Uncertainty In Data Envelopment Analysis Fuzzy And Belief Degree Based Uncertainties

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

This book is intended to present the milestones in the progression of uncertain Data envelopment analysis (DEA). Chapter 1 gives some basic introduction to uncertain theories, including probability theory, credibility theory, uncertainty theory and chance theory. Chapter 2 presents a comprehensive review and discussion of basic DEA models. The stochastic DEA is introduced in Chapter 3, in which the inputs and outputs are assumed to be random variables. To obtain the probability distribution of a random variable, a lot of samples are needed to apply the statistics inference approach. Chapter 4 and 5 provide two uncertain DEA methods to evaluate the DMUs with limited or insufficient statistical data, named fuzzy DEA and uncertain DEA. In order to evaluate the DMUs in which uncertainty and randomness appear simultaneously, the hybrid DEA based on chance theory is presented in Chapter 6.

Collection of selected, peer reviewed papers from the 2014 International Conference on Energy and Environmental Protection (ICEEP 2014), April 26-28, 2014, Xi'an, China. The 610 papers are grouped as follows: Chapter 1: Georesearch, Mineral Prospecting and Exploration, Chapter 2: Oil and Gas Research, Chapter 3: Forestry Research, Chapter 4: Chemical and Petroleum Engineering, Chapter 5: Mineral Processing Engineering, Chapter 6: Mining Engineering, Chapter 7: Agricultural Products Processing and Consumption, Chapter 8: Climate and Carbon Emission Research, Chapter 9: Energy Saving, Environmental Protection, Low-Carbon Concept, Chapter 10: Energy Consumption and Energy Strategy, Chapter 11: Energy Industry Development and Management, Chapter 12: International Energy, Resource Demand and Supply, Chapter 13: Environmental Protection and Economic Development, Chapter 14: Economy Research: Environmental Case, Chapter 15: Transportation, Urban and Regional Planning, Chapter 16: Information Technology, Computer Applications and Mathematical Modeling, Chapter 17: Management and Education

This book provides a self-contained, comprehensive and up-to-date presentation of uncertainty theory. The purpose is to equip the readers with an axiomatic approach to deal with uncertainty. For this new edition the entire text has been totally rewritten. The chapters on chance theory and uncertainty theory are completely new. Mathematicians, researchers, engineers, designers, and students will find this work a stimulating and useful reference.

Methods in Sustainability Science: Assessment, Prioritization, Improvement, Design and Optimization presents cutting edge, detailed methodologies needed to create sustainable growth in any field or industry, including life cycle assessments, building design, and energy systems. The book utilized a systematic structured approach to each of the methodologies described in an interdisciplinary way to ensure the methodologies are applicable in the real world, including case studies to demonstrate the methods. The chapters are written by a global team of authors in a variety of sustainability related fields. Methods in Sustainability Science: Assessment, Prioritization, Improvement, Design and Optimization will provide academics, researchers and practitioners in sustainability, especially environmental science and environmental engineering, with the most recent methodologies needed to maintain a sustainable future. It is also a necessary read for postgraduates in sustainability, as well as academics and researchers in energy and chemical engineering who need to ensure their industrial methodologies are sustainable. Provides a comprehensive overview of the most recent methodologies in sustainability assessment, prioritization, improvement, design and optimization Sections are organized in a systematic and logical way to clearly present the most recent methodologies for sustainability and the chapters utilize an interdisciplinary approach that covers all considerations of sustainability Includes detailed case studies demonstrating the efficacies of the described methods

This book presents the underlying theory, model development, and applications of network Data Envelopment Analysis (DEA) in a systematic way. The field of network DEA extends and complements conventional DEA by considering not only inputs and outputs when measuring system efficiency, but also the internal structure of the system being analyzed. By analyzing the efficiency of individual internal components, and more particularly by studying the effects of relationships among components which are modeled and implemented by means of various network structures, the “network DEA” approach is able to help identify and manage the specific components that contribute inefficiencies into the overall systems. This relatively new approach comprises an important analytical tool based on mathematical programming techniques, with valuable implications to production and operations management. The existing models for measuring the efficiency of systems of specific network structures are also discussed, and the relationships between the system and component efficiencies are explored. This book should be able to inspire new research and new applications based on the current state of the art. Performance evaluation is an important task in management, and is needed to (i) better understand the past accomplishments of an organization and (ii) plan for its future development. However, this task becomes rather challenging when multiple performance metrics are involved. DEA is a powerful tool to cope with such issues. For systems or operations composed of interrelated processes, managers need to know how the performances of the various processes evaluated and how they are aggregated to form the overall performance of the system. This book provides an advanced exposition on performance evaluation of systems with network structures. It explores the network nature of most production and operation systems, and explains why network analyses are necessary.

The intensity of global competition and ever-increasing economic uncertainties has led organizations to search for more efficient and effective ways to manage their business operations. Data envelopment analysis (DEA) has been widely used as a conceptually simple yet powerful tool for evaluating organizational productivity and performance. Fuzzy DEA (FDEA) is a promising extension of the conventional DEA proposed for dealing with imprecise and ambiguous data in performance measurement problems. This book is the first volume in the literature to present the state-of-the-art developments and applications of FDEA. It is designed for students, educators, researchers, consultants and practicing managers in business, industry, and government with a basic understanding of the DEA and fuzzy logic concepts.