Paper Info
Title
Solving Flexible Job Shop Scheduling Problem Using a Discrete Particle Swarm Optimization with Iterated Local Search.
Abstract
Considering tri-objective flexible job shop scheduling problem (FJSP), multi-objective-based discrete particle swarm optimizer (MOPSO) integrating iterated local search is presented to search the optimal scheduling. First of all, three discrete operators are embedded in MOPSO to produce new particles with a probability. Then global-best set and self-best sets are defined to obtain global-best position and self-best positions. Thirdly, iterated local search integrating two neighborhoods is introduced to search the neighborhoods of the global-best set. Evaluated on Kacem instances, MOPSO show its validity for solving FJSP.
Year
DOI
Venue
2016
10.1007/978-981-10-2663-8_62
Communications in Computer and Information Science
Keywords
Field
DocType
Multi-objective problem,Discrete particle swarm optimization,Iterated local search,Flexible job shop scheduling
Particle swarm optimization,Mathematical optimization,Job shop scheduling,Scheduling (computing),Flow shop scheduling,Control engineering,Multi-swarm optimization,Operator (computer programming),Engineering,Iterated local search,Metaheuristic
Conference
Volume
ISSN
Citations 
643
1865-0929
0
PageRank 
References 
Authors
0.34
10
4
Name
Order
Citations
PageRank
Song Huang100.34
Na Tian201.01
Yan Wang35412.95
Zhicheng Ji4347.59