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A preference-based, multi-unit auction for pricing and capacity allocation

dc.contributor.authorLessan, Javad
dc.contributor.authorKarabati, Selcuk
dc.date.accessioned2018-10-18T16:13:12Z
dc.date.available2018-10-18T16:13:12Z
dc.date.issued2018-03-01
dc.descriptionThe final publication is available at Elsevier via https://dx.doi.org/10.1016/j.cor.2017.09.024 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.description.abstractWe study a pricing and allocation problem of a seller of multiple units of a homogeneous item, and present a semi-market mechanism in the form of an iterative ascending-bid auction. The auction elicits buyers' preferences over a set of options offered by the seller, and processes them with a random-priority assignment scheme to address buyers' "fairness" expectations. The auction's termination criterion is derived from a mixed-integer programming formulation of the preference-based capacity allocation problem. We show that the random priority- and preference-based assignment policy is a universally truthful mechanism which can also achieve a Pareto-efficient Nash equilibrium. Computational results demonstrate that the auction mechanism can extract a substantial portion of the centralized system's profit, indicating its effectiveness for a seller who needs to operate under the "fairness" constraint.en
dc.identifier.urihttps://dx.doi.org/10.1016/j.cor.2017.09.024
dc.identifier.urihttp://hdl.handle.net/10012/14009
dc.language.isoenen
dc.publisherElsevieren
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectMulti-unit auctionsen
dc.subjectPricing and capacity allocationen
dc.subjectMixed-integer programmingen
dc.titleA preference-based, multi-unit auction for pricing and capacity allocationen
dc.typeArticleen
dcterms.bibliographicCitationLessan, J., & Karabatı, S. (2018). A preference-based, multi-unit auction for pricing and capacity allocation. Computers & Operations Research, 91, 237–246. doi:10.1016/j.cor.2017.09.024en
uws.contributor.affiliation1Faculty of Engineeringen
uws.contributor.affiliation2Civil and Environmental Engineeringen
uws.peerReviewStatusRevieweden
uws.scholarLevelGraduateen
uws.typeOfResourceTexten
uws.typeOfResourceTexten

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