Research Article | OPEN ACCESS
A Comparison of Genetic Algorithm and Particle Swarm Optimisation to Minimize the Makespan for Parallel Line Job Shop Scheduling
P. Ravichandran, K. Krishnamurthy and B. Meenakshipriya
Department of Mechatronics Engineering, Kongu Engineering College, Erode, Tamilnadu, India
Research Journal of Applied Sciences, Engineering and Technology 2013 5:409-415
Received: April 2, 2016 | Accepted: May 23, 2016 | Published: September 05, 2016
Abstract
This paper describes the implementation of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm to minimize the total completion time of jobs called makespan in parallel line Job Shop Scheduling Problem (PJSSP). PJSSP is one of the scheduling methods in manufacturing industry for increasing plant utilization, reducing cycle time and to find the optimal allocation of jobs in multiple processing lines. Each job is assigned to a particular line and the job should be completed only in that assigned line. Also, the job should be processed in a particular order. PJSSP is always a challenging task in the combinatorial research and it requires a heuristic approach. Results show that the performance of PSO is superior to GA in order to find optimal solution with minimum makespan for PJSSP.
Keywords:
Genetic Algorithm (GA), makespan, Parallel line Job Shop Scheduling Problem (PJSSP), Particle Swarm Optimisation (PSO) , Processing time , Setup time,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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The authors have no competing interests.
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