Based on experimental comparison, this paper discusses approximate solution methods of medium-scale traveling salesman problems (TSPs) that suit repetitive use in interactive simulation for globally optimizing a large-scale distribution logistic network. For constructing a globally optimized large-scale logistic network, the problem is decomposed into hundreds of sub-problems. And each sub-problem including above-mentioned TSPs should be repetitively solved. Thus, it is essential to find approximate solution methods of medium-scale TSPs that suit the heavily repetitive use in interactive simulation for globally optimizing a large-scale distribution logistic network. Accordingly, we made an experiment for comparison among approximate methods using random restart strategy that iterates the combination of random initialization and local search. As a result of this experimental comparison, we discovered one of above approximate methods could obtain solutions ensuring errors below 2-3% within 0.1 second.Thus, this method is considered promising to realize the system that enables to make above-mentioned interactive simulations repetitively for constructing a globally optimized large-scale logistic network.
Based on experimental comparison, this paper discusses GA applied solving methods of medium-scale (100 cities) time constraint Traveling Salesman Problem (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 this simulation. 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. And, even inferior individuals have the chance of their reproductions over generations. Thus, these individuals get the chance of improvement. As a result of our experimental comparison, the proposed solving method could solve one hundred time-constraint TSPs within 10% errors, less than a few minntes.
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