Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/62441
Title: A decision support tool for urban freight transport planning based on a multi-objective evolutionary algorithm
Authors: Miguel, Fabio
Frutos, Mariano
Tohme, Fernando
Méndez Babey, Máximo 
UNESCO Clasification: 3329 Planificación urbana
332907 Transporte
1207 Investigación operativa
120304 Inteligencia artificial
Keywords: Decision support systems
Evolutionary computation
Genetic algorithms
Logistics
Pareto optimization, et al
Issue Date: 2019
Journal: IEEE Access
Abstract: We present an optimization procedure based on a hybrid version of an evolutionary multi-objective 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.
URI: http://hdl.handle.net/10553/62441
ISSN: 2169-3536
DOI: 10.1109/ACCESS.2019.2949948
Source: IEEE Access [ISSN 2169-3536], v. 7, p. 156707-156721
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