Biased random key genetic algorithm with insertion and gender selection for capacitated vehicle routing problem with time windows

Rochman, Auliya Noor; Prasetyo, Hari; Nugroho, Munajat Tri

doi:10.1063/1.4985470

Cited by 4 publications

(1 citation statement)

References 7 publications

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“…The meta-heuristics that have been applied in the UFT context are ant colony optimization [30], artificial bee colony [34], biogeography-based algorithms [23], the firefly algorithm [19], evolutionary algorithms [15], [17], [18], [24], [25], [31], [21], taboo search [33], [38] and simulated annealing [29], [40], among others. From the articles that address multi-objective versions of VRP with more than 20 nodes (clients), in more than 80% of them use, as said, meta-heuristics, and among them, in particular Evolutionary Multi-Objective Algorithms (MOEAs) [36].…”

Section: Literature Reviewmentioning

confidence: 99%

A Decision Support Tool for Urban Freight Transport Planning Based on a Multi-Objective Evolutionary Algorithm

et al. 2019

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We present an optimization procedure based on a hybrid version of an evolutionary multiobjective decision-making algorithm for its application in urban freight transportation planning problems. This tool is intended to solve the planning problems of a merchandise distribution firm that dispatches small volume fractional loads of fresh foods on daily schedules. The firm owns a network of distribution centers supplying a large number of small businesses in Buenos Aires and its surroundings. The recombination operator of the evolutionary algorithm used here has been designed specifically for this problem. It is intended to embody a strategy that takes into account constraints like temporary closeness, closeness time window and connectivity in order to improve its performance in the clustering phase. The representation allows incorporating specific information about the actual instances of the problem and uses adaptive control of the parameters in the calibration stage. The performance of the proposed optimizer was tested against the results obtained by two evolutionary algorithms, NSGA II and SPEA 2, widely used in similar problems. We use hypervolume as a measure of convergence and dispersion of Pareto fronts. The statistical analysis of the results obtained with the three algorithms uses the Wilcoxon rank sum test, which yields evidence that our procedure provides good results.

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