A randomized concave programming method for choice network revenue management

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Talluri, Kalyan. A randomized concave programming method for choice network revenue management. 2010
To cite or link this document: http://hdl.handle.net/10230/6342
dc.contributor.author Talluri, Kalyan
dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
dc.date.issued 2010-04-01
dc.identifier.uri http://hdl.handle.net/10230/6342
dc.description.abstract Models incorporating more realistic models of customer behavior, as customers choosing from an offer set, have recently become popular in assortment optimization and revenue management. The dynamic program for these models is intractable and approximated by a deterministic linear program called the CDLP which has an exponential number of columns. However, when the segment consideration sets overlap, the CDLP is difficult to solve. Column generation has been proposed but finding an entering column has been shown to be NP-hard. In this paper we propose a new approach called SDCP to solving CDLP based on segments and their consideration sets. SDCP is a relaxation of CDLP and hence forms a looser upper bound on the dynamic program but coincides with CDLP for the case of non-overlapping segments. If the number of elements in a consideration set for a segment is not very large (SDCP) can be applied to any discrete-choice model of consumer behavior. We tighten the SDCP bound by (i) simulations, called the randomized concave programming (RCP) method, and (ii) by adding cuts to a recent compact formulation of the problem for a latent multinomial-choice model of demand (SBLP+). This latter approach turns out to be very effective, essentially obtaining CDLP value, and excellent revenue performance in simulations, even for overlapping segments. By formulating the problem as a separation problem, we give insight into why CDLP is easy for the MNL with non-overlapping considerations sets and why generalizations of MNL pose difficulties. We perform numerical simulations to determine the revenue performance of all the methods on reference data sets in the literature.
dc.language.iso eng
dc.relation.ispartofseries Economics and Business Working Papers Series; 1215
dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.title A randomized concave programming method for choice network revenue management
dc.type info:eu-repo/semantics/workingPaper
dc.date.modified 2014-06-03T07:14:26Z
dc.subject.keyword Management and Organization Studies
dc.subject.keyword assortment optimization
dc.subject.keyword randomized algorithms
dc.subject.keyword network revenue management.
dc.rights.accessRights info:eu-repo/semantics/openAccess

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