A Computational Study of Genetic Crossover Operators for MultiObjective Vehicle Routing Pr.pdfVIP

A Computational Study of Genetic Crossover Operators for Multi-Objective Vehicle Routing Problem with Soft Time Windows Martin Geiger 8 University of Hohenheim, Production and Operations Management Department, 0 70593 Stuttgart, Germany 0 2 Abstract. The article describes an investigation of the effectiveness of genetic al- p gorithms for multi-objective combinatorial optimization (MOCO) by presenting an e application for the vehicle routing problem with soft time windows. The work is S motivated by the question, if and how the problem structure influences the effec- 2 tiveness of different configurations of the genetic algorithm. Computational results are presented for different classes of vehicle routing problems, varying in their cov- ] erage with time windows, time window size, distribution and number of customers. I A The results are compared with a simple, but effective local search approach for . multi-objective combinatorial optimization problems. s c [ 1 Introduction 1 v The requirements of modern logistics are widespread. It seems natural that 0 1 for practical problems several objectives have to be optimized. The quality 4 of service and products and the corresponding cost are only a small subset 0 of necessary objectives that should be considered [25]. 9. The distribution of products, a part of the supply chain in logistics, can be 0