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,

References

1. Bean, J.C., 1994. Genetic algorithms and random keys for sequencing and optimization. ORSA J. Comput., 6(2): 154-160.
   CrossRef    Direct Link
2. Behnamian, J., 2014. Particle swarm optimization-based algorithm for fuzzy parallel machine scheduling. Int. J. Adv. Manuf. Tech., 75(5): 883-895.
   Direct Link
3. Cheng, B., Q. Wang, S. Yang and X. Hu, 2013. An improved ant colony optimization for scheduling identical parallel batching machines with arbitrary job sizes. Appl. Soft Comput., 13(2): 765-772.
   Direct Link
4. Elmi, A., M. Solimanpur, S. Topaloglu and A. Elmi, 2011. A simulated annealing algorithm for the job shop cell scheduling problem with intercellular moves and reentrant parts. Comput. Ind. Eng., 61(1): 171-178.
   Direct Link
5. French S., 1982. Sequencing and Scheduling: An Introduction to the Mathematics of the Job-Shop. John Willey and Sons Inc., New York, USA.
   
6. Geyik, F. and I.H. Cedimoglu, 2004. The strategies and parameters of tabu search for job-shop scheduling. J. Intell. Manuf., 15(4): 439-448.
   Direct Link
7. Gromicho, J.A.S., J.J. Van Hoorn, F. Saldanha-da-Gama and G.T. Timmer, 2012. Solving the job-shop scheduling problem optimally by dynamic programming. Comput. Oper. Res., 39(12): 2968-2977.
   Direct Link
8. Haq, A.N., K. Balasubramanian, B. Sashidharan and R.B. Karthick, 2008. Parallel line job shop scheduling using genetic algorithm. Int. J. Adv. Manuf. Tech., 35(9): 1047-1052.
   Direct Link
9. Musharavati, F. and A.S.M. Hamouda, 2011. Modified genetic algorithms for manufacturing process planning in multiple parts manufacturing lines. Expert Syst. Appl., 38(9): 10770-10779.
   Direct Link
10. Panneerselvam, R., 2012. Production and Operations Management. 3rd Edn., PHI Learning Pvt. Ltd., New Delhi.
    
11. Solimanpur, M. and A. Elmi, 2013. A tabu search approach for cell scheduling problem with makespan criterion. Int. J. Prod. Econ., 141(2): 639-645.
    Direct Link
12. Tang, D. and M. Dai, 2015. Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem. Chin. J. Mech. Eng., 28(5): 1048-1055.
    Direct Link
13. Tasgetiren, M.F., Y.C. Liang, M. Sevkli and G. Gencyilmaz, 2007. A particle swarm optimization algorithm for makespan and total flowtime minimization in the permutation flowshop sequencing problem. Eur. J. Oper. Res., 177(3): 1930-1947.
    Direct Link
14. Zhang, L., L. Gao and X. Li, 2013. A hybrid intelligent algorithm and rescheduling technique for job shop scheduling problems with disruptions. Int. J. Adv. Manuf. Tech., 65(5): 1141-1156.
    Direct Link

Competing interests

The authors have no competing interests.

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The authors have no competing interests.