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Intelligent evolutional algorithm for distribution network optimization

Based on experimental comparison, this paper discusses GA applied solving methods of medium-scale (100 cities) time constraint Traveling Salesman Problems (TSP) that suit repetitive use in interactive simulation for optimizing a large-scale distribution network. To solve both energy problems and environmental problems simultaneously, it is important to optimize a large-scale distribution network shared by multiple enterprises. Recently, in addition to the distribution efficiency, transportation specified time-constraints are increasingly required to improve productivity through supply chain management. Moreover, the network optimality should be considered from various aspects by human experts. Thus, both practical optimality and interactive response time are required. To satisfy these requirements, a "selfish-gene with limited allowance" type GA is proposed. Here, each gene of an individual satisfies only its constraints selfishly, disregarding the constraints of other genes in the same individual. Further, to some extent, even individuals that violate constraints can survive over generations. Even inferior individuals have the chance of reproduction over generations. The proposed method can solve one hundred time-constraint TSPs within 10% error bounds in a few minutes.