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dc.contributor.authorFerrer Biosca, Alberto
dc.contributor.authorCalvet Liñán, Laura
dc.contributor.authorJuan, Angel A.
dc.contributor.authorMasip Rodó, David
dc.contributor.authorGomes, M. Isabel
dc.date.accessioned2019-05-16T11:31:17Z
dc.date.available2019-05-16T11:31:17Z
dc.date.issued2016-02
dc.identifier.citationFerrer, A. [et al.]. Combining statistical learning with metaheuristics for the multi-depot vehicle routing problem with market segmentation. "Computers and industrial engineering", Febrer 2016, vol. 94, p. 93-104.
dc.identifier.issn0360-8352
dc.identifier.urihttp://hdl.handle.net/2117/133073
dc.description.abstractIn real-life logistics and distribution activities it is usual to face situations in which the distribution of goods has to be made from multiple warehouses or depots to the nal customers. This problem is known as the Multi-Depot Vehicle Routing Problem (MDVRP), and it typically includes two sequential and correlated stages: (a) the assignment map of customers to depots, and (b) the corresponding design of the distribution routes. Most of the existing work in the literature has focused on minimizing distance-based distribution costs while satisfying a number of capacity constraints. However, no attention has been given so far to potential variations in demands due to the tness of the customerdepot mapping in the case of heterogeneous depots. In this paper, we consider this realistic version of the problem in which the depots are heterogeneous in terms of their commercial o er and customers show di erent willingness to consume depending on how well the assigned depot ts their preferences. Thus, we assume that di erent customer-depot assignment maps will lead to di erent customer-expenditure levels. As a consequence, market-segmentation strategies need to be considered in order to increase sales and total income while accounting for the distribution costs. To solve this extension of the MDVRP, we propose a hybrid approach that combines statistical learning techniques with a metaheuristic framework. First, a set of predictive models is generated from historical data. These statistical models allow estimating the demand of any customer depending on the assigned depot. Then, the estimated expenditure of each customer is included as part of an enriched objective function as a way to better guide the stochastic local search inside the metaheuristic framework. A set of computational experiments contribute to illustrate our approach and how the extended MDVRP considered here diré in terms of the proposed solutions from the traditional one.
dc.format.extent12 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Matemàtiques i estadística::Àlgebra
dc.subject.lcshMachine learning
dc.subject.lcshAlgorithms
dc.subject.otherMulti-Depot Vehicle Routing Problem
dc.subject.othermarket segmentation applications
dc.subject.otherhybrid algorithms
dc.subject.otherstatistical learning
dc.titleCombining statistical learning with metaheuristics for the multi-depot vehicle routing problem with market segmentation
dc.typeArticle
dc.subject.lemacAprenentatge automàtic
dc.subject.lemacAlgorismes
dc.contributor.groupUniversitat Politècnica de Catalunya. GNOM - Grup d'Optimització Numèrica i Modelització
dc.identifier.doi10.1016/j.cie.2016.01.016
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
local.identifier.drac17494350
dc.description.versionPreprint
local.citation.authorFerrer, A.; Calvet , L.; Juan, A.; Masip, D.; Gomes , M. Isabel
local.citation.publicationNameComputers and industrial engineering
local.citation.volume94
local.citation.startingPage93
local.citation.endingPage104


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