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Lawrence M. Seiford
University of Michigan
95Publications
42H-index
16.2kCitations
Publications 95
Newest
Published on Jan 1, 2019
Andrea Raith9
Estimated H-index: 9
(University of Auckland),
Paul Rouse15
Estimated H-index: 15
(University of Auckland),
Lawrence M. Seiford42
Estimated H-index: 42
(UM: University of Michigan)
Data Envelopment Analysis (DEA) is a non-parametric, optimisation-based benchmarking technique first introduced by Charnes et al. (European Journal of Operational Research, 2(6), pp. 429–444, 1978), later extended by Banker et al. (Management Science 30(9), pp. 1078–1092, 1984), with many variations of DEA models proposed since. DEA measures the production efficiency of a so-called Decision Making Unit (DMU) which consumes inputs to produce outputs. DEA is a particularly useful tool when there a...
Source Cite
Wade D. Cook49
Estimated H-index: 49
(York University),
Lawrence M. Seiford42
Estimated H-index: 42
(UM: University of Michigan),
Joe Zhu59
Estimated H-index: 59
(WPI: Worcester Polytechnic Institute)
7 Citations Source Cite
Published on Jan 1, 2011
Rajiv D. Banker66
Estimated H-index: 66
(TU: Temple University),
William W. Cooper70
Estimated H-index: 70
(University of Texas at Austin)
+ 1 AuthorsJoe Zhu59
Estimated H-index: 59
(WPI: Worcester Polytechnic Institute)
This chapter discusses returns to scale (RTS) in data envelopment analysis (DEA). The BCC and CCR models described in Chap. 1 of this handbook are treated in input-oriented forms, while the multiplicative model is treated in output-oriented form. (This distinction is not pertinent for the additive model, which simultaneously maximizes outputs and minimizes inputs in the sense of a vector optimization.) Quantitative estimates in the form of scale elasticities are treated in the context of multipl...
37 Citations Source Cite
Published on Jan 1, 2011
William W. Cooper70
Estimated H-index: 70
,
Lawrence M. Seiford42
Estimated H-index: 42
,
Joe Zhu59
Estimated H-index: 59
-Preface W.W. Cooper, L.M. Seiford, J. Zhu. -1. Data Envelopment Analysis: History, Models and Interpretations W.W. Cooper, L.M. Seiford, J. Zhu. -2. Returns to Scale in DEA: R.D. Banker, W.W. Cooper, L.M. Seiford, J. Zhu. -3. Sensitivity Analysis in DEA: W.W. Cooper, Shanling Li, L.M. Seiford, J. Zhu. -4. Incorporating Value Judgments in DEA: E. Thanassoulis, M.C. Portela, R. Allen. -5. Distance Functions with Applications to DEA R. Fare, S. Grosskopf, G. Whittaker. -6. Qualitative Data in DEA ...
999 Citations Source Cite
Published on Jan 1, 2011
William W. Cooper70
Estimated H-index: 70
(University of Texas at Austin),
Lawrence M. Seiford42
Estimated H-index: 42
(UM: University of Michigan),
Joe Zhu59
Estimated H-index: 59
(WPI: Worcester Polytechnic Institute)
In about 30 years, Data Envelopment Analysis (DEA) has grown into a powerful quantitative, analytical tool for measuring and evaluating the performance. DEA has been successfully applied to a host of many different types of entities engaged in a wide variety of activities in many contexts worldwide. This chapter discusses the basic DEA models and some of their extensions.
274 Citations Source Cite
Published on Jan 1, 2011
William W. Cooper70
Estimated H-index: 70
(University of Texas at Austin),
Shanling Li16
Estimated H-index: 16
(McGill University)
+ 1 AuthorsJoe Zhu59
Estimated H-index: 59
(WPI: Worcester Polytechnic Institute)
This chapter presents some of the recently developed analytical methods for studying the sensitivity of DEA results to variations in the data. The focus is on the stability of classification of DMUs (decision making units) into efficient and inefficient performers. Early work on this topic concentrated on developing algorithms for conducting such analyses after it was noted that standard approaches for conducting sensitivity analyses in linear programming could not be used in DEA. However, recen...
23 Citations Source Cite
Published on Jan 1, 2011
William W. Cooper70
Estimated H-index: 70
(University of Texas at Austin),
Honghui Deng10
Estimated H-index: 10
(UNR: University of Nevada, Reno)
+ 1 AuthorsJoe Zhu59
Estimated H-index: 59
(WPI: Worcester Polytechnic Institute)
Congestion is a term that is applicable in a variety of disciplines which range from medical science to traffic engineering. It has also many uses in practical everyday life. This brings with it a certain looseness in usage. We therefore expand (and refine) our discussion of congestion with reference to its use in economics where we have access to a precise meaning which we can develop in this chapter. This chapter covers the standard approaches used for treating congestion in data envelopment a...
5 Citations Source Cite
Published on Jan 1, 2009in European Journal of Operational Research 3.81
Wade D. Cook49
Estimated H-index: 49
(York University),
Lawrence M. Seiford42
Estimated H-index: 42
(UM: University of Michigan)
This paper provides a sketch of some of the major research thrusts in data envelopment analysis (DEA) over the three decades since the appearance of the seminal work of Charnes et al. (1978) [Charnes, A., Cooper, W.W., Rhodes, E.L., 1978. Measuring the efficiency of decision making units. European Journal of Operational Research 2, 429-444]. The focus herein is primarily on methodological developments, and in no manner does the paper address the many excellent applications that have appeared dur...
809 Citations Source Cite
Published on Sep 28, 2007in Journal of Productivity Analysis 1.60
William W. Cooper70
Estimated H-index: 70
(University of Texas at Austin),
Lawrence M. Seiford42
Estimated H-index: 42
(UM: University of Michigan)
+ 1 AuthorsJoe Zhu59
Estimated H-index: 59
(WPI: Worcester Polytechnic Institute)
This paper covers some of the past accomplishments of DEA (Data Envelopment Analysis) and some of its future prospects. It starts with the “engineering-science” definitions of efficiency and uses the duality theory of linear programming to show how, in DEA, they can be related to the Pareto–Koopmans definitions used in “welfare economics” as well as in the economic theory of production. Some of the models that have now been developed for implementing these concepts are then described and propert...
74 Citations Source Cite
Published on Dec 1, 2006
William W. Cooper70
Estimated H-index: 70
(University of Texas at Austin),
Lawrence M. Seiford42
Estimated H-index: 42
,
Kaoru Tone34
Estimated H-index: 34
(GRIPS: National Graduate Institute for Policy Studies)
General Discussion.- The Basic CCR Model.- The CCR Model and Production Correspondence.- Alternative Dea Models.- Returns To Scale.- Models with Restricted Multipliers.- Discretionary, non-Discretionary and Categorical Variables.- Allocation Models.- Data Variations.- Super-Efficiency Models.
353 Citations Source Cite
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