dc.contributor.author | Salahi, Maziar | |
dc.contributor.author | Toloo, Mehdi | |
dc.contributor.author | Torabi, Narges | |
dc.date.accessioned | 2020-06-24T08:15:40Z | |
dc.date.available | 2020-06-24T08:15:40Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Journal of the Operational Research Society. 2020. | cs |
dc.identifier.issn | 0160-5682 | |
dc.identifier.issn | 1476-9360 | |
dc.identifier.uri | http://hdl.handle.net/10084/139564 | |
dc.description.abstract | Flexibility in selecting the weights of inputs and outputs in data envelopment analysis models and uncertainty associated with the data might lead to unreliable efficiency scores. In this paper, to avoid these problems, first, we discuss robust Charnes, Cooper, Rhodes (CCR) model under Bertsimas and Sim approach. Then, the robust CCR solutions are used to find robust common set of weights under norm-1 and Bertsimas and Sim approach. Finally, on two numerical real-world examples, the performance of the proposed approach is compared by a similar recent approach from the literature to show the advantages of the new method and its applicability. | cs |
dc.language.iso | en | cs |
dc.publisher | Taylor & Francis | cs |
dc.relation.ispartofseries | Journal of the Operational Research Society | cs |
dc.relation.uri | http://doi.org/10.1080/01605682.2020.1718016 | cs |
dc.rights | Rights managed by Taylor & Francis | cs |
dc.subject | data envelopment analysis | cs |
dc.subject | common set of weights | cs |
dc.subject | uncertainty | cs |
dc.subject | robust optimization | cs |
dc.title | A new robust optimization approach to common weights formulation in DEA | cs |
dc.type | article | cs |
dc.identifier.doi | 10.1080/01605682.2020.1718016 | |
dc.type.status | Peer-reviewed | cs |
dc.description.source | Web of Science | cs |
dc.identifier.wos | 000536109500001 | |