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

共 65 条

[21]

Deb K., 2001, MULTIOBJECTIVE OPTIM, V16

[22] Ant colony optimization -: Artificial ants as a computational intelligence technique [J].

Dorigo, Marco ;

Birattari, Mauro ;

Stuetzle, Thomas .

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, 2006, 1 (04) :28-39

[23] Multi-Objective Optimization for the m-PDPTW: Aggregation Method With Use of Genetic Algorithm and Lower Bounds [J].

Dridi, I. Harbaoui ;

Kammarti, R. ;

Ksouri, M. ;

Borne, P. .

INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 2011, 6 (02) :246-257

[24] The dynamic vehicle routing problem: Solution with hybrid metaheuristic approach [J].

Euchi, Jalel ;

Yassine, Adnan ;

Chabchoub, Habib .

SWARM AND EVOLUTIONARY COMPUTATION, 2015, 21 :41-53

[25] Dynamic multiobjective optimization problems: Test cases, approximations, and applications [J].

Farina, M ;

Deb, K ;

Amato, P .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2004, 8 (05) :425-442

[26] An improved multi-objective evolutionary algorithm for the vehicle routing problem with time windows [J].

Garcia-Najera, Abel ;

Bullinaria, John A. .

COMPUTERS & OPERATIONS RESEARCH, 2011, 38 (01) :287-300

[27] A multi-objective dynamic vehicle routing problem with fuzzy time windows: Model, solution and application [J].

Ghannadpour, Syed Farid ;

Noori, Simak ;

Tavakkoli-Moghaddam, Reza ;

Ghoseiri, Keivan .

APPLIED SOFT COMPUTING, 2014, 14 :504-527

[28]

Goldberg DE., 1989, GENETIC ALGORITHMS S, V1

[29] The Multi-Objective Multi-Vehicle Pickup and Delivery Problem with Time Windows [J].

Grandinetti, L. ;

Guerriero, F. ;

Pezzella, F. ;

Pisacane, O. .

TRANSPORTATION: CAN WE DO MORE WITH LESS RESOURCES? - 16TH MEETING OF THE EURO WORKING GROUP ON TRANSPORTATION - PORTO 2013, 2014, 111 :203-212

[30] A dynamic vehicle routing problem with time-dependent travel times [J].

Haghani, A ;

Jung, S .

COMPUTERS & OPERATIONS RESEARCH, 2005, 32 (11) :2959-2986

← 1 2 3 4 5 6 7 →