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A multiple criteria approach to two-stage data envelopment analysis

Mahdiloo, Mahdi, Jafarzadeh, Abdol Hossein, Saen, Reza Farzipoor, Tatham, Peter and Fisher, Ron 2016, A multiple criteria approach to two-stage data envelopment analysis, Transportation research part D, vol. 46, pp. 317-327, doi: 10.1016/j.trd.2016.04.008.

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Title A multiple criteria approach to two-stage data envelopment analysis
Author(s) Mahdiloo, Mahdi
Jafarzadeh, Abdol Hossein
Saen, Reza Farzipoor
Tatham, Peter
Fisher, Ron
Journal name Transportation research part D
Volume number 46
Start page 317
End page 327
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-07
ISSN 1361-9209
Keyword(s) two-stage DEA
multiple criteria DEA
discrimination power
sustainable design
automobile manufacturers
Summary Two-stage data envelopment analysis (DEA) models are commonly used in the evaluation and benchmarking of sustainable operations and processes across multiple research fields. To date, however, little attention has been given to the unrealistic weight distribution and weak discrimination power in the modeling and evaluation of the two-stage sustainable operations when using two-stage DEA models. In order to overcome this methodological weakness, we use the multiple criteria DEA (MCDEA) approach in the evaluation of the two-stage processes. The outcome is a multiple criteria two-stage DEA model which yields more realistic weights for the inputs and outputs and thus has better discrimination power than traditional two-stage DEA models. The developed model is tested and validated by assessing the sustainable design performances of a sample of car product designs.
Language eng
DOI 10.1016/j.trd.2016.04.008
Field of Research 1205 Urban And Regional Planning
1507 Transportation And Freight Services
0502 Environmental Science And Management
Socio Economic Objective 0 Not Applicable
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30092339

Document type: Journal Article
Collection: School of Information and Business Analytics
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