A multi-inner-world genetic algorithm using multiple heuristics to optimize delivery schedule

Authors:
Yoshitaka Sakurai;Setsuo Tsuruta;Takashi Onoyama;Sen Kubota
Affiliations:
School of Information Environment, Tokyo Denki University, Chiba, Japan;School of Information Environment, Tokyo Denki University, Chiba, Japan;Research & Development Department, Hitachi Software Engineering Co.,Ltd., Tokyo, Japan;Research & Development Department, Hitachi Software Engineering Co.,Ltd., Tokyo, Japan
Venue:
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
Year:
2009

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Abstract

Building a delivery route optimization system that improves the delivery efficiency in real time requires to solve several tens to hundreds cities Traveling Salesman Problems (TSP) within interactive response time, with expert-level accuracy (less than 3% of errors). 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 1000 times experiments proved the method is superior to others.