Stochastic Benchmarking

This book introduces readers to benchmarking techniques in the stochastic environment, primarily stochastic data envelopment analysis (DEA), and provides stochastic models in DEA for the possibility of variations in inputs and outputs. It focuses on the application of theories and interpretations of the mathematical programs, which are combined with economic and organizational thinking. The book's main purpose is to shed light on the advantages of the different methods in deterministic and stochastic environments and thoroughly prepare readers to properly use these methods in various cases. Simple examples, along with graphical illustrations and real-world applications in industry, are provided for a better understanding. The models introduced here can be easily used in both theoretical and real-world evaluations.

This revised edition introduces three key updates: A new section on "Stochastic Data Envelopment Analysis in the Presence of Undesirable Outputs," extending Shephard's (1970) weak disposability assumption to a stochastic environment. A section on "Stochastic Scale Elasticity in the Presence of Undesirable Outputs" with an application to the power sector, incorporating both undesirable outputs and data uncertainty. Additionally, a new chapter on "Managerial Ability in Deterministic and Stochastic Environments" presents a two-step procedure using data envelopment analysis and regression analysis to assess managerial ability in the presence of multiple variables.

This book is intended for graduate and PhD students, advanced consultants, and practitioners with an interest in quantitative performance evaluation.

Provides a comprehensive reference on quantitative performance evaluation Includes simple examples and real-life applications Extends important subjects in production theory into stochastic environment

Autorentext

Alireza Amirteimoori is a full professor of applied mathematics and operations at the Department of Industrial Engineering, Istinye University in Istanbul, Turkey. His research interests lie in the broad area of performance management with special emphasis on quantitative methods for performance measurement, and especially those based on the broad set of methods known as data envelopment analysis (DEA). His research findings have been published in major journals, including Applied Mathematics and Computation , Journal of the Operations Research Society , RAIRO - Operations Research , International Journal of Advanced Manufacturing Technology , International Journal of Production Economics , Optimization , Expert Systems with Applications , Central European Journal of Operational Research , International Journal of Mathematics in Operational Research , Decision Support Systems , and Journal of Global Optimization .

Biresh K. Sahoo is a professor of economics at Xavier Institute of Management, XIM University, Bhubaneswar, India, and an associate editor of Omega: The International Journal of Management Science . He specializes in applied production frontier analysis, and his research interests are in the areas of efficiency and productivity performance of firms and the economics of benchmarking.

Vincent Charles is an experienced researcher in the field of artificial intelligence and management science, currently with the School of Management, University of Bradford, UK. He has over two decades of experience in teaching, research, and consultancy, having served as a full professor and director of research for more than a decade. He has published over 130 research outputs and is the recipient of many international academic honors and awards.

Saber Mehdizadeh is affiliated with Istinye University, Istanbul, Türkiye. He received his PhD in applied mathematics and operations research from Islamic Azad University of Rasht, Iran.

Inhalt

Benchmarking.- An introduction to data envelopment analysis.- Probability Theory.- Stochastic data envelopment analysis.- Stochastic Network Data Envelopment Analysis .- Stochastic scale elasticity.- Managerial Ability in Deterministic and Stochastic Environments.