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Advances in DEA Theory and Applications: With Extensions to Forecasting Models [Kõva köide]

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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.

List Of Contributors xx
About The Authors xxii
Preface xxxii
Part I: DEA Theory 1(116)
1 Radial DEA Models
3(8)
Kaoru Tone
1.1 Introduction
3(1)
1.2 Basic Data
3(1)
1.3 Input-Oriented CCR Model
4(2)
1.3.1 The CRS Model
6(1)
1.4 The Input-Oriented BCC Model
6(1)
1.4.1 The VRS Model
7(1)
1.5 The Output-Oriented Model
7(1)
1.6 Assurance Region Method
8(1)
1.7 The Assumptions Behind Radial Models
8(1)
1.8 A Sample Radial Model
8(2)
References
10(1)
2 Non-Radial DEA Models
11(9)
Kaoru Tone
2.1 Introduction
11(1)
2.2 The SBM Model
12(3)
2.2.1 Input-Oriented SBM
13(1)
2.2.2 Output-Oriented SBM
14(1)
2.2.3 Non-Oriented SBM
14(1)
2.3 An Example of an SBM Model
15(2)
2.4 The Dual Program of the SBM Model
17(1)
2.5 Extensions of the SBM Model
17(1)
2.5.1 Variable-Returns-to-Scale (VRS) Model
17(1)
2.5.2 Weighted-SBM Model
18(1)
2.6 Concluding Remarks
18(1)
References
19(1)
3 Directional Distance DEA Models
20(8)
Hirofumi Fukuyama
William. L. Weber
3.1 Introduction
20(1)
3.2 Directional Distance Model
20(3)
3.3 Variable-Returns-to-Scale DD Models
23(1)
3.4 Slacks-Based DD Model
23(2)
3.5 Choice of Directional Vectors
25(1)
References
26(2)
4 Super-Efficiency DEA Models
28(5)
Kaoru Tone
4.1 Introduction
28(1)
4.2 Radial Super-Efficiency Models
28(1)
4.2.1 Input-Oriented Radial Super-Efficiency Model
28(1)
4.2.2 Output-Oriented Radial Super-Efficiency Model
29(1)
4.2.3 Infeasibility Issues in the VRS Model
29(1)
4.3 Non-Radial Super-Efficiency Models
29(2)
4.3.1 Input-Oriented Non-Radial Super-Efficiency Model
30(1)
4.3.2 Output-Oriented Non-Radial Super-Efficiency Model
30(1)
4.3.3 Non-Oriented Non-Radial Super-Efficiency Model
30(1)
4.3.4 Variable-Returns-to-Scale Models
31(1)
4.4 An Example of a Super-Efficiency Model
31(1)
References
32(1)
5 Determining Returns to Scale in the VRS DEA Model
33(7)
Biresh K. Sahoo
Kaoru Tone
5.1 Introduction
33(1)
5.2 Technology Specification and Scale Elasticity
34(3)
5.2.1 Technology
34(1)
5.2.2 Measure of Scale Elasticity
35(1)
5.2.3 Scale Elasticity in DEA Models
35(2)
5.3 Summary
37(1)
References
37(3)
6 Malmquist Productivity Index Models
40(17)
Kaoru Tone
Miki Tsutsui
6.1 Introduction
40(3)
6.2 Radial Malmquist Model
43(2)
6.3 Non-Radial and Oriented Malmquist Model
45(2)
6.4 Non-Radial and Non-Oriented Malmquist Model
47(1)
6.5 Cumulative Malmquist Index (CMI)
48(1)
6.6 Adjusted Malmquist Index (AMI)
49(1)
6.7 Numerical Example
50(5)
6.7.1 DMU A
54(1)
6.7.2 DMU B
54(1)
6.7.3 DMU C
55(1)
6.7.4 DMU D
55(1)
6.8 Concluding Remarks
55(1)
References
55(2)
7 The Network DEA Model
57(7)
Kaoru Tone
Miki Tsutsui
7.1 Introduction
57(1)
7.2 Notation and Production Possibility Set
58(1)
7.3 Description of Network Structure
59(2)
7.3.1 Inputs and Outputs
59(1)
7.3.2 Links
60(1)
7.4 Objective Functions and Efficiencies
61(2)
7.4.1 Input-Oriented Case
61(1)
7.4.2 Output-Oriented Case
62(1)
7.4.3 Non-Oriented Case
62(1)
Reference
63(1)
