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

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

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

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.

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 "e;Stochastic Data Envelopment Analysis in the Presence of Undesirable Outputs,"e; extending Shephard's (1970) weak disposability assumption to a stochastic environment. A section on "e;Stochastic Scale Elasticity in the Presence of Undesirable Outputs"e; with an application to the power sector, incorporating both undesirable outputs and data uncertainty. Additionally, a new chapter on "e;Managerial Ability in Deterministic and Stochastic Environments"e; 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.

This book provides a comprehensive and practical guide to Multi-Criteria Decision-Making (MCDM) methods, with a strong focus on their industrial applications for solving complex real-world challenges. It bridges the gap between theoretical foundations and practical implementation, offering clear, actionable insights for professionals and researchers in engineering, operations management, and business analytics. By combining rigorous methodology with practical relevance, the book equips readers to make informed decisions in environments where trade-offs between cost, quality, and sustainability are critical.Exploring advanced techniques such as AHP, TOPSIS, DEMATEL, and PROMETHEE, the book situates MCDM within industrial contexts like supply chain optimization, sustainability assessment, and risk management. It addresses the growing need for structured decision-making frameworks in data-driven settings and integrates Industry 4.0 technologies, including AI and IoT, to enable dynamic and adaptive strategies. Through real-world case studies from manufacturing, logistics, and energy sectors, it demonstrates how MCDM enhances transparency, resilience, and adaptability, making it an essential resource for academics, practitioners, and graduate students seeking to master decision analysis in modern industrial landscapes.

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

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

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

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.

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 "e;Stochastic Data Envelopment Analysis in the Presence of Undesirable Outputs,"e; extending Shephard's (1970) weak disposability assumption to a stochastic environment. A section on "e;Stochastic Scale Elasticity in the Presence of Undesirable Outputs"e; with an application to the power sector, incorporating both undesirable outputs and data uncertainty. Additionally, a new chapter on "e;Managerial Ability in Deterministic and Stochastic Environments"e; 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.

This book provides a comprehensive and practical guide to Multi-Criteria Decision-Making (MCDM) methods, with a strong focus on their industrial applications for solving complex real-world challenges. It bridges the gap between theoretical foundations and practical implementation, offering clear, actionable insights for professionals and researchers in engineering, operations management, and business analytics. By combining rigorous methodology with practical relevance, the book equips readers to make informed decisions in environments where trade-offs between cost, quality, and sustainability are critical.Exploring advanced techniques such as AHP, TOPSIS, DEMATEL, and PROMETHEE, the book situates MCDM within industrial contexts like supply chain optimization, sustainability assessment, and risk management. It addresses the growing need for structured decision-making frameworks in data-driven settings and integrates Industry 4.0 technologies, including AI and IoT, to enable dynamic and adaptive strategies. Through real-world case studies from manufacturing, logistics, and energy sectors, it demonstrates how MCDM enhances transparency, resilience, and adaptability, making it an essential resource for academics, practitioners, and graduate students seeking to master decision analysis in modern industrial landscapes.