Entity

Time filter

Source Type


Jin J.,Molde University College | Crainic T.G.,Interuniversity Research Center on Enterprise Networks | Lokketangen A.,Molde University College
Computers and Operations Research | Year: 2014

This paper introduces a cooperative parallel metaheuristic for the capacitated vehicle routing problem. The proposed metaheuristic consists of multiple parallel tabu search threads that cooperate by asynchronously exchanging best-found solutions through a common solution pool. The solutions sent to the pool are clustered according to their similarities. The search history information identified from the solution clusters is applied to guide the intensification or diversification of the tabu search threads. Computational experiments on two sets of large-scale benchmark instance sets from the literature demonstrate that the suggested metaheuristic is highly competitive, providing new best solutions to ten of those well-studied instances. © 2013 Elsevier Ltd. All rights reserved. Source


Laporte G.,Interuniversity Research Center on Enterprise Networks | Pascoal M.M.B.,University of Coimbra | Pascoal M.M.B.,Polytechnic Institute of Coimbra
Computers and Operations Research | Year: 2010

The minimum cost path problem with relays (MCPPR) consists of finding a minimum cost path from a source to a destination, along which relay nodes are located at a certain cost, subject to a weight constraint. This paper first models the MCPPR as a particular bicriteria path problem involving an aggregated function of the path and relay costs, as well as a weight function. A variant of this problem which takes into account all three functions separately is then considered. Formulating the MCPPR as a part of a bicriteria path problem allows the development of labeling algorithms in which the bound on the weight of paths controls the number of node labels. The algorithm for this constrained single objective function version of the problem has a time complexity of O(WmWnlog(maxW,n)), where n is the number of nodes, m is the number of arcs and W is the weight upper bound. Computational results on random instances with up to 10 000 nodes and 100 000 arcs, are reported. © 2010 Elsevier Ltd. All rights reserved. Source


Lei H.,National University of Defense Technology | Laporte G.,HEC Montreal | Laporte G.,Interuniversity Research Center on Enterprise Networks | Guo B.,National University of Defense Technology
Computers and Operations Research | Year: 2011

The capacitated vehicle routing problem with stochastic demands and time windows is an extension of the capacitated vehicle routing problem with stochastic demands, in which demands are stochastic and a time window is imposed on each vertex. A vertex failure occurring when the realized demand exceeds the vehicle capacity may trigger a chain reaction of failures on the remaining vertices in the same route, as a result of time windows. This paper models this problem as a stochastic program with recourse, and proposes an adaptive large neighborhood search heuristic for its solution. Modified Solomon benchmark instances are used in the experiments. Computational results clearly show the superiority of the proposed heuristic over an alternative solution approach. © 2011 Elsevier Ltd. All rights reserved. Source


Abounacer R.,Interuniversity Research Center on Enterprise Networks | Abounacer R.,University Ibn Zohr | Rekik M.,Interuniversity Research Center on Enterprise Networks | Rekik M.,Laval University | And 2 more authors.
Computers and Operations Research | Year: 2014

This paper considers a three-objective location-transportation problem for disaster response. The location problem aims at determining the number, the position and the mission of required humanitarian aid distribution centers (HADC) within the disaster region. The transportation problem deals with the distribution of aid from HADCs to demand points. Three conflicting objectives are considered. The first objective minimizes the total transportation duration of needed products from the distribution centers to the demand points. The second objective minimizes the number of agents (first-aiders) needed to open and operate the selected distribution centers. The third objective minimizes the non-covered demand for all demand points within the affected area. We propose an epsilon-constraint method for this problem and prove that it generates the exact Pareto front. The proposed algorithm can be applied to any three-objective optimization problem provided that the problem involves at least two integer and conflicting objectives. The results obtained in our experimental study show that the computing time required by the proposed method may be large for some instances. A heuristic version of our algorithm yielded, however, good approximation of the Pareto front in relatively short computing times. © 2013 Elsevier Ltd. Source


Coelho L.C.,Interuniversity Research Center on Enterprise Networks | Coelho L.C.,Laval University | Laporte G.,Interuniversity Research Center on Enterprise Networks | Laporte G.,HEC Montreal
Computers and Operations Research | Year: 2014

In this paper we analyze the optimal joint decisions of when, how and how much to replenish customers with products of varying ages. We discuss the main features of the problem arising in the joint replenishment and delivery of perishable products, and we model them under general assumptions. We then solve the problem by means of an exact branch-and-cut algorithm, and we test its performance on a set of randomly generated instances. Our algorithm is capable of computing optimal solutions for instances with up to 30 customers, three periods, and a maximum age of two periods for the perishable product. For the unsolved instances the optimality gap is always small, less than 1.5% on average for instances with up to 50 customers. We also implement and compare two suboptimal selling priority policies with an optimized policy: always sell the oldest available items first to avoid spoilage, and always sell the fresher items first to increase revenue. © 2014 Elsevier Ltd. Source

Discover hidden collaborations