A Grid-Based Genetic Approach to Solving the Vehicle Routing Problem with Time Windows

Abstract

This paper describes one grid-based genetic algorithm approach to solve the vehicle routing problem with time windows in one experimental cluster MiniGrid. Clusters used in this approach are located in two Mexican cities (Cuernavaca and Jiutepec, Morelos) securely communicating with each other since they are configured as one virtual private network, and its use as a single set of processors instead of isolated groups allows one to increase the computing power to solve complex tasks. The genetic algorithm splits the population of candidate solutions in several segments, which are simultaneously mutated in each process generated by the MiniGrid. These mutated segments are used to build a new population combining the results produced by each process. In this paper, the MiniGrid configuration scheme is described, and both the communication latency and the speedup behavior are discussed. Experimental results show one information exchange reduction through the MiniGrid clusters as well as an improved behavior of the evolutionary algorithm. A statistical analysis of these results suggests that our approach is better as a combinatorial optimization procedure as compared with other methods.