A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands

Mendoza, Jorge E.; Castanier, Bruno; Guéret, Christelle; Medaglia, Andrés L.; Velasco, Nubia

doi:10.1016/j.cor.2009.06.015

Cited by 154 publications

(88 citation statements)

References 26 publications

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“…The most common solution methodology for this class of problems is called a priori optimization, a concept initially proposed by Bertsimas et al (1990) and applied by several authors to the field of vehicle routing (e.g. Bertsimas 1992;Gendreau et al 1996;Laporte et al 2002Laporte et al , 2010Tan et al 2007;Mendoza et al 2010 andLei et al 2011). In a priori optimization, a first-stage solution consisting of a set of districts is first constructed the realizations of the random variables (presence or absence of stochastic customers) are then revealed.…”

Section: Introductionmentioning

confidence: 99%

Districting for routing with stochastic customers

Lei

Laporte

Guo

2012

EURO Journal on Transportation and Logistics

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We introduce the vehicle routing and districting problem with stochastic customers (VRDPSC). This problem is modelled and solved as a two-stage stochastic program during which the districting decisions are made in the first stage and the Beardwood-Halton-Hammersley formula is used to approximate the expected routing cost of each district in the second stage. District compactness is also considered as part of the objective function. We have developed a large neighbourhood search heuristic for VRDPSC. The heuristic was tested on modified Solomon instances and on modified Gehring and Homberger instances. Extensive computational results confirm the effectiveness of the proposed heuristic.

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Section: Introductionmentioning

confidence: 99%

Districting for routing with stochastic customers

Lei

Laporte

Guo

2012

EURO Journal on Transportation and Logistics

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“…It has the ability in this process to escape from local optima by visiting worse neighbouring solutions, and shows to be very effective when exploring the search space of complex multi-objective optimisation problems. Meanwhile, genetic local search algorithms (Ishibuchi and Murata 1998;Jaszkiewicz 2002;Mendoza et al 2010) have been investigated for different multi-objective optimisation problems. Due to the ability of local search to find local optima effectively over a relatively small part of the search space, genetic local search algorithms have been shown to be very suitable for solving complex multi-objective optimisation problems.…”

Section: Related Workmentioning

confidence: 99%

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

2013

Ann Oper Res

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Abstract. This paper presents a new hybrid evolutionary algorithm to solve multi-objective multicast routing problems in telecommunication networks. The algorithm combines simulated annealing strategies and genetic local search, aiming at a more flexible and effective exploration and exploitation in the search space of the complex problem to find more non-dominated solutions in the Pareto Front. Due to the complex structure of the multicast tree, crossover and mutation operators have been specifically devised concerning the features and constraints in the problem. A new adaptive mutation probability based on simulated annealing is proposed in the hybrid algorithm to adaptively adjust the mutation rate according to the fitness of the new solution against the average quality of the current population during the evolution procedure. Two simulated annealing based search direction tuning strategies are applied to improve the efficiency and effectiveness of the hybrid evolutionary algorithm. Simulations have been carried out on some benchmark multi-objective multicast routing instances and a large amount of random networks with five real world objectives including cost, delay, link utilizations, average delay and delay variation in telecommunication networks. Experimental results demonstrate that both the simulated annealing strategies and the genetic local search within the proposed multi-objective algorithm, compared with other multi-objective evolutionary algorithms, can efficiently identify high quality non-dominated solution set for multi-objective multicast routing problems and outperform other conventional multi-objective evolutionary algorithms in the literature.

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“…they have been introduced by Moscato in 1989 [19], they have led to excellent results for different problems, Krasnogor and Smith [20] have reviewed some applications of memetic algorithms to well known combinatorial optimization problems and Neri and Cotta have presented a literature review of these algorithms [21]. Various memetic algorithms have been developed for routing problems; Freizleben and Mertz [22] used it to solve the TSP, Cattaruzza et al [23] for the multi-trip vehicle routing, Mendoza et al [24] for the multi-compartment vehicle routing problem with stochastic demands, Prins [25] and Lima et al [26] have proposed memetic algorithms to solve heterogenous fleet vehicle routing problem. The most recent method has been proposed by Sörensen and Sevaux [27], it is a memetic algorithm with population management (MA/PM) that defines a measure of distance in order to diversify the chromosomes parents of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Memetic Algorithm for the Capacitated Location-Routing Problem

Kechmane¹,

Nsiri²,

Baalal³

2016

ijacsa

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Abstract-In this paper, a hybrid genetic algorithm is proposed to solve a Capacitated Location-Routing Problem. The objective is to minimize the total cost of the distribution in a network composed of depots and customers, both depots and vehicles have limited capacities, each depot has a homogenous vehicle fleet and customers' demands are known and must be satisfied. Solving this problem involves making strategic decisions such as the location of depots, as well as tactical and operational decisions which include assigning customers to the opened depots and organization of the vehicle routing. To evaluate the performance of the proposed algorithm, its results are compared to those obtained by a greedy randomized adaptive search procedure, computational results shows that the algorithm gave good quality solutions.

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A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands

Cited by 154 publications

References 26 publications

Districting for routing with stochastic customers

Districting for routing with stochastic customers

A simulated annealing based genetic local search algorithm for multi-objective multicast routing problems

A Memetic Algorithm for the Capacitated Location-Routing Problem

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