented Non-Radial Super-Efficiency Model 4.3.4 Variable-Returns-to-Scale Models 4.4 An Example of a Super-Efficiency Model References Chapter 5 Determining Returns to Scale in the VRS DEA Model 5.1 Introduction 5.2 Technology Specification and Scale Elasticity 5.2.1 Technology 5.2.2 Measure of Scale Elasticity 5.2.3 Scale Elasticity in DEA Models 5.3 Summary References. Chapter 6 Malmquist Productivity Index Models6.1 Introduction 6.2 Radial Malmquist Model 6.3 Non-Radial and Oriented Malmquist Model 6.4 Non-Radial and Non-Oriented Malmquist Model 6.5 Cumulative Malmquist Index (CMI) 6.6 Adjusted Malmquist Index (AMI) 6.7 Numerical Example 6.7.1 DMU A 6.7.2 DMU B 6.7.3 DMU C 6.7.4 DMU D 6.8 Concluding Remarks References Chapter 7 The Network DEA Model 7.1 Introduction 7.2 Notation and Production Possibility Set 7.3 Description of Network Structure 7.3.1 Inputs and Outputs 7.3.2 Links 7.4 Objective Functions and Efficiencies. 7.4.1 Input-Oriented Case7.4.2 Output-Oriented Case 7.4.3 Non-Oriented Case Reference Chapter 8 The Dynamic DEA Model 8.1 Introduction 8.2 Notation and Production Possibility Set 8.3 Description of Dynamic Structure 8.3.1 Inputs and Outputs 8.3.2 Carry-Overs 8.4 Objective Functions and Efficiencies 8.4.1 Input-Oriented Case 8.4.2 Output-Oriented Case 8.4.3 Non-Oriented Case 8.5 Dynamic Malmquist Index 8.5.1 Dynamic Catch-up Index 8.5.2 Dynamic Frontier Shift Effect 8.5.3 Dynamic Malmquist Index 8.5.4 Dynamic Cumulative Malmquist Index 8.5.5 Dynamic Adjusted Malmquist Index. Title Page -- Copyright Page -- Contents -- List of Contributors -- About the Authors -- Preface -- Part 1 DEA Theory -- Chapter 1 Radial DEA Models -- 1.1 Introduction -- 1.2 Basic Data -- 1.3 Input-Oriented CCR Model -- 1.3.1 The CRS Model -- 1.4 The Input-Oriented BCC Model -- 1.4.1 The VRS Model -- 1.5 The Output-Oriented Model -- 1.6 Assurance Region Method -- 1.7 The Assumptions behind Radial Models -- 1.8 A Sample Radial Model -- References -- Chapter 2 Non-Radial DEA Models -- 2.1 Introduction -- 2.2 The SBM Model -- 2.2.1 Input-Oriented SBM -- 2.2.2 Output-Oriented SBM -- 2.2.3 Non-Oriented SBM -- 2.3 An Example of an SBM Model -- 2.4 The Dual Program of the SBM Model -- 2.5 Extensions of the SBM Model -- 2.5.1 Variable-Returns-to-Scale (VRS) Model -- 2.5.2 Weighted-SBM Model -- 2.6 Concluding Remarks -- References -- Chapter 3 Directional Distance DEA Models -- 3.1 Introduction -- 3.2 Directional Distance Model -- 3.3 Variable-Returns-to-Scale DD Models -- 3.4 Slacks-Based DD Model -- 3.5 Choice of Directional Vectors -- References -- Chapter 4 Super-Efficiency DEA Models -- 4.1 Introduction -- 4.2 Radial Super-Efficiency Models -- 4.2.1 Input-Oriented Radial Super-Efficiency Model -- 4.2.2 Output-Oriented Radial Super-Efficiency Model -- 4.2.3 Infeasibility Issues in the VRS Model -- 4.3 Non-radial Super-Efficiency Models -- 4.3.1 Input-Oriented Non-Radial Super-Efficiency Model -- 4.3.2 Output-Oriented Non-Radial Super-Efficiency Model -- 4.3.3 Non-Oriented Non-Radial Super-Efficiency Model -- 4.3.4 Variable-Returns-to-Scale Models -- 4.4 An Example of a Super-Efficiency Model -- References -- Chapter 5 Determining Returns to Scale in the VRS DEA Model -- 5.1 Introduction -- 5.2 Technology Specification and Scale Elasticity -- 5.2.1 Technology -- 5.2.2 Measure of Scale Elasticity -- 5.2.3 Scale Elasticity in DEA Models -- 5.3 Summary 13.2.2 Qualitative Information on Returns to Scale -- 13.2.3 Quantitative Information on Returns to Scale -- 13.2.4 An Alternative Measure of Scale Elasticity -- 13.3 The Dataset for LIC Operations -- 13.4 Results and Discussion -- 13.4.1 Production-Based Analysis -- 13.4.2 Cost-Based Analysis -- 13.4.3 Returns-to-Scale Issue -- 13.4.4 Sensitivity Analysis -- 13.5 Concluding Remarks -- References -- Chapter 14 An Account of DEA-Based Contributions in the Banking Sector -- 14.1 Introduction -- 14.2 Performance Evaluation of Banks: A Detailed Account -- 14.3 Current State of the Art Summarized -- 14.4 Conclusion -- References -- Chapter 15 DEA in the Healthcare Sector -- 15.1 Introduction -- 15.2 Method and Data -- 15.2.1 Previous Literature -- 15.2.2 Formulas for Efficiency Estimation by DN DEA Model -- 15.2.3 Formulas for Malmquist Index by DN DEA Model -- 15.2.4 Empirical Data -- 15.3 Results -- 15.3.1 Estimated Efficiency Scores -- 15.3.2 Estimated Malmquist Index Scores -- 15.4 Discussion -- 15.4.1 Estimation Results and Policy Implications -- 15.4.2 Further Research Questions -- Acknowledgements -- References -- Chapter 16 DEA in