Handbook Of Operations Analytics Using Data Envelopment Analysis
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| Author | : Shiuh-Nan Hwang |
| Publisher | : Springer |
| Total Pages | : 511 |
| Release | : 2016-07-01 |
| Genre | : Business & Economics |
| ISBN | : 1489977058 |
This handbook focuses on Data Envelopment Analysis (DEA) applications in operations analytics which are fundamental tools and techniques for improving operation functions and attaining long-term competitiveness. In fact, the handbook demonstrates that DEA can be viewed as Data Envelopment Analytics. Chapters include a review of cross-efficiency evaluation; a case study on measuring the environmental performance of OECS countries; how to select a set of performance metrics in DEA with an application to American banks; a relational network model to take the operations of individual periods into account in measuring efficiencies; how the efficient frontier methods DEA and stochastic frontier analysis (SFA) can be used synergistically; and how to integrate DEA and multidimensional scaling. In other chapters, authors construct a dynamic three-stage network DEA model; a bootstrapping based methodology to evaluate returns to scale and convexity assumptions in DEA; hybridizing DEA and cooperative games; using DEA to represent the production technology and directional distance functions to measure band performance; an input-specific Luenberger energy and environmental productivity indicator; and the issue of reference set by differentiating between the uniquely found reference set and the unary and maximal types of the reference set. Finally, additional chapters evaluate and compare the technological advancement observed in different hybrid electric vehicles (HEV) market segments over the past 15 years; radial measurement of efficiency for the production process possessing multi-components under different production technologies; issues around the use of accounting information in DEA; how to use DEA environmental assessment to establish corporate sustainability; a summary of research efforts on DEA environmental assessment applied to energy in the last 30 years; and an overview of DEA and how it can be utilized alone and with other techniques to investigate corporate environmental sustainability questions.
| Author | : Toshiyuki Sueyoshi |
| Publisher | : John Wiley & Sons |
| Total Pages | : 820 |
| Release | : 2018-01-29 |
| Genre | : Mathematics |
| ISBN | : 111897929X |
Introduces a bold, new model for energy industry pollution prevention and sustainable growth Balancing industrial pollution prevention with economic growth is one of the knottiest problems faced by industry today. This book introduces a novel approach to using data envelopment analysis (DEA) as a powerful tool for achieving that balance in the energy industries—the world’s largest producers of greenhouse gases. It describes a rigorous framework that integrates elements of the social sciences, corporate strategy, regional economics, energy economics, and environmental policy, and delivers a methodology and a set of strategies for promoting green innovation while solving key managerial challenges to greenhouse gas reduction and business growth. In writing this book the authors have drawn upon their pioneering work and considerable experience in the field to develop an unconventional, holistic approach to using DEA to assess key aspects of sustainability development. The book is divided into two sections, the first of which lays out a conventional framework of DEA as the basis for new research directions. In the second section, the authors delve into conceptual and methodological extensions of conventional DEA for solving problems of environmental assessment in all contemporary energy industry sectors. Introduces a powerful new approach to using DEA to achieve pollution prevention, sustainability, and business growth Covers the fundamentals of DEA, including theory, statistical models, and practical issues of conventional applications of DEA Explores new statistical modeling strategies and explores their economic and business implications Examines applications of DEA to environmental analysis across the complete range of energy industries, including coal, petroleum, shale gas, nuclear energy, renewables, and more Summarizes important studies and nearly 800 peer reviewed articles on energy, the environment, and sustainability Environmental Assessment on Energy and Sustainability by Data Envelopment Analysis is must-reading for researchers, academics, graduate students, and practitioners in the energy industries, as well as government officials and policymakers tasked with regulating the environmental impacts of industrial pollution.