8 The Dynamic DEA Model
64(10)
Kaoru Tone
Miki Tsutsui
8.1 Introduction
64(1)
8.2 Notation and Production Possibility Set
65(2)
8.3 Description of Dynamic Structure
67(2)
8.3.1 Inputs and Outputs
67(1)
8.3.2 Carry-Overs
67(2)
8.4 Objective Functions and Efficiencies
69(2)
8.4.1 Input-Oriented Case
69(1)
8.4.2 Output-Oriented Case
70(1)
8.4.3 Non-Oriented Case
71(1)
8.5 Dynamic Malmquist Index
71(2)
8.5.1 Dynamic Catch-up Index
72(1)
8.5.2 Dynamic Frontier Shift Effect
72(1)
8.5.3 Dynamic Malmquist Index
72(1)
8.5.4 Dynamic Cumulative Malmquist Index
72(1)
8.5.5 Dynamic Adjusted Malmquist Index
73(1)
References
73(1)
9 The Dynamic Network DEA Model
74(11)
Kaoru Tone
Miki Tsutsui
9.1 Introduction
74(1)
9.2 Notation and Production Possibility Set
75(2)
9.2.1 Notation
75(2)
9.3 Description of Dynamic Network Structure
77(3)
9.3.1 Inputs and Outputs
77(1)
9.3.2 Links
77(1)
9.3.3 Carry-Overs
78(2)
9.4 Objective Function and Efficiencies
80(2)
9.4.1 Overall Efficiency
80(1)
9.4.2 Period and Divisional Efficiencies
81(1)
9.5 Dynamic Divisional Malmquist Index
82(2)
9.5.1 Dynamic Divisional Catch-up Index
82(1)
9.5.2 Dynamic Divisional Frontier Shift Effect
82(1)
9.5.3 Dynamic Divisional Malmquist Index
82(1)
9.5.4 Dynamic Divisional Cumulative Malmquist Index
83(1)
9.5.5 Dynamic Divisional Adjusted Malmquist Index
83(1)
9.5.6 Overall Dynamic Malmquist Index
83(1)
References
84(1)
10 Stochastic DEA: The Regression-Based Approach
85(15)
Andrew L. Johnson
10.1 Introduction
85(2)
10.2 Review of Literature on Stochastic DEA
87(9)
10.2.1 Random Sampling
88(1)
10.2.2 Imprecise Measurement of Data
88(2)
10.2.3 Uncertainty in the Membership of Observations
90(1)
10.2.4 Random Production Possibility Sets
91(2)
10.2.5 Random Noise
93(3)
10.3 Conclusions
96(1)
References
96(4)
11 A Comparative Study of AHP and DEA
100(7)
Kaoru Tone
11.1 Introduction
100(1)
11.2 A Glimpse of Data Envelopment Analysis
100(2)
11.3 Benefit/Cost Analysis by Analytic Hierarchy Process
102(2)
11.3.1 Three-Level Perfect Graph Case
102(1)
11.3.2 General Cases
103(1)
11.4 Efficiencies in AHP and DEA
104(1)
11.4.1 Input x and Output y
104(1)
11.4.2 Weights
104(1)
11.4.3 Efficiency
104(1)
11.4.4 Several Propositions
105(1)
11.5 Concluding Remarks
105(1)
References
106(1)
12 A Computational Method for Solving DEA Problems with Infinitely Many DMUs
107(10)
Abraham Charnes
Kaoru Tone
12.1 Introduction
107(1)
12.2 Problem
108(1)
12.3 Outline of the Method
109(1)
12.4 Details of the Method When Z is One-Dimensional
110(3)
12.4.1 Initial Discretization and Subdivision Parameter
110(1)
12.4.2 Solving (Dh)
110(1)
12.4.3 Deletion/Subdivision Rules
111(1)
12.4.4 Solving the New LP
112(1)
12.4.5 Convergence Check
112(1)
12.5 General Case
113(2)
12.5.1 Initial Discretization
113(1)
12.5.2 Deletion and Subdivision (Bisection) Rules
113(2)
12.5.3 Solving New LPs and Checking Convergence
115(1)
12.6 Concluding Remarks (by Tone)
115(1)
Appendix 12.A: Proof of Theorem 12.1
115(1)
Appendix 12.B: Proof of Theorem 12.2
116(1)
Reference
116(1)
Part II: DEA Applications (Past-Present Scenario) 117(214)
13 Examining the Productive Performance of Life Insurance Corporation of India
