Analytical decision making and data envelopment analysis : advances and challenges / S.A. Edalatpanah, Farhad Hosseinzadeh Lotfi, Kristiaan Kerstens, Peter Wanke, editors

Livre

Edited by Springer - 2024

This book explores the intersection of data envelopment analysis (DEA) and various analytical decision-making methodologies. Featuring contributions from experts in the field from across the world, each chapter delves into different aspects of DEA and its applications in real-world scenarios. The book covers a wide range of topics, including integrating DEA with machine learning techniques, performance evaluation in diverse sectors like banking and civil engineering, and using DEA in managerial decision-making. It also examines data mining during the Covid-19 pandemic and the application of blockchain and IoT in supply chain management. The book offers a deep dive into the evolution of nonparametric frontier methods and the development of new optimization algorithms, addressing the complexities of modern analytical decision-making tools. A few chapters delve into futuristic topics like fuzzy sets and their extensions in decision-making and exploring e-learning platforms for education. This book is an invaluable resource for researchers, practitioners and students interested in the latest DEA advancements and practical applications in various fields. Its multidisciplinary approach makes it a useful addition to the libraries of those seeking to understand the complexities and potentials of modern analytical decision-making tools

Intro. Preface. Contents. Merging Data Envelopment Analysis and Structural Risk Minimization: Some Examples of Use of Multi-output Machine Learning Techniques on Real-World Data. 1 Introduction. 2 Background. 2.1 Measures of Technical Efficiency in Efficiency Analysis. 2.2 Data Envelopment Analysis-Based Machines. 3 Methodology. 4 Computational Experiences. 4.1 Schools in Madrid. 4.2 Spanish Regional Tax Offices. 5 Conclusions. References. A New Network Data Envelopment Analysis Model for Efficacy Evaluation of Decision-Making Units. 1 Introduction. 2 Background. 2.1 Series Networks. 2.2 Parallel Networks. 2.3 Mixed Networks Structures. 3 A New Network DEA Model. 4 A Real Case Study. 4.1 Determining the Efficiency Metrics of the Airline Industry. 4.2 Performance Evaluation. 5 Conclusion. References. Possibilistic Network DEA Approach for Performance Evaluation of Two-Stage Decision Making Units Under Uncertainty. 1 Introduction. 2 Two-Stage DEA Model. 3 Possibilistic Network DEA Model. 4 Real-Life Case Study of Investment Firms. 5 Concluding Remarks and Future Research Directions. References. Managerial Ability in Indian Life Insurance Companies: A Comparison Based on DEA and DEAGP. 1 Introduction. 2 Literature Review. 3 Research Methodology. 3.1 DEA Modeling for Efficiency Evaluation. 3.2 DEAGP Modeling for Efficiency Evaluation. 3.3 Second and Third Stage Estimation. 3.4 Managerial Ability Estimation. 4 Input and Output Variables and Data. 4.1 Description of the Input and Output Variables. 4.2 Data and Period of Analysis. 5 Results and Discussion. 5.1 Efficiency Estimates. 5.2 Influence of Environmental Variables on the Efficiency Estimates. 5.3 Estimates of Managerial Ability. 5.4 Clustering of Life Insurers Based on Managerial Ability Scores. 5.5 Relationship with Other Measures of Firm Performance. 6 Conclusion. Appendix. References. Efficiency Appraisal and Classification of Flexible Random Factors. 1 Introduction. 2 Flexible Measures and Stochastic DEA: A Brief Review. 2.1 Flexible DEA Models. 2.2 Chance-Constrained DEA. 3 Efficiency Analysis with Flexible Random Factors. 3.1 Oriented Flexible Chance-Constrained DEA Model. 4 Empirical Analysis. 5 Conclusions. References. Performance Evaluation of Indian Banking Financial Sector by Using DEA Approach. 1 Introduction. 2 DEA Framework. 2.1 Case Study in the Indian Banking Sector. 2.2 Result and Discussion. 3 Conclusion and Future Work. Appendix: The Input and Output Criteria Detailed Explained as Follows. References. Application of Data Envelopment Analysis in Decision Making of Civil Engineering Problems. 1 Introduction. 2 Selecting Appropriate FRP-To-Concrete Bond Strength Models. 2.1 Bond Strength Models. 2.2 Evaluation of Goodness-Of-Fit. 2.3 Data Envelopment Analysis -SBM.

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