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

被引：65

作者：

Zhu, Zexuan ^{[1
]}

Xiao, Jun ^{[1
]}

He, Shan ^{[2
]}

Ji, Zhen ^{[1
]}

Sun, Yiwen ^{[3
]}

机构：

[1] Shenzhen Univ, Coll Comp Sci & Software Engn, Shenzhen 518060, Peoples R China

[2] Univ Birmingham, Sch Comp Sci, Birmingham B15 2TT, W Midlands, England

[3] Shenzhen Univ, Dept Biomed Engn, Shenzhen 518060, Peoples R China

来源：

INFORMATION SCIENCES | 2016年 / 329卷

基金：

中国国家自然科学基金;

关键词：

Memetic algorithm; Multi-objective evolutionary algorithm; Dynamic pickup and delivery problem; Locality-sensitive hashing; VEHICLE-ROUTING PROBLEM; ADAPTIVE PREDICTIVE CONTROL; EVOLUTIONARY ALGORITHMS; GENETIC ALGORITHM; OPTIMIZATION; MODEL; SELECTION;

D O I：

10.1016/j.ins.2015.09.006

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2015 Elsevier Inc. All rights reserved.

引用

页码：73 / 89

页数：17

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