Abstract
This paper presents a hybrid model of computational intelligence to solve the job shop scheduling problem, classified as full NP. It is proposed to solve it using the technique of ant colony assisted with simulated annealing. The ant colony technique was used as a global search strategy, and the simulated annealing technique was used as a local search strategy. This proposal was validated experimentally with test problems reported in the literature.
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References
Deng, Q., Gong, G., Gong, X., Zhang, L., Liu, W., Ren, Q.: A bee evolutionary guiding nondominated sorting genetic algorithm II for multiobjective flexible job-shop scheduling. Computational intelligence and neuroscience (2017)
Li, X., Gao, L.: An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem. Int. J. Prod. Econ. 174, 93–110 (2016)
Zhou, G., Zhou, Y., Zhao, R.: Hybrid social spider optimization algorithm with differential mutation operator for the job-shop scheduling problem. J. Industr. Manag. Optim. 13(5) (2019)
Keddari, N., Mebarki, N., Shahzad, A., Sari, Z.: Solving an integration process planning and scheduling in a flexible job shop using a hybrid approach. In: IFIP International Conference on Computational Intelligence and its Applications, pp. 387–398. Springer, Cham (2018, May)
Li, X., Peng, Z., Du, B., Guo, J., Xu, W., Zhuang, K.: Hybrid artificial bee colony algorithm with a rescheduling strategy for solving flexible job shop scheduling problems. Comput. Ind. Eng. 113, 10–26 (2017)
Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Genetic programming for job shop scheduling. In: Evolutionary and Swarm Intelligence Algorithms, pp. 143–167. Springer, Cham (2019)
Wang, H., Smys, S.: Soft computing strategies for optimized route selection in wireless sensor network. J. Soft Comput. Parad. (JSCP) 2(01), 1–12 (2020)
Nouri, H.E., Driss, O.B., Ghédira, K.: Solving the flexible job shop problem by hybrid metaheuristics-based multiagent model. J. Ind. Eng. Int. 14(1), 1–14 (2018)
Asadzadeh, L.: A parallel artificial bee colony algorithm for the job shop scheduling problem with a dynamic migration strategy. Comput. Ind. Eng. 102, 359–367 (2016)
Karimi, S., Ardalan, Z., Naderi, B., Mohammadi, M.: Scheduling flexible job-shops with transportation times: mathematical models and a hybrid imperialist competitive algorithm. Appl. Mathem. Modell. 41, 667–682 (2017)
Viloria, A., Sierra, D.M., de la Hoz, L., Bohórquez, M.O., Bilbao, O.R., Pichón, A.R., Hernández-Palma, H.: NoSQL database for storing historic records in monitoring systems: selection process. In: Advances in Intelligent Systems and Computing, vol. 1039, pp. 336–344. Springer (2020). https://doi.org/10.1007/978-3-030-30465-2_38
Bissoli, D.D.C., Amaral, A.R.: A hybrid iterated local search metaheuristic for the flexible job shop scheduling problem. In: 2018 XLIV Latin American Computer Conference (CLEI), pp. 149–157. IEEE (2018, October)
Sel, Ç., Hamzadayi, A.: A simulated annealing approach based simulation-optimisation to the dynamic job-shop scheduling problem. Pamukkale Univ. J. Eng. Sci 24(4) (2018)
Baykasoğlu, A., Karaslan, F.S.: Solving comprehensive dynamic job shop scheduling problem by using a GRASP-based approach. Int. J. Prod. Res. 55(11), 3308–3325 (2017)
Shahgholi Zadeh, M., Katebi, Y., Doniavi, A.: A heuristic model for dynamic flexible job shop scheduling problem considering variable processing times. Int. J. Prod. Res. 57(10), 3020–3035 (2019)
Viloria, A., Sierra, D.M., Duran, S.E., Rambal, E.P., Hernández-Palma, H., Ventura, J.M., Torres, L.J.J.: Optimization of flow shop scheduling through a hybrid genetic algorithm for manufacturing companies. In: Advances in Intelligent Systems and Computing, vol. 1039, pp. 20–29. Springer (2020). https://doi.org/10.1007/978-3-030-30465-2_3
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Varela, N., Zelama, N., Hernandez, R., de Avila Villalobos, J.R. (2021). Ant Colony Technique for Task Sequencing Problems in Industrial Processes. In: Pandian, A.P., Palanisamy, R., Ntalianis, K. (eds) Proceedings of International Conference on Intelligent Computing, Information and Control Systems. Advances in Intelligent Systems and Computing, vol 1272. Springer, Singapore. https://doi.org/10.1007/978-981-15-8443-5_61
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DOI: https://doi.org/10.1007/978-981-15-8443-5_61
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