TY - JOUR
T1 - Combining statistical learning with metaheuristics for the multi-depot vehicle routing problem with market
AU - Calvet, Laura
AU - Ferrer, Albert
AU - Gomes, M. Isabel
AU - Juan, Angel A
AU - Masip, David
N1 - sem pdf do autor.
Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P ; TRA2015-71883-REDT MTM2014-59179-C2-01-P)
Portuguese Foundation for Science and Technology (FCT) - UID/MAT/00297/2013
Dept. of Universities, Research, and Information Society of the Catalan Government (2014-CTP-00001)
PY - 2016
Y1 - 2016
N2 - 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 final 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 fitness
of the customer-depot 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 offer and
customers show different willingness to consume depending on how well
the assigned depot fits their preferences. Thus, we assume that
different customer-depot assignment maps will lead to different
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 differs in terms
of the proposed solutions from the traditional one. (C) 2016 Elsevier
Ltd. All rights reserved.
AB - 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 final 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 fitness
of the customer-depot 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 offer and
customers show different willingness to consume depending on how well
the assigned depot fits their preferences. Thus, we assume that
different customer-depot assignment maps will lead to different
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 differs in terms
of the proposed solutions from the traditional one. (C) 2016 Elsevier
Ltd. All rights reserved.
KW - market segmentation applications, multi-depot vehicle routing problem
KW - Hybrid algorithms
KW - Multi-DepVehicle Routing Problem
KW - Statistical learning
U2 - 10.1016/j.cie.2016.01.016
DO - 10.1016/j.cie.2016.01.016
M3 - Article
SN - 0360-8352
VL - 94
SP - 93
EP - 104
JO - Computers & Industrial Engineering
JF - Computers & Industrial Engineering
ER -