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

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 ob...

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Vydané v:Information sciences Ročník 329; s. 73 - 89
Hlavní autori: Zhu, Zexuan, Xiao, Jun, He, Shan, Ji, Zhen, Sun, Yiwen
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Inc 01.02.2016
Predmet:
Algorithms
Benchmarking
Dynamic pickup and delivery problem
Dynamics
Evolutionary
Locality-sensitive hashing
Memetic algorithm
Multi-objective evolutionary algorithm
Optimization
Rectification
Searching
Workload
Memetic algorithm
Multi-objective evolutionary algorithm
Locality-sensitive hashing
Dynamic pickup and delivery problem
ISSN:0020-0255, 1872-6291
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Shrnutí: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.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2015.09.006