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Handbook of Operations Analytics Using Data Envelopment Analysis.

By: Contributor(s): Material type: TextTextSeries: International Series in Operations Research and Management Science SeriesPublisher: New York, NY : Springer, 2016Copyright date: ©2016Edition: 1st edDescription: 1 online resource (511 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9781489977052
Subject(s): Genre/Form: Additional physical formats: Print version:: Handbook of Operations Analytics Using Data Envelopment AnalysisDDC classification:
  • 658.5036
LOC classification:
  • T57.6-.97
Online resources:
Contents:
Intro -- Preface -- Contents -- Contributors -- Chapter 1: Ranking Decision Making Units: The Cross-Efficiency Evaluation -- 1.1 Introduction -- 1.2 Ranking Methods in DEA -- 1.3 The Cross-Efficiency Evaluation: The Standard Approach -- 1.4 The Choice of DEA Weights in Cross-Efficiency Evaluations -- 1.4.1 Ranking Ranges and Cross-Efficiency Intervals -- 1.4.2 Illustrative Example -- 1.5 The Aggregation of Cross-Efficiencies -- 1.5.1 Illustrative Example (Cont.) -- 1.6 Other Uses -- 1.6.1 Identification of Mavericks and All-Round Performers -- 1.6.2 Classification of DMUs and Benchmarking -- 1.6.3 Fixed Cost and Resource Allocation -- 1.7 Extensions -- 1.7.1 Cross-Efficiency Evaluation with Directional Distance Functions -- 1.7.2 Cross-Efficiency Evaluation with Multiplicative DEA Models -- 1.7.3 Cross-Efficiency Evaluation Under VRS -- 1.7.4 Fuzzy Cross-Efficiency Evaluation -- 1.7.5 Game Cross Efficiency -- 1.8 Conclusions -- References -- Chapter 2: Data Envelopment Analysis for Measuring Environmental Performance -- 2.1 Introduction -- 2.2 Environmental DEA Technology -- 2.3 Models for Measuring Environmental Performance -- 2.3.1 Environmental Efficiency Index -- 2.3.2 Environmental Productivity Index -- 2.3.3 Other Developments -- 2.4 Case Study -- 2.4.1 Data -- 2.4.2 Results and Discussions -- 2.4.2.1 EEI Analysis -- 2.4.2.2 EPI Analysis -- 2.5 Conclusion -- References -- Chapter 3: Input and Output Search in DEA: The Case of Financial Institutions -- 3.1 Introduction -- 3.2 Efficiency Modeling in Financial Institutions -- 3.3 A Case Study: American Banks -- 3.3.1 The Data Set: Three Inputs and Three Outputs -- 3.3.1.1 Labor -- 3.3.1.2 Physical Capital -- 3.3.1.3 Deposits -- 3.3.1.4 Interest and Non-interest Income -- 3.3.1.5 Loans -- 3.3.2 DEA Specification Searches Using Multivariate Methods.
