dc.contributor |
Universitat Politècnica de Catalunya. GNOM - Grup d'Optimització Numèrica i Modelització |
dc.contributor.author |
Ferrer Biosca, Alberto |
dc.contributor.author |
Calvet Liñán, Laura |
dc.contributor.author |
Juan, Angel A. |
dc.contributor.author |
Masip Rodó, David |
dc.contributor.author |
Gomes, M. Isabel |
dc.date |
2016-02 |
dc.identifier.citation |
Ferrer, 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.citation |
0360-8352 |
dc.identifier.citation |
10.1016/j.cie.2016.01.016 |
dc.identifier.uri |
http://hdl.handle.net/2117/133073 |
dc.language.iso |
eng |
dc.rights |
Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights |
info:eu-repo/semantics/openAccess |
dc.rights |
http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject |
Àrees temàtiques de la UPC::Matemàtiques i estadística::Àlgebra |
dc.subject |
Machine learning |
dc.subject |
Algorithms |
dc.subject |
Multi-Depot Vehicle Routing Problem |
dc.subject |
market segmentation
applications |
dc.subject |
hybrid algorithms |
dc.subject |
statistical learning |
dc.subject |
Aprenentatge automàtic |
dc.subject |
Algorismes |
dc.title |
Combining statistical learning with metaheuristics for the multi-depot vehicle routing problem with market segmentation |
dc.type |
info:eu-repo/semantics/draft |
dc.type |
info:eu-repo/semantics/article |
dc.description.abstract |
In 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.description.abstract |
Peer Reviewed |