119(22)
Kaoru Tone
Biresh K. Sahoo
13.1 Introduction
119(2)
13.2 Nonparametric Approach to Measuring Scale Elasticity
121(7)
13.2.1 Technology and Returns to Scale
122(1)
13.2.2 Qualitative Information on Returns to Scale
123(1)
13.2.3 Quantitative Information on Returns to Scale
124(2)
13.2.4 An Alternative Measure of Scale Elasticity
126(2)
13.3 The Dataset for LIC Operations
128(2)
13.4 Results and Discussion
130(6)
13.4.1 Production-Based Analysis
132(1)
13.4.2 Cost-Based Analysis
133(1)
13.4.3 Returns-to-Scale Issue
133(2)
13.4.4 Sensitivity Analysis
135(1)
13.5 Concluding Remarks
136(1)
References
136(5)
14 An Account of DEA-Based Contributions in the Banking Sector
141(31)
Jamal Ouenniche
Skarleth Carrales
Kaoru Tone
Hirofumi Fukuyama
14.1 Introduction
141(1)
14.2 Performance Evaluation of Banks: A Detailed Account
142(12)
14.3 Current State of the Art Summarized
154(9)
14.4 Conclusion
163(6)
References
169(3)
15 DEA in the Healthcare Sector
172(20)
Hiroyuki Kawaguchi
Kaoru Tone
Miki Tsutsui
15.1 Introduction
172(2)
15.2 Method and Data
174(10)
15.2.1 Previous Literature
174(2)
15.2.2 Formulas for Efficiency Estimation by DN DEA Model
176(3)
15.2.3 Formulas for Malmquist Index by DN DEA Model
179(1)
15.2.4 Empirical Data
179(5)
15.3 Results
184(4)
15.3.1 Estimated Efficiency Scores
184(1)
15.3.2 Estimated Malmquist Index Scores
184(4)
15.4 Discussion
188(1)
15.4.1 Estimation Results and Policy Implications
188(1)
15.4.2 Further Research Questions
189(1)
Acknowledgements
189(1)
References
190(2)
16 DEA in the Transport Sector
192(24)
Ming-Miin Yu
Li-Hsueh Chen
16.1 Introduction
192(2)
16.2 DNDEA in Transport
194(6)
16.2.1 The Production Technology for the Production Process
196(1)
16.2.2 The Production Technology for the Service Process
197(3)
16.3 Extension
200(7)
16.3.1 The Production Technology for FIB Activity
202(1)
16.3.2 The Production Technology for UB Activity
203(1)
16.3.3 The Production Technology for the Service Process
204(3)
16.4 Application
207(5)
16.4.1 Input and Output Variables
207(2)
16.4.2 Empirical Results
209(3)
16.5 Conclusions
212(1)
References
212(4)
17 Dynamic Network Efficiency of Japanese Prefectures
216(15)
Hirofumi Fukuyama
Atsuo Hashimoto
Kaoru Tone
William L. Weber
17.1 Introduction
216(1)
17.2 Multiperiod Dynamic Multiprocess Network
217(4)
17.3 Efficiency/Productivity Measurement
221(1)
17.4 Empirical Application
222(7)
17.4.1 Prefectural Production and Data
222(3)
17.4.2 Efficiency Estimates and Their Determinants
225(4)
17.5 Conclusions
229(1)
References
229(2)
18 A Quantitative Analysis of Market Utilization in Electric Power Companies
231(19)
Miki Tsutsui
Kaoru Tone
18.1 Introduction
231(1)
18.2 The Functions of the Trading Division
232(3)
18.3 Measuring the Effect of Energy Trading
235(7)
18.3.1 Definition of Transaction Volumes and Prices
235(2)
18.3.2 Constraints on Internal Transactions
237(1)
18.3.3 Profit Maximization
238(2)
18.3.4 Exogenous Variables
240(2)
18.4 DEA Calculation
242(1)
18.5 Empirical Results
243(5)
18.5.1 Results of Profit Maximization
243(3)
18.5.2 Results of DEA
246(2)
18.6 Concluding Remarks
248(1)
References
249(1)