3.3.3 Results Visualization and Strategic Pattern Identification -- 3.3.4 Dissecting the Efficiency Score -- 3.4 Conclusions -- References -- Chapter 4: Multi-period Efficiency Measurement with Fuzzy Data and Weight Restrictions -- 4.1 Introduction -- 4.2 Crisp Network DEA with Weight Restrictions -- 4.3 Fuzzy Multi-period Efficiency with Weight Restrictions -- 4.4 Example -- 4.5 Conclusion -- References -- Chapter 5: Pitching DEA Against SFA in the Context of Chinese Domestic Versus Foreign Banks -- 5.1 Introduction -- 5.2 Conceptual Framework -- 5.2.1 Chinese Banking Sector -- 5.2.2 Modeling Performance to Estimate Bank Efficiency -- 5.2.3 Contextual Variables -- 5.3 Data and Method -- 5.3.1 Data -- 5.3.2 Data Envelopment Analysis (DEA) -- 5.3.3 Stochastic Frontier Analysis (SFA) -- 5.4 Results and Analysis -- 5.4.1 Testing for Scale Inefficiency Using DEA -- 5.4.2 Main DEA Results -- 5.4.2.1 Core Model (Single-Output BCC-O) -- 5.4.2.2 Extended Model (Two-Output BCC-O) -- Overall Potential Improvements Identified by DEA Using the Extended Model -- Assessing the Marginal Role of the Output Variables in DEA: Efficiency Contribution Measures (ECM) for the Extended Model -- 5.4.3 SFA Results -- 5.4.3.1 Core Model (Single-Output Translog Function) -- 5.4.3.2 Extended Model (Two-Output Translog Function) -- 5.4.4 Comparing DEA and SFA Results -- 5.5 Concluding Remarks -- References -- Chapter 6: Assessing Organizations´ Efficiency Adopting Complementary Perspectives: An Empirical Analysis Through Data Envelop... -- 6.1 Introduction -- 6.2 DEA and MDS Methodologies: A Brief Overview -- 6.2.1 The Data Envelopment Analysis Method -- 6.2.2 The Multidimensional Scaling Method -- 6.3 Data and Selection of Indicators -- 6.3.1 Our Sample -- 6.3.2 Inputs and Outputs Employed in the DEA Analysis -- 6.3.3 Indicators Included in the MDS Analysis.
6.4 Studying HEIs´ Efficiency by Means of Data Envelopment Analysis: Results -- 6.5 Combining DEA and MDS Methodologies: Results -- 6.5.1 Preliminary Insights -- 6.6 Results -- 6.7 Concluding Remarks -- Appendix: List of Universities Included in the Analysis and Their Acronyms -- References -- Chapter 7: Capital Stock and Performance of RandD Organizations: A Dynamic DEA-ANP Hybrid Approach -- 7.1 Introduction -- 7.2 Literature Review -- 7.2.1 Current Status of Taiwanese RandD Organizations -- 7.2.2 DEA Applications in RandD Organizations -- 7.3 Research Design -- 7.3.1 Three-Stage Value-Creation Process of RandD Organizations -- 7.3.2 Data Selection and Description -- 7.3.3 Dynamic Extension of Network Slack-Based Measure DEA Model -- 7.4 Results and Discussions -- 7.4.1 Performance Analysis in Value-Creation Process -- 7.4.2 The Relationship Between Capital Stock and RandD Organizations Performance -- 7.5 Conclusions -- References -- Chapter 8: Evaluating Returns to Scale and Convexity in DEA Via Bootstrap: A Case Study with Brazilian Port Terminals -- 8.1 Introduction -- 8.2 Efficiency Measurement and RTS Characterization -- 8.2.1 Measuring Efficiency Scores Under Different Orientations and Frontiers -- 8.2.2 Scaling or RTS Characterization -- 8.2.3 Orientation Impact on RTS Characterization -- 8.3 Estimation and Bootstrapping in DEA -- 8.3.1 Estimation -- 8.3.2 Bootstrapping Method -- 8.4 Case Study: Brazilian Port Terminals -- 8.5 Results -- 8.5.1 Initial Estimates -- 8.5.2 Preliminary Statistics Tests on Initial Estimates -- 8.5.2.1 Testing for Model Specification -- 8.5.2.2 Testing for Differences Between Container and Bulk Terminals -- 8.5.2.3 Testing for Relevant Inputs and Outputs -- 8.5.2.4 Testing for Outliers -- 8.5.3 Bootstrapped Efficiency Scores and Convexity Assumption -- 8.5.4 RTS Characterizations: CIs for SI and uo.
