Lawrence M. Seiford
University of Michigan
93Publications
37H-index
16.4kCitations
Publications 93
Newest
Wade D. Cook48
Estimated H-index: 48
(York University),
Lawrence M. Seiford37
Estimated H-index: 37
(University of Michigan),
Joe Zhu57
Estimated H-index: 57
(Worcester Polytechnic Institute)
7 Citations Source Cite
Published on Jan 1, 2011
William W. Cooper66
Estimated H-index: 66
,
Lawrence M. Seiford37
Estimated H-index: 37
,
Joe Zhu57
Estimated H-index: 57
-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 ...
987 Citations Source Cite
Published on Jan 1, 2009in European Journal of Operational Research 3.43
Wade D. Cook48
Estimated H-index: 48
(York University),
Lawrence M. Seiford37
Estimated H-index: 37
(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...
798 Citations Source Cite
Published on Sep 28, 2007in Journal of Productivity Analysis 1.27
William W. Cooper66
Estimated H-index: 66
(University of Texas at Austin),
Lawrence M. Seiford37
Estimated H-index: 37
(University of Michigan)
+ 1 AuthorsJoe Zhu57
Estimated H-index: 57
(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...
73 Citations Source Cite
Published on Jul 1, 2005in Journal of Productivity Analysis 1.27
Lawrence M. Seiford37
Estimated H-index: 37
(University of Michigan),
Joe Zhu57
Estimated H-index: 57
(Worcester Polytechnic Institute)
Seiford and Zhu (1999) address the issue of sensitivity of returns to scale (RTS)classifications in data envelopment analysis (DEA). As noted in Jahanshahloo,Lotfi and Zohrehbandian (2004), a number of Theorems (e.g., Theorems 11, 12,16 and 17) in Seiford and Zhu (1999) may not be true for some decision makingunits (DMUs). They proposed a remedy for these Theorems. We point out that theissue can be addressed directly by the findings in Seiford and Zhu (1999).Note that such an issue is caused by DMU...
1 Citations Source Cite
Published on Mar 1, 2005in European Journal of Operational Research 3.43
Lawrence M. Seiford37
Estimated H-index: 37
(University of Michigan),
Joe Zhu57
Estimated H-index: 57
(Worcester Polytechnic Institute)
F€are and Grosskopf (2004) address the approach of Seiford and Zhu (2002) where undesirable input and output measures are treated in data envelopment analysis (DEA). One key feature of Seiford and Zhu s (2002) approach is that the bad outputs are treated as outputs in DEA model but are reduced when DEA efficiency is evaluated. F€are and Grosskopf (2004) suggest an alternative approach in treating the undesirable measures by distinguishing between weak and strong disposability and using a directi...
86 Citations Source Cite
Published on Apr 1, 2004in European Journal of Operational Research 3.43
Rajiv D. Banker62
Estimated H-index: 62
(University of Texas at Dallas),
William W. Cooper66
Estimated H-index: 66
(University of Texas at Austin)
+ 2 AuthorsJoe Zhu57
Estimated H-index: 57
(Worcester Polytechnic Institute)
Abstract This paper discusses returns to scale (RTS) in data envelopment analysis (DEA) for each of the presently available types of models. The BCC and CCR models 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...
225 Citations Source Cite
Wade D. Cook48
Estimated H-index: 48
(York University),
Lawrence M. Seiford37
Estimated H-index: 37
(University of Michigan),
Joe Zhu57
Estimated H-index: 57
(Worcester Polytechnic Institute)
The current paper presents mathematical programming models for use in benchmarking where multiple performance measures are needed to examine the performance and productivity changes. The standard data envelopment analysis method is extended to incorporate benchmarks through (i) a variable-benchmark model where a unit under benchmarking selects a portion of benchmark such that the performance is characterized in the most favorable light, and (ii) a fixed-benchmark model where a unit is benchmarke...
82 Citations Source Cite
Published on Apr 1, 2004in European Journal of Operational Research 3.43
William W. Cooper66
Estimated H-index: 66
(University of Texas at Austin),
Lawrence M. Seiford37
Estimated H-index: 37
+ 1 AuthorsStelios H. Zanakis21
Estimated H-index: 21
(College of Business Administration)
45 Citations Source Cite
Lawrence M. Seiford37
Estimated H-index: 37
(University of Michigan),
Joe Zhu57
Estimated H-index: 57
(Worcester Polytechnic Institute)
Data envelopment analysis (DEA) is a methodology for identifying the efficient frontier of decision making units (DMUs). Context-dependent DEA refers to a DEA approach where a set of DMUs are evaluated against a particular evaluation context. Each evaluation context represents an efficient frontier composed by DMUs in a specific performance level. The context-dependent DEA measures (i) the attractiveness when DMUs exhibiting poorer performance are chosen as the evaluation context, and (ii) the p...
132 Citations Source Cite
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