A hybrid PSO-GA algorithm for job shop scheduling in machine tool production

In our previous research applied to the job shop scheduling problem (JSSP) for machine tool production, the multi-objective optimisation model based on the particle swarm optimization (PSO) research had an imbalance performance between the convergence rate and the convergence precision. In this arti...

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Bibliographic Details
Published in:International journal of production research 2015-10, Vol.53 (19), p.5755-5781
Main Authors: Liu, Li-Lan, Hu, Rong-Song, Hu, Xiang-Ping, Zhao, Gai-Ping, Wang, Sen
Format: Article
Language:English
Subjects:
Algorithms
Convergence
generic algorithm (GA)
Genetic algorithms
job shop scheduling problem (JSSP)
Job shops
Machine tool industry
Machine tools
makespan
Mathematical models
Operators
Optimization
Optimization algorithms
particle swarm optimization (PSO)
Production scheduling
simulated annealing (SA)
Studies
Swarm intelligence
tardiness
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Summary:In our previous research applied to the job shop scheduling problem (JSSP) for machine tool production, the multi-objective optimisation model based on the particle swarm optimization (PSO) research had an imbalance performance between the convergence rate and the convergence precision. In this article, a new hybrid algorithm using PSO and generic algorithm (GA) is proposed to solve this particular problem. In this new algorithm, named hybrid PSO-GA algorithm (HPGA), the PSO algorithm is redefined and modified by introducing genetic operators, i.e. the crossover operator and the mutation operator, to update the particles in the population. The HPGA is then applied in heavy machinery company with minimising machines' makespan and minimising jobs' tardiness as the two optimal objectives. The comparisons with actual application report have illustrated that the proposed HPGA can obtain higher quality of schedule solution for machine tool production. Furthermore, with solution quality and convergence rate as the two estimation measurements metrics, some comparisons are performed in order to illustrate that the HPGA has superiority over the PSO, GA and simulated annealing algorithm (SA). Results have indicated that, with the combination of the merits of PSO and GA, the proposed HPGA approach can achieve not only better solution quality but also faster convergence rate than the PSO, GA and SA, within a reasonable computation time for high dimensions JSSP.
ISSN:0020-7543
1366-588X
DOI:10.1080/00207543.2014.994714