8.5.5 Discussion -- 8.6 Conclusions -- References -- 9: DEA and Cooperative Game Theory -- 9.1 Introduction -- 9.2 Cooperative Game Theory -- 9.2.1 Bargaining Problems -- 9.2.1.1 The Nash Solution -- 9.2.1.2 The Kalai-Smorodinsky Solution -- 9.2.2 Transferable Utility Games -- 9.2.2.1 The Core and Related Concepts -- 9.2.2.2 The Shapley Value -- 9.2.2.3 The Least Core and the Nucleolus -- 9.3 Nash Bargaining Approaches to DEA -- 9.4 TU Cooperative Game Approaches to DEA -- 9.5 Further Potential Applications -- 9.5.1 Nash Decomposition for Process Efficiency in Multistage Production Systems -- 9.5.2 DEA Production Games -- References -- Chapter 10: Measuring Bank Performance: From Static Black Box to Dynamic Network Models -- 10.1 Introduction -- 10.2 Selective Literature Review -- 10.2.1 Network DEA and Dynamic DEA -- 10.2.2 Bank Production and Risk -- 10.3 Preliminaries -- 10.3.1 Black-Box Technology -- 10.3.2 Network Technology with Bad Outputs -- 10.3.3 Dynamic Technology with Carryovers -- 10.3.4 Dynamic-Network Technology -- 10.4 DEA Implementation -- 10.5 A Choice of Variables and Regulatory Constraints -- 10.5.1 Variable Selection: An Example -- 10.5.2 Imposing Bank Regulatory Constraint -- 10.6 A Summary -- References -- Chapter 11: Evaluation and Decomposition of Energy and Environmental Productivity Change Using DEA -- 11.1 Introduction -- 11.2 Luenberger Productivity Indicator and Its Decomposition -- 11.3 DEA Model for Energy and Environmental Efficiency Measurement -- 11.4 Application to China´s Regional Energy and Environmental Productivity Change -- 11.4.1 Data and Variables -- 11.4.2 Results and Discussions -- 11.5 Conclusions -- References -- Chapter 12: Identifying the Global Reference Set in DEA: An Application to the Determination of Returns to Scale -- 12.1 Introduction -- Part I: On Identification of the Global Reference Set.
Part II: On Determination of the RTS -- 12.2 Background -- 12.2.1 Technology Set -- 12.2.2 The RAM Model -- 12.3 Identifying the Global Reference Set (GRS) -- 12.3.1 Definition of the GRS -- 12.3.2 Properties of the GRS -- 12.3.3 Identification of the GRS -- 12.3.4 Properties of the Proposed Approach -- 12.3.5 Numerical example -- 12.4 Determination of Returns to Scale (RTS) -- 12.4.1 Definition of RTS for an Inefficient DMU -- 12.4.2 Determination of RTS Via the BCC Model -- 12.4.3 Determination of RTS Via the CCR Model -- 12.4.4 Numerical Example -- 12.4.4.1 Determining RTS Statuses of the DMUs Using Algorithm I -- 12.4.4.2 Determining RTS Statuses of the DMUs Using Algorithm II -- 12.5 Empirical Application -- 12.5.1 Evaluation of Schools via the RAM Model -- 12.5.2 Determining RTS Statuses of the Efficient Schools -- 12.5.3 Determining RTS Statuses of the Inefficient Schools -- 12.6 Summary and Concluding Remarks -- References -- Chapter 13: Technometrics Study Using DEA on Hybrid Electric Vehicles (HEVs) -- 13.1 Introduction -- 13.2 Methodology -- 13.3 Research Model and Dataset -- 13.3.1 TFDEA Parameters -- 13.3.1.1 Input Variable -- 13.3.1.2 Output Variables -- 13.3.1.3 Categorical Parameter -- 13.3.2 Dataset -- 13.4 Analysis of the Technological Advancement Patterns -- 13.4.1 Two-Seaters and Compact Segments: ``Stagnated´´ -- 13.4.2 Midsize Segment: ``Flourishing´´ -- 13.4.3 Large Segment: ``Emerging´´ -- 13.4.4 SUV Segment: ``Forging Ahead´´ -- 13.4.5 Minivan Segment: ``Crossover´´ -- 13.4.6 Pickup Truck Segment: ``Steady´´ -- 13.5 Conclusion -- Appendix: 2013 State-of-the-Art Frontiers of Different HEV Segments -- References -- Chapter 14: A Radial Framework for Estimating the Efficiency and Returns to Scale of a Multi-component Production System in DEA -- 14.1 Introduction -- 14.2 Radial Performance Measurement for a Multi-component System.
