Traveling salesman problem solving method fit for interactive repetitive simulation of large-scale distribution networks

Based on experimental comparison, this paper discusses approximate solution methods of medium-scale traveling salesman problems (TSPs) which 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 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 carried out an experiment for comparison among approximate methods using a random restart strategy that iterates the combination of random initialization and local search. As a result of this experimental comparison, we discovered that one of the approximate methods could obtain solutions ensuring errors below 2-3% within 0.1 second. Thus, this method is considered to be promising for realizing a system that enables one to carry out interactive simulations repetitively for constructing a globally optimized large-scale logistic network