A multi-objective memetic algorithm based on locality-sensitive hashing for one-to-many-to-one dynamic pickup-and-delivery problem

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• 作者：Zexuan Zhu^a ; Jun Xiao^a ; Shan He^b ; Zhen Ji^a ; Yiwen Sun ; ^c ; ^{ywsun@szu.edu.cn" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Memetic algorithm ; Multi-objective evolutionary algorithm ; Dynamic pickup and delivery problem ; Locality-sensitive hashing
• 刊名：Information Sciences
• 出版年：2016
• 出版时间：1 February 2016
• 年：2016
• 卷：329
• 期：Complete
• 页码：73-89
• 全文大小：5227 K

文摘

This paper presents an early attempt to solve one-to-many-to-one dynamic pickup-and-delivery problem (DPDP) by proposing a multi-objective memetic algorithm called LSH-MOMA, which is a synergy of multi-objective evolutionary algorithm and locality-sensitive hashing (LSH) based local search. Three objectives namely route length, response time, and workload are optimized simultaneously in an evolutionary framework. In each generation of LSH-MOMA, LSH-based rectification and local search are imposed to repair and improve the individual solutions. LSH-MOMA is evaluated on four benchmark DPDPs and the experimental results show that LSH-MOMA is efficient in obtaining optimal tradeoff solutions of the three objectives.