14.2.1 Basic Model.
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Intro -- Preface -- Contents -- Contributors -- Chapter 1: Ranking Decision Making Units: The Cross-Efficiency Evaluation -- 1.1 Introduction -- 1.2 Ranking Methods in DEA -- 1.3 The Cross-Efficiency Evaluation: The Standard Approach -- 1.4 The Choice of DEA Weights in Cross-Efficiency Evaluations -- 1.4.1 Ranking Ranges and Cross-Efficiency Intervals -- 1.4.2 Illustrative Example -- 1.5 The Aggregation of Cross-Efficiencies -- 1.5.1 Illustrative Example (Cont.) -- 1.6 Other Uses -- 1.6.1 Identification of Mavericks and All-Round Performers -- 1.6.2 Classification of DMUs and Benchmarking -- 1.6.3 Fixed Cost and Resource Allocation -- 1.7 Extensions -- 1.7.1 Cross-Efficiency Evaluation with Directional Distance Functions -- 1.7.2 Cross-Efficiency Evaluation with Multiplicative DEA Models -- 1.7.3 Cross-Efficiency Evaluation Under VRS -- 1.7.4 Fuzzy Cross-Efficiency Evaluation -- 1.7.5 Game Cross Efficiency -- 1.8 Conclusions -- References -- Chapter 2: Data Envelopment Analysis for Measuring Environmental Performance -- 2.1 Introduction -- 2.2 Environmental DEA Technology -- 2.3 Models for Measuring Environmental Performance -- 2.3.1 Environmental Efficiency Index -- 2.3.2 Environmental Productivity Index -- 2.3.3 Other Developments -- 2.4 Case Study -- 2.4.1 Data -- 2.4.2 Results and Discussions -- 2.4.2.1 EEI Analysis -- 2.4.2.2 EPI Analysis -- 2.5 Conclusion -- References -- Chapter 3: Input and Output Search in DEA: The Case of Financial Institutions -- 3.1 Introduction -- 3.2 Efficiency Modeling in Financial Institutions -- 3.3 A Case Study: American Banks -- 3.3.1 The Data Set: Three Inputs and Three Outputs -- 3.3.1.1 Labor -- 3.3.1.2 Physical Capital -- 3.3.1.3 Deposits -- 3.3.1.4 Interest and Non-interest Income -- 3.3.1.5 Loans -- 3.3.2 DEA Specification Searches Using Multivariate Methods.

3.3.3 Results Visualization and Strategic Pattern Identification -- 3.3.4 Dissecting the Efficiency Score -- 3.4 Conclusions -- References -- Chapter 4: Multi-period Efficiency Measurement with Fuzzy Data and Weight Restrictions -- 4.1 Introduction -- 4.2 Crisp Network DEA with Weight Restrictions -- 4.3 Fuzzy Multi-period Efficiency with Weight Restrictions -- 4.4 Example -- 4.5 Conclusion -- References -- Chapter 5: Pitching DEA Against SFA in the Context of Chinese Domestic Versus Foreign Banks -- 5.1 Introduction -- 5.2 Conceptual Framework -- 5.2.1 Chinese Banking Sector -- 5.2.2 Modeling Performance to Estimate Bank Efficiency -- 5.2.3 Contextual Variables -- 5.3 Data and Method -- 5.3.1 Data -- 5.3.2 Data Envelopment Analysis (DEA) -- 5.3.3 Stochastic Frontier Analysis (SFA) -- 5.4 Results and Analysis -- 5.4.1 Testing for Scale Inefficiency Using DEA -- 5.4.2 Main DEA Results -- 5.4.2.1 Core Model (Single-Output BCC-O) -- 5.4.2.2 Extended Model (Two-Output BCC-O) -- Overall Potential Improvements Identified by DEA Using the Extended Model -- Assessing the Marginal Role of the Output Variables in DEA: Efficiency Contribution Measures (ECM) for the Extended Model -- 5.4.3 SFA Results -- 5.4.3.1 Core Model (Single-Output Translog Function) -- 5.4.3.2 Extended Model (Two-Output Translog Function) -- 5.4.4 Comparing DEA and SFA Results -- 5.5 Concluding Remarks -- References -- Chapter 6: Assessing Organizations´ Efficiency Adopting Complementary Perspectives: An Empirical Analysis Through Data Envelop... -- 6.1 Introduction -- 6.2 DEA and MDS Methodologies: A Brief Overview -- 6.2.1 The Data Envelopment Analysis Method -- 6.2.2 The Multidimensional Scaling Method -- 6.3 Data and Selection of Indicators -- 6.3.1 Our Sample -- 6.3.2 Inputs and Outputs Employed in the DEA Analysis -- 6.3.3 Indicators Included in the MDS Analysis.