19 DEA in Resource Allocation
250(21)
Ming-Miin Yu
Li-Hsueh Chen
19.1 Introduction
250(2)
19.2 Centralized DEA in Resource Allocation
252(9)
19.2.1 Minor Adjustment
253(3)
19.2.2 Moderate Adjustment
256(3)
19.2.3 Major Adjustment '
259(2)
19.3 Applications of Centralized DEA in Resource Allocation
261(4)
19.3.1 Human Resource Rightsizing in Airports
261(3)
19.3.2 Resource Allocation in Container Terminal Operations
264(1)
19.4 Extension
265(3)
19.4.1 Phase I
266(1)
19.4.2 Phase II
267(1)
19.5 Conclusions
268(1)
References
268(3)
20 How to Deal with Non-convex Frontiers in Data Envelopment Analysis
271(29)
Kaoru Tone
Miki Tsutsui
20.1 Introduction
271(2)
20.2 Global Formulation
273(3)
20.2.1 Notation and Basic Tools
273(1)
20.2.2 Uniqueness of Slacks
274(1)
20.2.3 Decomposition of CRS Slacks
275(1)
20.2.4 Scale-Independent Dataset
275(1)
20.3 In-cluster Issue: Scale-and Cluster-Adjusted DEA Score
276(5)
20.3.1 Clusters
276(1)
20.3.2 Solving the CRS Model in the Same Cluster
277(1)
20.3.3 Scale-and Cluster-Adjusted Score
278(1)
20.3.4 Summary of the SAS Computation
279(1)
20.3.5 Global Characterization of SAS-Projected DMUs
280(1)
20.4 An Illustrative Example
281(3)
20.5 The Radial-Model Case
284(3)
20.5.1 Decomposition of CCR Slacks
285(1)
20.5.2 Scale-Adjusted Input and Output
285(1)
20.5.3 Solving the CCR Model in the Same Cluster
286(1)
20.5.4 Scale-and Cluster-Adjusted Score
286(1)
20.6 Scale-Dependent Dataset and Scale Elasticity
287(2)
20.6.1 Scale-Dependent Dataset
287(1)
20.6.2 Scale Elasticity
288(1)
20.7 Application to a Dataset Concerning Japanese National Universities
289(5)
20.7.1 Data
289(2)
20.7.2 Adjusted Score (SAS)
291(1)
20.7.3 Scale Elasticity
291(3)
20.8 Conclusions
294(1)
Appendix 20.A: Clustering Using Returns to Scale and Scale Efficiency
295(1)
Appendix 20.B: Proofs of Propositions
295(3)
References
298(2)
21 Using DEA to Analyze the Efficiency of Welfare Offices and Influencing Factors: The Case of Japan's Municipal Public Assistance Programs
300(15)
Masayoshi Hayashi
21.1 Introduction
300(1)
21.2 Institutional Background, DEA, and Efficiency Scores
301(3)
21.2.1 DMUs
302(1)
21.2.2 Outputs and Inputs
302(1)
21.2.3 Efficiency Scores
303(1)
21.3 External Effects on Efficiency
304(5)
21.3.1 Adjustments for Environmental/External Factors
304(1)
21.3.2 The Second-Stage Regression Model
305(1)
21.3.3 Econometric Issues
306(1)
21.3.4 Estimation Results
307(2)
21.4 Quantile Regression Analysis
309(3)
21.4.1 Different Responses along the Quantiles of Efficiency
309(1)
21.4.2 Results
310(2)
21.5 Concluding Remarks
312(1)
Acknowledgements
312(1)
References
312(3)
22 DEA as a Kaizen Tool: SBM Variations Revisited
315(16)
Kaoru Tone
22.1 Introduction
315(1)
22.2 The SBM-Min Model
316(2)
22.2.1 Production Possibility Set
317(1)
22.2.2 Non-Oriented SBM
317(1)
22.3 The SBM-Max Model
318(3)
22.4 Observations
321(2)
22.4.1 Distance and Choice of the Set Rh
321(1)
22.4.2 The Role of Programs (22.10) and (22.16)
321(1)
22.4.3 Computational Amount
322(1)
22.4.4 Consistency with the Super-Efficiency SBM Measure
322(1)
22.4.5 Addition of Weights to Input and Output Slacks
323(1)
22.5 Numerical Examples