the Transport Sector -- 16.1 Introduction -- 16.2 DNDEA in Transport -- 16.2.1 The Production Technology for the Production Process -- 16.2.2 The Production Technology for the Service Process -- 16.3 Extension -- 16.3.1 The Production Technology for HB Activity -- 16.3.2 The Production Technology for UB Activity -- 16.3.3 The Production Technology for the Service Process -- 16.4 ApplicationAdapted from Yu et al. . -- 16.4.1 Input and Output Variables -- 16.4.2 Empirical Results -- 16.5 Conclusions -- References -- Chapter 17 Dynamic Network Efficiency of Japanese Prefectures -- 17.1 Introduction -- 17.2 Multiperiod Dynamic Multiprocess Network -- 17.3 Efficiency/Productivity Measurement -- 17.4 Empirical Application 17.4.1 Prefectural Production and Data -- 17.4.2 Efficiency Estimates and Their Determinants -- 17.5 Conclusions -- References -- Chapter 18 A Quantitative Analysis of Market Utilization in Electric Power Companies -- 18.1 Introduction -- 18.2 The Functions of the Trading Division -- 18.3 Measuring the Effect of Energy Trading -- 18.3.1 Definition of Transaction Volumes and Prices -- 18.3.2 Constraints on Internal Transactions -- 18.3.3 Profit Maximization -- 18.3.4 Exogenous Variables -- 18.4 DEA Calculation -- 18.5 Empirical Results -- 18.5.1 Results of Profit Maximization -- 18.5.2 Results of DEA -- 18.6 Concluding Remarks -- References -- Chapter 19 DEA in Resource Allocation -- 19.1 Introduction -- 19.2 Centralized DEA in Resource Allocation -- 19.2.1 Minor Adjustment -- 19.2.2 Moderate Adjustment -- 19.2.3 Major Adjustment -- 19.3 Applications of Centralized DEA in Resource Allocation -- 19.3.1 Human Resource Rightsizing in Airports6 -- 19.3.2 Resource Allocation in Container Terminal OperationsAdapted from Chang et al. . -- 19.4 Extension -- 19.4.1 Phase I -- 19.4.2 Phase II -- 19.5 Conclusions -- References -- Chapter 20 How to Deal with Non-convex Frontiers in Data Envelopment Analysis -- 20.1 Introduction -- 20.2 Global Formulation -- 20.2.1 Notation and Basic Tools -- 20.2.2 Uniqueness of Slacks -- 20.2.3 Decomposition of CRS Slacks -- 20.2.4 Scale-Independent Dataset -- 20.3 In-cluster Issue: Scale- and Cluster-Adjusted DEA Score -- 20.3.1 Clusters -- 20.3.2 Solving the CRS Model in the Same Cluster -- 20.3.3 Scale- and Cluster-Adjusted Score -- 20.3.4 Summary of the SAS Computation -- 20.3.5 Global Characterization of SAS-Projected DMUs -- 20.4 An Illustrative Example -- 20.5 The Radial-Model Case -- 20.5.1 Decomposition of CCR Slacks -- 20.5.2 Scale-Adjusted Input and Output -- 20.5.3 Solving the CCR Model in the Same Cluster 9.5.4 Dynamic Divisional Cumulative Malmquist Index -- 9.5.5 Dynamic Divisional Adjusted Malmquist Index -- 9.5.6 Overall Dynamic Malmquist Index -- References -- Chapter 10 Stochastic DEA: The Regression-Based Approach -- 10.1 Introduction -- 10.2 Review of Literature on Stochastic DEA -- 10.2.1 Random Sampling -- 10.2.2 Imprecise Measurement of Data -- 10.2.3 Uncertainty in the Membership of Observations -- 10.2.4 Random Production Possibility Sets -- 10.2.5 Random Noise -- 10.3 Conclusions -- References -- Chapter 11 A Comparative Study of AHP and DEA -- 11.1 Introduction -- 11.2 A Glimpse of Data Envelopment Analysis -- 11.3 Benefit/Cost Analysis by Analytic Hierarchy Process -- 11.3.1 Three-Level Perfect Graph Case -- 11.3.2 General Cases -- 11.4 Efficiencies in AHP and DEA -- 11.4.1 Input x and Output y -- 11.4.2 Weights -- 11.4.3 Efficiency -- 11.4.4 Several Propositions -- 11.5 Concluding Remarks -- References -- Chapter 12 A Computational Method for Solving DEA Problems with Infinitely Many DMUs -- 12.1 Introduction -- 12.2 Problem -- 12.3 Outline of the Method -- 12.4 Details of the Method When Z is One-Dimensional -- 12.4.1 Initial Discretization and Subdivision Parameter -- 12.4.2 Solving (Dh) -- 12.4.3 Deletion/Subdivision Rules -- 12.4.4 Solving the New LP -- 12.4.5 Convergence Check -- 12.5 General Case -- 12.5.1 Initial Discretization -- 12.5.2 Deletion and Subdivision (Bisection) Rules -- 12.5.3 Solving New LPs and Checking Convergence -- 12.6 Concluding Remarks (by Tone) -- Appendix 12.A Proof of Theorem 12.1 -- Appendix 12.B Proof of Theorem 12.2 -- Reference -- Part 2 DEA Applications (Past-Present Scenario) -- Chapter 13 Examining the Productive Performance of Life Insurance Corporation of India -- 13.1 Introduction -- 13.2 Nonparametric Approach to Measuring Scale Elasticity -- 13.2.1 Technology and Returns to Scale 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. -- Provided by publisher