Abstract
This paper focuses on the application of modern reinforcement learning techniques to solve the job shop scheduling problem where the objective is to minimize the makespan. It starts by discussing the proposed system, which is based on an original architecture that proposes a novel approach based on the cooperation of different components, from the problem that needs to be solved to the job shop training environment or the reinforcement learning algorithm. Then, a methodical computational study is presented. The computational study is divided into two phases of performance analysis, namely of efficiency and effectiveness. In the efficiency analysis the execution times until a solution is obtained by the studied approaches are compared. In the efficiency analysis the quality of the solutions proposed by the chosen methods of optimization are evaluated. Both phases included a meticulous statistical inference analysis to validate the obtained results. A detailed discussion of the collected results is also included in this paper. Based on the statistical evidence that was found, it culminates in the conclusion that the proposed approach examined and evaluated in this paper is most likely one of the most efficient to date to solve job shop scheduling problems.
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Cunha, B., Madureira, A., Fonseca, B.: Reinforcement Learning Environment for Job Shop Scheduling Problems. Int. J. Comput. Inf. Syst. Ind. Manag. Appl. 12, 231–238 (2020)
Cunha, B., Madureira, A. M., Fonseca, B., Coelho, D.: Deep reinforcement learning as a job shop scheduling solver: a literature review. In: Madureira, A. M., Abraham, A., Gandhi, N., Varela, M. L. (eds.) Hybrid Intelligent Systems: 18th International Conference on Hybrid Intelligent Systems (HIS 2018) Held in Porto, Portugal, December 13-15, 2018, pp. 350–359. Springer, Cham (2020)
Cook, S.A.: The complexity of theorem-proving procedures. In: Proceedings of the third annual ACM symposium on Theory of computing, pp. 151–158 (1971)
Cunha, B., Madureira, A., Pereira, J.P., Pereira, I.: Evaluating the effectiveness of bayesian and neural networks for adaptive schedulling systems. In: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016, pp. 1–6 (2017)
Ku, W.-Y., Beck, J.C.: Mixed integer programming models for job shop scheduling: a computational analysis. Comput. Oper. Res. 73, 165–173 (2016)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction, Second. The MIT Press, New York (2018)
Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems, 5th ed. Springer, Cham (2016)
Zhang, C.Y., Li, P., Rao, Y., Guan, Z.: A very fast TS/SA algorithm for the job shop scheduling problem. Comput. Oper. Res. 35(1), 282–294 (2008)
Peng, B., Lü, Z., Cheng, T.C.E.: A tabu search/path relinking algorithm to solve the job shop scheduling problem. Comput. Oper. Res. 53, 154–164 (2015)
Taillard, E.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64(2), 278–285 (1993)
Pardalos, P.M., Shylo, O.V.: An algorithm for the job shop scheduling problem based on global equilibrium search techniques. Comput. Manag. Sci. 3(4), 331–348 (2006)
Pardalos, P.M., Shylo, O.V., Vazacopoulos, A.: Solving job shop scheduling problems utilizing the properties of backbone and big valley. Comput. Optim. Appl. 47, 61–76 (2010)
van Hoorn, J.J.: The Current state of bounds on benchmark instances of the job-shop scheduling problem. J. Sched. 21(1), 127–128 (2018)
Vilím, P., Laborie, P., Shaw, P.: Failure-Directed Search for Constraint-Based Scheduling. In: CPAIOR (2015)
Siala, M., Artigues, C., Hebrard, E.: Two clause learning approaches for disjunctive scheduling. In: Pesant, G. (ed.) Principles and Practice of Constraint Programming: 21st International Conference, CP 2015, Cork, Ireland, August 31 -- September 4, 2015, Proceedings, pp. 393–402. Springer, Cham (2015)
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Cunha, B., Madureira, A., Fonseca, B. (2021). Solving the Job Shop Scheduling Problem with Reinforcement Learning: A Statistical Analysis. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_55
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DOI: https://doi.org/10.1007/978-3-030-71187-0_55
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