323(7)
22.5.1 An Illustrative Example
323(3)
22.5.2 Japanese Municipal Hospitals
326(4)
22.6 Conclusions
330(1)
References
330(1)
Part III: DEA For Forecasting And Decision-Making (Past-Present-Future Scenario) 331(198)
23 Corporate Failure Analysis Using SBM
333(24)
Joseph C. Paradi
Xiaopeng Yang
Kaoru Tone
23.1 Introduction
333(1)
23.2 Literature Review
334(6)
23.2.1 Beaver's Univariate Model
335(1)
23.2.2 Altman's Multivariate Model
336(1)
23.2.3 Subsequent Models
337(3)
23.3 Methodology
340(3)
23.3.1 Slacks-Based Measure
340(2)
23.3.2 Model Development
342(1)
23.4 Application to Bankruptcy Prediction
343(9)
23.4.1 Data Acquisition
344(1)
23.4.2 Analysis of Results
345(7)
23.5 Conclusions
352(2)
References
354(3)
24 Ranking of Bankruptcy Prediction Models under Multiple Criteria
357(24)
Jamal Ouenniche
Mohammad M. Mousavi
Bing Xu
Kaoru Tone
24.1 Introduction
357(2)
24.2 An Overview of Bankruptcy Prediction Models
359(7)
24.2.1 Discriminant Analysis Models
360(1)
24.2.2 Probability Models
360(3)
24.2.3 Survival Analysis Models
363(1)
24.2.4 Stochastic Models
364(2)
24.3 A Slacks-Based Super-Efficiency Framework for Assessing Bankruptcy Prediction Models
366(6)
24.3.1 What Are the Units To Be Assessed, or DMUs?
366(2)
24.3.2 What Are the Inputs and the Outputs?
368(1)
24.3.3 What Is the Appropriate DEA Formulation To Solve?
368(4)
24.4 Empirical Results from Super-Efficiency DEA
372(4)
24.5 Conclusion
376(1)
References
377(4)
25 DEA in Performance Evaluation of Crude Oil Prediction Models
381(23)
Jamal Ouenniche
Bing Xu
Kaoru Tone
25.1 Introduction
381(4)
25.2 An Overview of Crude Oil Prices and Their Volatilities
385(3)
25.3 Assessment of Prediction Models of Crude Oil Price Volatility
388(13)
25.3.1 Forecasting Models of Crude Oil Volatility-DMUs
389(1)
25.3.2 Performance Criteria and Their Measures: Inputs and Outputs
390(1)
25.3.3 Slacks-Based Super-Efficiency Analysis
390(6)
25.3.4 Empirical Results from Slacks-Based Super-Efficiency DEA
396(5)
25.4 Conclusion
401(1)
References
402(2)
26 Predictive Efficiency Analysis: A Study of US Hospitals
404(15)
Andrew L. Johnson
Chia-Yen Lee
26.1 Introduction
404(1)
26.2 Modeling of Predictive Efficiency
405(3)
26.3 Study of US Hospitals
408(4)
26.4 Forecasting, Benchmarking, and Frontier Shifting
412(4)
26.4.1 Effect of Forecast on Effectiveness
412(1)
26.4.2 Benchmarks
412(2)
26.4.3 Technical Progress
414(2)
26.5 Conclusions
416(1)
References
417(2)
27 Efficiency Prediction Using Fuzzy Piecewise Autoregression
419(24)
Ming-Miin Yu
Bo Hsiao
27.1 Introduction
419(1)
27.2 Efficiency Prediction
420(3)
27.3 Modeling and Formulation
423(10)
27.3.1 Notation
423(1)
27.3.2 Phase I: Efficiency Evaluation
424(2)
27.3.3 Phase II: CIE
426(1)
27.3.4 Phase III: Fuzzy Piecewise Regression
426(5)
27.3.5 Phase IV: Validating and Forecasting
431(2)
27.4 Illustrating the Application
433(5)
27.4.1 Efficiency Evaluations
433(3)
27.4.2 Validation
436(1)
27.4.3 Forecasting
437(1)
27.5 Discussion
438(2)
27.6 Conclusion
440(1)
References
441(2)
28 Time Series Benchmarking Analysis for New Product Scheduling: Who Are the Competitors and How Fast Are They Moving Forward?
