Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis
In a relatively short period of time, Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating performance. It has been successfully applied to a whole variety of problems in many different conte
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Modeling Data Irregularities and Structural Complexities in Data Envelopment Analysis
Edited by
Joe Zhu Worcester Polytechnic Institute, U.S.A.
Wade D. Cook York University, Canada
Joe Zhu
Wade D. Cook
Worcester Polytechnic Institute Worcester, MA, USA
York University Toronto, ON, Canada
Library of Congress Control Number: 2007925039 ISBN 978-0-387-71606-0
e-ISBN 978-0-387-71607-7
Printed on acid-free paper. © 2007 by Springer Science+Business Media, LLC All rights reserved. This work may not be translated or copied in whole or in part without the written permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York, NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now know or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks and similar terms, even if the are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights. 9 8 7 6 5 4 3 2 1 springer.com
To Alec and Marsha Rose
CONTENTS
1
Data Irregularities and Structural Complexities in DEA
1
Wade D. Cook and Joe Zhu
2
Rank Order Data in DEA
13
Wade D. Cook and Joe Zhu
3
Interval and Ordinal Data
35
Yao Chen and Joe Zhu
4
Variables with Negative Values in DEA
63
Jesús T. Pastor and José L. Ruiz
5
Non-Discretionary Inputs
85
John Ruggiero
6
DEA with Undesirable Factors
103
Zhongsheng Hua and Yiwen Bian
7
European Nitrate Pollution Regulation and French Pig Farms’ Performance
123
Isabelle Piot-Lepetit and Monique Le Moing
8
PCA-DEA
139
Nicole Adler and Boaz Golany
9
Mining Nonparametric Frontiers José H. Dulá
155
viii
10
Contents
DEA Presented Graphically Using Multi-Dimensional Scaling
171
Nicole Adler, Adi Raveh and Ekaterina Yazhemsky
11
DEA Models for Supply Chain or Multi-Stage Structure
189
Wade D. Cook, Liang Liang, Feng Yang, and Joe Zhu
12
Network DEA
209
Rolf Färe, Shawna Grosskopf and Gerald Whittaker
13
Context-Dependent Data Envelopment Analysis and its Use
241
Hiroshi Morita and Joe Zhu
14
Flexible Measures-Classifying Inputs and Outputs
261
Wade D. Cook and Joe Zhu
15
Integer DEA Models
271
Sebastián Lozano and Gabriel Villa
16
Data Envelopment Analysis with Missing Data
291
Chiang Kao and Shiang-Tai Liu
17
Preparing Your Data for DEA
305
Joe Sarkis
About the Authors
321
Index
331
Chapter 1 DATA IRREGULARITIES AND STRUCTURAL COMPLEXITIES IN DEA Wade D. Cook1 and Joe Zhu2 1
Schulich School of Business, York University, Toronto, Ontario, Canada, M3J 1P3, [email protected] 2
Department of Management, Worcester Polytechnic Institute, Worcester, MA 01609, [email protected]
Abstract:
Over the recent years, we have seen a notable increase in interest in data envelopment analysis (DEA) techniques and applications. Basic a
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