Parallel genetic algorithms for product configuration management on PC cluster systems

Abstract

This work presents two parallel genetic algorithms (PGAs) for product configuration management: a parallel conventional genetic algorithm (PCGA) and a parallel multiple-searching genetic algorithm (PMGA). This parallel/distributed approach is based on a coarse-grained (or island) paradigm which is implemented on a cluster of PCs using message passing interface for the genetic information interchange. The product configuration problem assuming that customers would like to have minimum cost and a customized product can be obtained by finding the shortest path of the configuration network diagram. The performance of these algorithms is estimated by comparing the solutions of PGAs with those of sequential genetic algorithms (GAs) and mathematical programming. A weighting scale example from an empirical study is reported for illustrational purposes. Computational results show that the solutions obtained from the PMGA outperform other GAs in both accuracy and efficiency.