6.4 Studying HEIs´ Efficiency by Means of Data Envelopment Analysis: Results -- 6.5 Combining DEA and MDS Methodologies: Results -- 6.5.1 Preliminary Insights -- 6.6 Results -- 6.7 Concluding Remarks -- Appendix: List of Universities Included in the Analysis and Their Acronyms -- References -- Chapter 7: Capital Stock and Performance of RandD Organizations: A Dynamic DEA-ANP Hybrid Approach -- 7.1 Introduction -- 7.2 Literature Review -- 7.2.1 Current Status of Taiwanese RandD Organizations -- 7.2.2 DEA Applications in RandD Organizations -- 7.3 Research Design -- 7.3.1 Three-Stage Value-Creation Process of RandD Organizations -- 7.3.2 Data Selection and Description -- 7.3.3 Dynamic Extension of Network Slack-Based Measure DEA Model -- 7.4 Results and Discussions -- 7.4.1 Performance Analysis in Value-Creation Process -- 7.4.2 The Relationship Between Capital Stock and RandD Organizations Performance -- 7.5 Conclusions -- References -- Chapter 8: Evaluating Returns to Scale and Convexity in DEA Via Bootstrap: A Case Study with Brazilian Port Terminals -- 8.1 Introduction -- 8.2 Efficiency Measurement and RTS Characterization -- 8.2.1 Measuring Efficiency Scores Under Different Orientations and Frontiers -- 8.2.2 Scaling or RTS Characterization -- 8.2.3 Orientation Impact on RTS Characterization -- 8.3 Estimation and Bootstrapping in DEA -- 8.3.1 Estimation -- 8.3.2 Bootstrapping Method -- 8.4 Case Study: Brazilian Port Terminals -- 8.5 Results -- 8.5.1 Initial Estimates -- 8.5.2 Preliminary Statistics Tests on Initial Estimates -- 8.5.2.1 Testing for Model Specification -- 8.5.2.2 Testing for Differences Between Container and Bulk Terminals -- 8.5.2.3 Testing for Relevant Inputs and Outputs -- 8.5.2.4 Testing for Outliers -- 8.5.3 Bootstrapped Efficiency Scores and Convexity Assumption -- 8.5.4 RTS Characterizations: CIs for SI and uo.

