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.

Introduction

Decision-making tools based on bio-inspired algorithms have been successfully used in logistics during the last decades. They have been continuously improved in the context of urban freight transport (UFT). The goal has always been increasing the efficiency and competitiveness of the firms, an objective usually hampered by the atomization of the sector and the complexity of logistic management at this stage of supply chains. A frequent issue involves taking into account in the decision-making process the needs of third parties since externalities over the relations with other agents may lead to quality and competitiveness losses in merchandise deliverance. We seek here to overcome those limitations by changing to a multi-objective cooperative objective approach, taking into account the interests of all the parties involved in the process, ranging from managers of distribution centers to the final customers. We proceed by developing a hybrid version of an evolutionary multi-objective algorithm addressing the problem of a firm delivering perishable fresh goods from several distribution centers, carrying relatively small fractional volumes to a large number of grocery stores in Buenos Aires