Inequity averse optimization in operational research

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dc.citation.epage359en_US
dc.citation.issueNumber2en_US
dc.citation.spage343en_US
dc.citation.volumeNumber245en_US
dc.contributor.authorKarsu, Ö.en_US
dc.contributor.authorMorton, A.en_US
dc.date.accessioned2016-02-08T10:04:22Z
dc.date.available2016-02-08T10:04:22Z
dc.date.issued2015en_US
dc.departmentDepartment of Industrial Engineeringen_US
dc.description.abstractThere are many applications across a broad range of business problem domains in which equity is a concern and many well-known operational research (OR) problems such as knapsack, scheduling or assignment problems have been considered from an equity perspective. This shows that equity is both a technically interesting concept and a substantial practical concern. In this paper we review the operational research literature on inequity averse optimization. We focus on the cases where there is a tradeoff between efficiency and equity. We discuss two equity related concerns, namely equitability and balance. Equitability concerns are distinguished from balance concerns depending on whether an underlying anonymity assumption holds. From a modeling point of view, we classify three main approaches to handle equitability concerns: the first approach is based on a Rawlsian principle. The second approach uses an explicit inequality index in the mathematical model. The third approach uses equitable aggregation functions that can represent the DM's preferences, which take into account both efficiency and equity concerns. We also discuss the two main approaches to handle balance: the first approach is based on imbalance indicators, which measure deviation from a reference balanced solution. The second approach is based on scaling the distributions such that balance concerns turn into equitability concerns in the resulting distributions and then one of the approaches to handle equitability concerns can be applied. We briefly describe these approaches and provide a discussion of their advantages and disadvantages. We discuss future research directions focussing on decision support and robustness.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T10:04:22Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2015en
dc.identifier.doi10.1016/j.ejor.2015.02.035en_US
dc.identifier.eissn1872-6860
dc.identifier.issn0377-2217
dc.identifier.urihttp://hdl.handle.net/11693/22758
dc.language.isoEnglishen_US
dc.publisherElsevieren_US
dc.relation.isversionofhttp://dx.doi.org/10.1016/j.ejor.2015.02.035en_US
dc.source.titleEuropean Journal of Operational Researchen_US
dc.subjectEquitable efficiencyen_US
dc.subjectFairnessen_US
dc.subjectInequityen_US
dc.subjectMulticriteria decision makingen_US
dc.subjectOptimizationen_US
dc.subjectCombinatorial optimizationen_US
dc.subjectDecision makingen_US
dc.subjectDecision support systemsen_US
dc.subjectSchedulingen_US
dc.subjectAggregation functionsen_US
dc.subjectAssignment problemsen_US
dc.subjectBusiness problemsen_US
dc.subjectFuture research directionsen_US
dc.subjectMulti criteria decision makingen_US
dc.subjectOperational researchen_US
dc.subjectEfficiencyen_US
dc.titleInequity averse optimization in operational researchen_US
dc.typeArticleen_US

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