DocumentCode :
498288
Title :
CPSO-Based Hybrid Approach for Scheduling Parallel Machines with Processing Set Restrictions
Author :
Jinghua, Hao ; Min, Liu ; Cheng, Wu
Author_Institution :
Dept. of Autom., Tsinghua Univ., Beijing, China
Volume :
2
fYear :
2009
fDate :
19-21 May 2009
Firstpage :
8
Lastpage :
13
Abstract :
We consider identical parallel machine scheduling problem where every job is only allowed to be processed on a specified subset of machines. We develop an efficient CPSO (chaotic particle swarm optimization) based hybrid approach to solve the above problem with the objective of minimizing total number of tardy jobs. In the proposed approach, the problem is firstly decomposed into a machine assignment subproblem and a job sequencing subproblem, then the job sequencing subproblem is solved optimally by a heuristic algorithm with the time complexity of O(mnlogn), and the machine assignment subproblem is solved by the proposed CPSO algorithm, in which the problem characteristics are incorporated to enhance the performance of exploration and exploitation in the proposed algorithm. Numerical computations show that the proposed hybrid approach outperforms several other methods on the studied problems.
Keywords :
computational complexity; parallel machines; particle swarm optimisation; processor scheduling; chaotic particle swarm optimization; heuristic algorithm; job sequencing subproblem; machine assignment subproblem; numerical computations; parallel machines scheduling; set restriction processing; time complexity; Chaos; Genetic algorithms; Heuristic algorithms; Job shop scheduling; Manufacturing; Parallel machines; Particle swarm optimization; Polynomials; Processor scheduling; Scheduling algorithm; chaotic; particle swarm optimization; processing set restriction; scheduling;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Systems, 2009. GCIS '09. WRI Global Congress on
Conference_Location :
Xiamen
Print_ISBN :
978-0-7695-3571-5
Type :
conf
DOI :
10.1109/GCIS.2009.179
Filename :
5209120
Link To Document :
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