Classical data envelopment analysis (DEA) models use crisp data to measure the inputs and outputs of a given system. In cases such as manufacturing systems, production processes, service systems, etc., the inputs and outputs may be complex and difficult to measure with classical DEA models. Crisp input and output data are fundamentally indispensable in the conventional DEA models. If these models contain complex uncertain data, then they will become more important and practical for decision makers.Uncertainty in Data Envelopment Analysis introduces methods to investigate uncertain data in DEA models, providing a deeper look into two types of uncertain DEA methods, fuzzy DEA and belief degree-based uncertainty DEA, which are based on uncertain measures. These models aim to solve problems encountered by classical data analysis in cases where the inputs and outputs of systems and processes are volatile and complex, making measurement difficult. - Introduces methods to deal with uncertain data in DEA models, as a source of information and a reference book for researchers and engineers - Presents DEA models that can be used for evaluating the outputs of many reallife systems in social and engineering subjects - Provides fresh DEA models for efficiency evaluation from the perspective of imprecise data - Applies the fuzzy set and uncertainty theories to DEA to produce a new method of dealing with the empirical data
Practical Aspects of Electroorganic Synthesis presents educational insights into the practical aspects of electrosynthesis methods, providing a variety of examples and techniques. The book covers concepts referred to as "green chemistry" and "sustainable technology." Sections cover direct electrolysis, anodic oxidation, cathodic reduction, mechanistic studies (cyclic voltammetry), and how to set-up electrochemical experiments. Indirect electrolysis is also covered, including an exploration of catalysts and additives to take on modern electrochemical methods. Finally, the book explores the burgeoning new field of paired electrolysis, in which the ultimate green-synthesis applications are possible, with no wasted electrons and very few by-products. This book offers researchers a modern and authoritative resource that brings complete and up-to-date practical concepts of electrosynthesis methods and guides the audience on how to carry out a large number of experimental techniques. - Discusses complete and up-to-date practical concepts of electrosynthesis methods - Provides sound insights into the experimental approaches of electrosynthesis, covering new and novel synthesis techniques - Breaks down the fundamentals aspects of electrolysis into three digestible and logical sections
Classical data envelopment analysis (DEA) models use crisp data to measure the inputs and outputs of a given system. In cases such as manufacturing systems, production processes, service systems, etc., the inputs and outputs may be complex and difficult to measure with classical DEA models. Crisp input and output data are fundamentally indispensable in the conventional DEA models. If these models contain complex uncertain data, then they will become more important and practical for decision makers.Uncertainty in Data Envelopment Analysis introduces methods to investigate uncertain data in DEA models, providing a deeper look into two types of uncertain DEA methods, fuzzy DEA and belief degree-based uncertainty DEA, which are based on uncertain measures. These models aim to solve problems encountered by classical data analysis in cases where the inputs and outputs of systems and processes are volatile and complex, making measurement difficult. - Introduces methods to deal with uncertain data in DEA models, as a source of information and a reference book for researchers and engineers - Presents DEA models that can be used for evaluating the outputs of many reallife systems in social and engineering subjects - Provides fresh DEA models for efficiency evaluation from the perspective of imprecise data - Applies the fuzzy set and uncertainty theories to DEA to produce a new method of dealing with the empirical data
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