A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows

Montoya, Francisco G.; Ortega, Julio; Gil, C.; Márquez, Antonio López; Toro, Francisco de

doi:10.1016/j.cie.2013.01.007

Cited by 124 publications

(57 citation statements)

References 33 publications

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“…The ant colony (ACO) has been applied to solve capacitated vehicle routing problems successfully [13][14][15]. Baños et al [16] introduced a hybrid meta-heuristic for multi-objective vehicle routing problems with time windows. To solve vehicle routing problem with simultaneous pickup and delivery, Goksal et al [17] described a hybrid discrete particle swarm optimization.…”

Section: Introductionmentioning

confidence: 99%

Vehicle Routing Problem with Time Windows Using Multi-Objective Co-Evolutionary Approach

Wu¹,

Dong²,

Li³

2016

Int. j. simul. model.

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This paper introduces a novel multi-objective algorithm (HMPSO) based on discrete particle swarm optimization (PSO) to solve vehicle routing problems with time windows (VRPTW). The presented HMPSO algorithm was combined with an advanced discrete PSO based on set and variable neighbourhood searches to find Pareto optimal routing solutions. These consisted of a complete routing schedule for serving the customers to minimize the two aims of travelling distance and number of vehicles. To increase the discrete PSO efficiency, a novel decoding scheme based on set was designed, and the variable neighbourhood local search was employed to explore new solutions. The experiment results were showed for a set of the Solomon's 56 VRPTW. The HMPSO algorithm was compared with some algorithms published in papers with the computational evaluations clearly supporting the high performance of the proposed HMPSO algorithm against other algorithms, and confirming that the HMPSO is an efficient algorithm because of a reasonable computational time and cost in solve VRPTW.

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Section: Introductionmentioning

confidence: 99%

Vehicle Routing Problem with Time Windows Using Multi-Objective Co-Evolutionary Approach

Wu¹,

Dong²,

Li³

2016

Int. j. simul. model.

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“…For example couple (22,29) intersection of line 1 and column 2 is obtained by: The set of sequentially efficient solutions is in conformity with the table 3: E(P) = { (22,29), (40,27), (53,21), (98,20), (108,19), (143,11)}.Corresponding roads are respectively (0-1-2-0), (0-2-3-0), (0-1-10-0), (0-1-11-0), (0-2-11-0) and (0-10-11-0) of respective capacities 6, 7, 6, 7, 7, 7 , these roads are incompatible because only customer 3 is visited once.…”

Section: Solving Problemmentioning

confidence: 99%

“…In transportation management [9], there is a requirement to provide services from a supply point (depot) (see [10], [11] to various geographically dispersed points (customers) with significant economic implications; many researchers have developed solution approaches for those problems (see [12], [15]). We devote this paper to the hybridization of some heuristics dedicated to classical VRP problems for solving the multiobjective Vehicle Routing Problem (MOVRP) (see [16], [19]). There are: i)…”

Section: Introductionmentioning

confidence: 99%

Cobweb heuristic for multi-objective vehicle routing problem

F¹,

E.-L²,

Nt.³

2015

IJAMR

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Solving a classical vehicle routing problem (VRP) by exact methods presents many difficulties for large dimension problem. Consequently, in multi-objective framework, heuristic or metaheuristic methods are required. Due to particular VRP structure, it seems that a dedicated heuristicis more suitable than a metaheuristic. The aim of this article is to collapse different heuristics solving classical VRP and adapt them for to solve the multi-objective vehicle routing problem (MOVRP). The so-called Cobweb Algorithm simulates spider's behavior when weaving cobweb. This paper presents the algorithm, a didactic example, concluding remarks and way for further researches.

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“…Meta-heuristic algorithms, which often couple exploring the search space with its intensive exploitation, allow for existing infeasible solutions and deteriorating their quality temporarily. Such approaches include simulated annealing (Chiang and Russell 1996), tabu searches (Ho and Haugland 2004), swarm optimization (Hu et al 2013), ant colony systems (Gambardella et al 1999;Gomez et al 2014), hybrid techniques (Liu et al 2014), and many more (Bräysy and Gendreau 2005;Coltorti and Rizzoli 2007;Banos et al 2013).…”

Section: Vehicle Routing Problem With Time Windowsmentioning

confidence: 99%

Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

Nalepa

Błocho²

2015

Soft Comput

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This paper presents an adaptive memetic algorithm to solve the vehicle routing problem with time windows (VRPTW). It is a well-known NP-hard discrete optimization problem with two objectives-to minimize the number of vehicles serving a set of geographically dispersed customers, and to minimize the total distance traveled in the routing plan. Although memetic algorithms have been proven to be extremely efficient in solving the VRPTW, their main drawback is an unclear tuning of their numerous parameters. Here, we introduce the adaptive memetic algorithm (AMA-VRPTW) for minimizing the total travel distance. In AMA-VRPTW, a population of solutions evolves with time. The parameters of the algorithm, including the selection scheme, population size and the number of child solutions generated for each pair of parents, are adjusted dynamically during the search. We propose a new adaptive selection scheme to balance the exploration and exploitation of the solution space. Extensive experimental study performed on the well-known Solomon's and Gehring and Homberger's benchmark sets confirms the efficacy and convergence capabilities of the proposed AMA-VRPTW. We show that it is very competitive compared with other state-of-the-art techniques. Finally, the influence of the proposed adaptive schemes on the AMA-VRPTW behavior and performance is investigated in a thorough sensitivity analysis. This analysis is complemented with

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A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows

Cited by 124 publications

References 33 publications

Vehicle Routing Problem with Time Windows Using Multi-Objective Co-Evolutionary Approach

Vehicle Routing Problem with Time Windows Using Multi-Objective Co-Evolutionary Approach

Cobweb heuristic for multi-objective vehicle routing problem

Adaptive memetic algorithm for minimizing distance in the vehicle routing problem with time windows

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