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The article describes an investigation of the effectiveness of genetic al- gorithms for multi-objective combinatorial optimization (MOCO) by presenting an application for the vehicle routing problem with soft time windows. The work is motivated by the question, if and how the problem structure influences the effec- 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. The results are compared with a simple, but effective local search approach for multi-objective combinatorial optimization problems.

A Computational Study of Genetic Crossover Operators for Multi-Objective Vehicle Routing Problem with Soft Time Windows