Article ID: | iaor201525940 |
Volume: | 7 |
Issue: | 4 |
Start Page Number: | 426 |
End Page Number: | 456 |
Publication Date: | May 2015 |
Journal: | International Journal of Shipping and Transport Logistics |
Authors: | Saen Reza Farzipoor, Shabani Amir, Torabipourv Seyed Mohammad Reza |
Keywords: | supply & supply chains, statistics: data envelopment analysis |
Cold chain management (CCM) is a system in which perishable products are managed. For the success of transportation system of CCM, optimal selection of vehicle is a significant subject. Data envelopment analysis (DEA) can be used for vehicle selection problems. However, in this particular area, conventional DEA models are faced with difficulties. The problem are: 1) dual‐role factors which classical DEA models cannot deal with them; 2) comparing decision making units (DMUs) with virtual DMUs (not with actual DMUs); 3) tie between DMUs (i.e., many DMUs are recognised efficient; 4) infeasibility of DEA ranking model for certain data. In this paper, a new procedure is developed to rectify the mentioned shortcomings. The proposed procedure, which utilises free disposal hull (FDH) technology, is a super‐efficiency approach to provide a full ranking of efficient DMUs in the presence of dual‐role factors. A case study illustrates application of the proposed procedure.