| Author | : Thijs ten Raa |
| Publisher | : Springer Nature |
| Total Pages | : 766 |
| Release | : 2019-12-13 |
| Genre | : Business & Economics |
| ISBN | : 3030237273 |
This Handbook takes an econometric approach to the foundations of economic performance analysis. The focus is on the measurement of efficiency, productivity, growth and performance. These concepts are commonly measured residually and difficult to quantify in practice. In real-life applications, efficiency and productivity estimates are often quite sensitive to the models used in the performance assessment and the methodological approaches adopted by the analysis. The Palgrave Handbook of Performance Analysis discusses the two basic techniques of performance measurement – deterministic benchmarking and stochastic benchmarking – in detail, and addresses the statistical techniques that connect them. All chapters include applications and explore topics ranging from the output/input ratio to productivity indexes and national statistics.
| Author | : Sabri Boubaker |
| Publisher | : World Scientific |
| Total Pages | : 619 |
| Release | : 2024-07-26 |
| Genre | : Business & Economics |
| ISBN | : 1800615795 |
This Handbook presents recent trends and new developments in Data Envelopment Analysis (DEA) research within the realms of business, finance, and sustainability. Divided into three distinct parts, it encompasses 19 chapters that offer insightful studies conducted in diverse national environments and organizational settings.Part I focuses on DEA applications in business, including healthcare, supply chain management, and governmental organizations. Part II delves into the application of DEA in banking and finance, providing valuable insights into the efficiency and performance of financial institutions. Part III explores DEA's diverse applications in sustainability, addressing topics such as sustainability indicators, resource mobilization, food production, and farming. In essence, this Handbook stands as an invaluable reference work for stakeholders seeking to optimize organizational efficiency and performance across a variety of sectors.
| Author | : Zilla Sinuany-Stern |
| Publisher | : Springer Nature |
| Total Pages | : 529 |
| Release | : 2021-09-09 |
| Genre | : Business & Economics |
| ISBN | : 303074051X |
This handbook covers various areas of Higher Education (HE) in which operations research/management science (OR/MS) techniques are used. Key examples include: international comparisons, university rankings, and rating academic efficiency with Data Envelopment Analysis (DEA); formulating academic strategy with balanced scorecard; budgeting and planning with linear and quadratic models; student forecasting; E-learning evaluation; faculty evaluation with questionnaires and multivariate statistics; marketing for HE; analytic and educational simulation; academic information systems; technology transfer with systems analysis; and examination timetabling. Overviews, case studies and findings on advanced OR/MS applications in various functional areas of HE are included.
| Author | : Vincent Charles |
| Publisher | : Springer Nature |
| Total Pages | : 441 |
| Release | : 2020-05-23 |
| Genre | : Business & Economics |
| ISBN | : 3030433846 |
This book includes a spectrum of concepts, such as performance, productivity, operations research, econometrics, and data science, for the practically and theoretically important areas of ‘productivity analysis/data envelopment analysis’ and ‘data science/big data’. Data science is defined as the collection of scientific methods, processes, and systems dedicated to extracting knowledge or insights from data and it develops on concepts from various domains, containing mathematics and statistical methods, operations research, machine learning, computer programming, pattern recognition, and data visualisation, among others. Examples of data science techniques include linear and logistic regressions, decision trees, Naïve Bayesian classifier, principal component analysis, neural networks, predictive modelling, deep learning, text analysis, survival analysis, and so on, all of which allow using the data to make more intelligent decisions. On the other hand, it is without a doubt that nowadays the amount of data is exponentially increasing, and analysing large data sets has become a key basis of competition and innovation, underpinning new waves of productivity growth. This book aims to bring a fresh look onto the various ways that data science techniques could unleash value and drive productivity from these mountains of data. Researchers working in productivity analysis/data envelopment analysis will benefit from learning about the tools available in data science/big data that can be used in their current research analyses and endeavours. The data scientists, on the other hand, will also get benefit from learning about the plethora of applications available in productivity analysis/data envelopment analysis.
