Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows

A typical vehicle routing problem can be described as the problem of designing least cost routes from one depot to a set of geographically scattered points (cities, stores, warehouses, schools, customers etc). The routes must be designed in such a way that each point is visited only once by exactly one vehicle, all routes start and end at the depot, and the total demands of all points on one particular route must not exceed the capacity of the vehicle. The vehicle routing problem with time windows is a generalization of the standard vehicle routing problem involving the added complexity that every customer should be served within a given time window. In this paper we review shortly the developed genetic algorithm based approaches for solving the vehicle routing problem with time windows and compare their performance with the best recent metaheuristic algorithms. The findings indicate that the results obtained with pure genetic algorithms are not competitive with the best published results, though the differences are not overwhelming.