Abstract
FJSP provides the better solution for job scheduling at the given set of machines, but as the rapidly increasing environment of technology, it becomes complex to maintain and schedule the machines according to the optimal solution in a particular amount of time. In this paper, FJSP (flexible job shops scheduling problem) for the given set of the machine with the PSO (Particle Swarm Optimization) for optimization of it with mutation operator; the target is to reduce the makespan. Particle swarm optimization rule of the flexible job shop scheduling problem with mutation operator is introducing dissimilarity within the searching and scheduling procedure. Once the modification of the total records tends to decrease, the mutation method can begin. The proposed algorithm is implemented on DP data which shows a better result.
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References
Muth JF, Thompson GL (1963) Industrial scheduling. New Jersey, Englewood Cliffs
Dave M, Choudhary K (2016) Job shop scheduling algorithms—a shift from traditional techniques to non-traditional techniques. In: Proceedings of the INDIA Com-2016, IEEE conference ID: 37465, ISSN 0973-7529, 3 rd international conference on “computing for sustainable global development, Mar 2016
Kacem I, Hammadi S, Borne P (2002) Approach by localization and multi-objective evolutionary optimization for flexible job-shop scheduling problems. IEEE Trans Syst, Man, Cybern 32:1–13
Xing LN, Chen YW, Wang P, Zhao QS, Xiong J (2014) A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl Soft Comput:888–96
Dave M, Choudhary K (2009) Particle swarm optimization with the mutation for flexible job shop scheduling problem. IJEE 9:16–21. ISSN-09737383
Dauzere P, Pauli E (1997) An integrated approach for modelling and solving the general multi-processor job shop scheduling problem using tabu search. Ann Oper Res:281–306
Brandimarte P (1993) Routing and scheduling in a flexible job shop by tabu search. Ann Oper Res:157–183
Ruiz R, Maroto C (2006) A genetic algorithm for hybrid flow-shops with sequence dependent setup times and machine eligibility. Eur J Oper Res:781–800
Saidi M, Fattahi P (2007) Flexible job shop scheduling with tabu search algorithms. Int J Adv Manuf Technol:563–570
Zhang G, Shao X, Li P, Gao L (2009) An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem. Comput Ind Eng:1309–1318
Garey MR, Johnson DS, Sethi R (1976) Complexity of fellowship and job shop scheduling. Math Oper Res:117–129
Mastrolilli M, Gambardella M (2000) Effective neighbourhood functions for the flexible job shop problem. J Scheduling:3–20
Gao J, Sun L, Gen M (2008) A hybrid genetic and variable neighbourhood descent algorithm for flexible job shop scheduling problems. Comput Oper Res: 2892–2907
Zhang GH, Gao L, Shi Y (2001) An effective genetic algorithm for the flexible job-shop scheduling problem. Expert Syst Appl:3563–3573
Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neural network, pp 1942–1948. Washington, USA
Phanden RK, Jain A, Verma R (2012) A genetic algorithm-based approach for flexible job shop scheduling. Appl Mech Mater:3930–3937
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Choudhary, K., Gautam, G., Bharti, N., Rathore, V.S. (2019). Particle Swarm Optimization for Flexible Job Scheduling Problem with Mutation Strategy. In: Peng, SL., Dey, N., Bundele, M. (eds) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol 75. Springer, Singapore. https://doi.org/10.1007/978-981-13-7150-9_53
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DOI: https://doi.org/10.1007/978-981-13-7150-9_53
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