443(16)
Dong-Joon Lim
Timothy R. Anderson
28.1 Introduction
443(2)
28.2 Methodology
445(4)
28.2.1 Preliminaries
445(1)
28.2.2 Conceptual Framework
446(1)
28.2.3 Formulation
447(2)
28.3 Application: Commercial Airplane Development
449(5)
28.3.1 Research Framework
449(1)
28.3.2 Analysis of the Current (2007) State of the Art
449(2)
28.3.3 Risk Analysis
451(2)
28.3.4 Proof of Concept
453(1)
28.4 Conclusion and Matters for Future Work
454(1)
References
455(4)
29 DEA Score Confidence Intervals with Past-Present and Past-Present-Future-Based Resampling
459(21)
Kaoru Tone
Jamal Ouenniche
29.1 Introduction
459(2)
29.2 Proposed Methodology
461(4)
29.2.1 Past-Present-Based Framework
461(4)
29.2.2 Past-Present-Future Time-Based Framework
465(1)
29.3 An Application to Healthcare
465(11)
29.3.1 Illustration of the Past-Present Framework
466(9)
29.3.2 Illustration of the Past-Present-Future Framework
475(1)
29.4 Conclusion
476(2)
References
478(2)
30 DEA Models Incorporating Uncertain Future Performance
480(36)
Tsung-Sheng Chang
Kaoru Tone
Chen-Hui Wu
30.1 Introduction
480(2)
30.2 Generalized Dynamic Evaluation Structures
482(2)
30.3 Future Performance Forecasts
484(3)
30.4 Generalized Dynamic DEA Models
487(8)
30.4.1 Production Possibility Sets
488(1)
30.4.2 DEA Models Incorporating Uncertain Future Performance
489(6)
30.5 Empirical Study
495(18)
30.5.1 Data Analysis
497(3)
30.5.2 Analysis of Empirical Results
500(13)
30.6 Conclusions
513(1)
References
514(2)
31 Site Selection for the Next-Generation Supercomputing Center of Japan
516(13)
Kaoru Tone
31.1 Introduction
516(3)
31.2 Hierarchical Structure and Group Decision by AHP
519(2)
31.2.1 Hierarchical Structure
519(1)
31.2.2 Evaluation of Candidate Sites with Respect to Criteria, and Importance of Criteria
520(1)
31.2.3 Evaluation by Average Weights
520(1)
31.3 DEA Assurance Region Approach
521(1)
31.3.1 Use of Variable Weights
521(1)
31.3.2 Evaluation of the "Positives" of Each Site
521(1)
31.3.3 Evaluation of the "Negatives" of Each Site
522(1)
31.4 Application to the Site Selection Problem
522(5)
31.4.1 Preliminary Selection
523(1)
31.4.2 Final Selection
523(4)
31.5 Decision and Conclusion
527(1)
References
527(2)
Appendix A: DEA-Solver-Pro 529(6)
Index 535
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.