Data Envelopment Analysis A Handbook of Models and Methods
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
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Series Editors Camille C Price Stephen F. Austin State University, TX, USA Associate Series Editor Joe Zhu Worcester Polytechnic Institute, MA, USA Founding Series Editor Frederick S. Hillier Stanford University, CA, USA
More information about this series at http://www.springer.com/series/6161
Joe Zhu Editor
Data Envelopment Analysis A Handbook of Models and Methods
Editor Joe Zhu International Center for Auditing and Evaluation Nanjing Audit University Nanjing, P.R. China School of Business Worcester Polytechnic Institute Worcester, MA 01545 USA
ISSN 0884-8289 ISSN 2214-7934 (electronic) International Series in Operations Research & Management Science ISBN 978-1-4899-7552-2 ISBN 978-1-4899-7553-9 (eBook) DOI 10.1007/978-1-4899-7553-9 Library of Congress Control Number: 2015931384 Springer Boston New York Dordrecht London © Springer Science+Business Media New York 2015 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)
Preface
This handbook complements the second edition of the Handbook on Data Envelopment Analysis (Cooper et al. 2011, Springer). Data envelopment analysis (DEA) is a “data-oriented” approach for evaluating the performance of a set of entities called Decision Making Units (DMUs) whose performance is categorized by multiple metrics. These performance metrics are indicated as inputs and outputs under DEA. Although DEA has a strong link to production theory in economics, the tool is also used for benchmarking in operations management where a set of measures is selected to benchmark the performance of manufacturing and service operations. In the circumstance of benchmarking, the efficient DMUs, as defined by DEA, may not necessarily form a “production frontier,” but rather lead to a “best-practice frontier” (Cook et al. 2014). Since the publication of the second edition of Handbook on Data Envelopment Analysis, there has been a significant amount of research on
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