8.5.5 Discussion -- 8.6 Conclusions -- References -- 9: DEA and Cooperative Game Theory -- 9.1 Introduction -- 9.2 Cooperative Game Theory -- 9.2.1 Bargaining Problems -- 9.2.1.1 The Nash Solution -- 9.2.1.2 The Kalai-Smorodinsky Solution -- 9.2.2 Transferable Utility Games -- 9.2.2.1 The Core and Related Concepts -- 9.2.2.2 The Shapley Value -- 9.2.2.3 The Least Core and the Nucleolus -- 9.3 Nash Bargaining Approaches to DEA -- 9.4 TU Cooperative Game Approaches to DEA -- 9.5 Further Potential Applications -- 9.5.1 Nash Decomposition for Process Efficiency in Multistage Production Systems -- 9.5.2 DEA Production Games -- References -- Chapter 10: Measuring Bank Performance: From Static Black Box to Dynamic Network Models -- 10.1 Introduction -- 10.2 Selective Literature Review -- 10.2.1 Network DEA and Dynamic DEA -- 10.2.2 Bank Production and Risk -- 10.3 Preliminaries -- 10.3.1 Black-Box Technology -- 10.3.2 Network Technology with Bad Outputs -- 10.3.3 Dynamic Technology with Carryovers -- 10.3.4 Dynamic-Network Technology -- 10.4 DEA Implementation -- 10.5 A Choice of Variables and Regulatory Constraints -- 10.5.1 Variable Selection: An Example -- 10.5.2 Imposing Bank Regulatory Constraint -- 10.6 A Summary -- References -- Chapter 11: Evaluation and Decomposition of Energy and Environmental Productivity Change Using DEA -- 11.1 Introduction -- 11.2 Luenberger Productivity Indicator and Its Decomposition -- 11.3 DEA Model for Energy and Environmental Efficiency Measurement -- 11.4 Application to China´s Regional Energy and Environmental Productivity Change -- 11.4.1 Data and Variables -- 11.4.2 Results and Discussions -- 11.5 Conclusions -- References -- Chapter 12: Identifying the Global Reference Set in DEA: An Application to the Determination of Returns to Scale -- 12.1 Introduction -- Part I: On Identification of the Global Reference Set.

Part II: On Determination of the RTS -- 12.2 Background -- 12.2.1 Technology Set -- 12.2.2 The RAM Model -- 12.3 Identifying the Global Reference Set (GRS) -- 12.3.1 Definition of the GRS -- 12.3.2 Properties of the GRS -- 12.3.3 Identification of the GRS -- 12.3.4 Properties of the Proposed Approach -- 12.3.5 Numerical example -- 12.4 Determination of Returns to Scale (RTS) -- 12.4.1 Definition of RTS for an Inefficient DMU -- 12.4.2 Determination of RTS Via the BCC Model -- 12.4.3 Determination of RTS Via the CCR Model -- 12.4.4 Numerical Example -- 12.4.4.1 Determining RTS Statuses of the DMUs Using Algorithm I -- 12.4.4.2 Determining RTS Statuses of the DMUs Using Algorithm II -- 12.5 Empirical Application -- 12.5.1 Evaluation of Schools via the RAM Model -- 12.5.2 Determining RTS Statuses of the Efficient Schools -- 12.5.3 Determining RTS Statuses of the Inefficient Schools -- 12.6 Summary and Concluding Remarks -- References -- Chapter 13: Technometrics Study Using DEA on Hybrid Electric Vehicles (HEVs) -- 13.1 Introduction -- 13.2 Methodology -- 13.3 Research Model and Dataset -- 13.3.1 TFDEA Parameters -- 13.3.1.1 Input Variable -- 13.3.1.2 Output Variables -- 13.3.1.3 Categorical Parameter -- 13.3.2 Dataset -- 13.4 Analysis of the Technological Advancement Patterns -- 13.4.1 Two-Seaters and Compact Segments: ``Stagnated´´ -- 13.4.2 Midsize Segment: ``Flourishing´´ -- 13.4.3 Large Segment: ``Emerging´´ -- 13.4.4 SUV Segment: ``Forging Ahead´´ -- 13.4.5 Minivan Segment: ``Crossover´´ -- 13.4.6 Pickup Truck Segment: ``Steady´´ -- 13.5 Conclusion -- Appendix: 2013 State-of-the-Art Frontiers of Different HEV Segments -- References -- Chapter 14: A Radial Framework for Estimating the Efficiency and Returns to Scale of a Multi-component Production System in DEA -- 14.1 Introduction -- 14.2 Radial Performance Measurement for a Multi-component System.

14.2.1 Basic Model.

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