A Multi-Inner-World Genetic Algorithm Using Multiple Heuristics to Optimize Delivery Schedule
A delivery route optimization that improves the efficiency of real time delivery or a distribution network requires to solve several tens to hundreds cities Traveling Salesman Problems (TSP) (1)(2) within interactive response time, with expert-level accuracy (less than about 3% of error rate). To me...
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Veröffentlicht in: | Denki Gakkai ronbunshi. C, Erekutoronikusu, joho kogaku, shisutemu Information and Systems, 2010/05/01, Vol.130(5), pp.766-774 |
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container_title | Denki Gakkai ronbunshi. C, Erekutoronikusu, joho kogaku, shisutemu |
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creator | Sakurai, Yoshitaka Onoyama, Takashi Tsukamoto, Natsuki Takada, Kouhei Tsuruta, Setsuo |
description | A delivery route optimization that improves the efficiency of real time delivery or a distribution network requires to solve several tens to hundreds cities Traveling Salesman Problems (TSP) (1)(2) within interactive response time, with expert-level accuracy (less than about 3% of error rate). To meet these requirements, a multi-inner-world Genetic Algorithm (Miw-GA) method is developed. This method combines several types of GA's inner worlds. Each world of this method uses a different type of heuristics such as a 2-opt type mutation world and a block (Nearest Insertion) type mutation world. Comparison based on the results of experiments proved the method is superior to others and our previously proposed method. |
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subjects | delivery route optimization distribution networks Genetic Algorithm (GA) Heuristics Traveling Salesman Problems (TSP) |
title | A Multi-Inner-World Genetic Algorithm Using Multiple Heuristics to Optimize Delivery Schedule |
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