Parallel computing for huge scale logistics optimization through binary PSO associated with topological comparison

Abstract

To cope with extremely large-scale logistic optimization for strategic planning and real time operational optimizations as well, in this paper, we have proposed an extended algorithm of our hybrid method so that it becomes available for parallel computing. We have also developed a novel algorithm of particle swarm optimization (PSO) associated with binary decision variables. It is quite effective for finding the optimum opening distribution centers in three-echelon logistic network by parallel computing. Eventually, we have implemented the procedure in the parallel algorithm deployed as a multi-population based approach using multi-thread programming technique. Taking two topologies belonging to a coarse grain parallelism, we compared their effects on the performance of the algorithm through large scale logistics optimization. Finally, we confirmed that the proposed method can bring about high performance for the parallel computing that is suitable for the present goal and circumstance through numerical experiments.