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dc.contributor.authorVercamer, Dauwe*
dc.contributor.authorVan den Poel, Dirk*
dc.contributor.authorGendreau, Michel*
dc.contributor.authorBaecke, Philippe*
dc.date.accessioned2019-01-14T14:14:17Z
dc.date.available2019-01-14T14:14:17Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6111
dc.description.abstractBased on a real case in door-to-door sales, the study assesses whether revenue predictions coming from transactional data can effectively be used to improve fleet schedules. To do this, two different customer selection models are compared. In the static model, customers are first chosen based on the revenue predictions and then routed through a VNS. The dynamic model uses the predictions in an orienteering problem. Initial results show that the dynamic approach is the most profitable.
dc.language.isoen
dc.subjectMarketing
dc.titleImproving profitability of vehicle routing problems through advanced analytics
vlerick.conferencedate09/11/2014-12/11/2014
vlerick.conferencelocationSan Francisco, California, United States
vlerick.conferencenameINFORMS Annual meeting
vlerick.knowledgedomainMarketing & Sales
vlerick.typeconfpresConference Presentation
vlerick.vlerickdepartmentMKT
dc.identifier.vperid151145


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