| Author | : William W. Cooper |
| Publisher | : Springer Science & Business Media |
| Total Pages | : 513 |
| Release | : 2011-08-23 |
| Genre | : Business & Economics |
| ISBN | : 1441961518 |
This handbook covers DEA topics that are extensively used and solidly based. The purpose of the handbook is to (1) describe and elucidate the state of the field and (2), where appropriate, extend the frontier of DEA research. It defines the state-of-the-art of DEA methodology and its uses. This handbook is intended to represent a milestone in the progression of DEA. Written by experts, who are generally major contributors to the topics to be covered, it includes a comprehensive review and discussion of basic DEA models, which, in the present issue extensions to the basic DEA methods, and a collection of DEA applications in the areas of banking, engineering, health care, and services. The handbook's chapters are organized into two categories: (i) basic DEA models, concepts, and their extensions, and (ii) DEA applications. First edition contributors have returned to update their work. The second edition includes updated versions of selected first edition chapters. New chapters have been added on: different approaches with no need for a priori choices of weights (called “multipliers) that reflect meaningful trade-offs, construction of static and dynamic DEA technologies, slacks-based model and its extensions, DEA models for DMUs that have internal structures network DEA that can be used for measuring supply chain operations, Selection of DEA applications in the service sector with a focus on building a conceptual framework, research design and interpreting results.
| Author | : Joe Zhu |
| Publisher | : Springer |
| Total Pages | : 472 |
| Release | : 2015-03-18 |
| Genre | : Business & Economics |
| ISBN | : 1489975535 |
This handbook represents a milestone in the progression of Data Envelopment Analysis (DEA). Written by experts who are often major contributors to DEA theory, it includes a collection of chapters that represent the current state-of-the-art in DEA research. Topics include distance functions and their value duals, cross-efficiency measures in DEA, integer DEA, weight restrictions and production trade-offs, facet analysis in DEA, scale elasticity, benchmarking and context-dependent DEA, fuzzy DEA, non-homogenous units, partial input-output relations, super efficiency, treatment of undesirable measures, translation invariance, stochastic nonparametric envelopment of data, and global frontier index. Focusing only on new models/approaches of DEA, the book includes contributions from Juan Aparicio, Mette Asmild, Yao Chen, Wade D. Cook, Juan Du, Rolf Färe, Julie Harrison, Raha Imanirad, Andrew Johnson, Chiang Kao, Abolfazl Keshvari, Timo Kuosmanen, Sungmook Lim, Wenbin Liu, Dimitri Margaritis, Reza Kazemi Matin, Ole B. Olesen, Jesus T. Pastor, Niels Chr. Petersen, Victor V. Podinovski, Paul Rouse, Antti Saastamoinen, Biresh K. Sahoo, Kaoru Tone, and Zhongbao Zhou.
| Author | : Croes, Robertico |
| Publisher | : Edward Elgar Publishing |
| Total Pages | : 455 |
| Release | : 2022-09-06 |
| Genre | : Business & Economics |
| ISBN | : 1800378769 |
This Modern Guide captures the evolution of foundational tenets, theories, frameworks and models that buttressed tourism economics into an evolving discipline, shining light on both new and old approaches. It systematically examines current and future trends and issues related to new economic perspectives, consolidating the notion of tourism economics as a discipline.
| Author | : Usman Qamar |
| Publisher | : Springer Nature |
| Total Pages | : 207 |
| Release | : 2020-06-08 |
| Genre | : Computers |
| ISBN | : 9811561338 |
This book comprehensively covers the topic of data science. Data science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. This book synthesizes both fundamental and advanced topics of a research area that has now reached maturity. The chapters of this book are organized into three sections: The first section is an introduction to data science. Starting from the basic concepts, the book will highlight the types of data, its use, its importance and issues that are normally faced in data analytics. Followed by discussion on wide range of applications of data science and widely used techniques in data science. The second section is devoted to the tools and techniques of data science. It consists of data pre-processing, feature selection, classification and clustering concepts as well as an introduction to text mining and opining mining. And finally, the third section of the book focuses on two programming languages commonly used for data science projects i.e. Python and R programming language. Although this book primarily serves as a textbook, it will also appeal to industrial practitioners and researchers due to its focus on applications and references. The book is suitable for both undergraduate and postgraduate students as well as those carrying out research in data science. It can be used as a textbook for undergraduate students in computer science, engineering and mathematics. It can also be accessible to undergraduate students from other areas with the adequate background. The more advanced chapters can be used by postgraduate researchers intending to gather a deeper theoretical understanding.
