Abstract
Job Shop Scheduling problems have become popular because of their many industrial and practical applications. Among the many solving strategies for this problem, selection hyper-heuristics have attracted attention due to their promising results in this and similar optimization problems. A selection hyper-heuristic is a method that determines which heuristic to apply at given points of the problem throughout the solving process. Unfortunately, results from previous studies show that selection hyper-heuristics are not free from making wrong choices. Hence, this paper explores a novel way of improving selection hyper-heuristics by using neural networks that are trained with information from existing selection hyper-heuristics. These networks learn high-level patterns that result in improved performance concerning the hyper-heuristics they were generated from. At the end of the process, the neural networks work as hyper-heuristics that perform better than their original counterparts. The results presented in this paper confirm the idea that we can refine existing hyper-heuristics to the point of being able to defeat the best possible heuristic for each instance. For example, one of our experiments generated one hyper-heuristic that produced a schedule that reduced the makespan of the one obtained by a synthetic oracle by ten days.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kurdi, M.: An effective new island model genetic algorithm for job shop scheduling problem. Comput. Oper. Res. 67, 132–142 (2016)
Hernández-Ramírez, L., Frausto Solís, J., Castilla-Valdez, G., González-Barbosa, J.J., Terán-Villanueva, D., Morales-Rodríguez, M.L.: A hybrid simulated annealing for job shop scheduling problem. Int. J. Comb. Optimiz. Probl. Inform. 10, 6–15 (2018)
van Laarhoven, P.J.M., Aarts, E.H.L., Lenstra, J.K.: Job shop scheduling by simulated annealing. Oper. Res. 40, 113–125 (1992)
Satake, T., Morikawa, K., Takahashi, K., Nakamura, N.: Simulated annealing approach for minimizing the makespan of the general job-shop. Int. J. Prod. Econ. 60–61, 515–522 (1999)
Bozejko, W., Gnatowski, A., Pempera, J., Wodecki, M.: Parallel tabu search for the cyclic job shop scheduling problem. Comput. Ind. Eng. 113, 512–524 (2017)
Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Manag. Sci. 42, 797–813 (1996)
Zhang, C., Li, P., Guan, Z., Rao, Y.: A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem. Comput. Oper. Res. 34, 3229–3242 (2007)
Bhatt, N., Chauhan, N.R.: Genetic algorithm applications on job shop scheduling problem: a review. In: International Conference on Soft Computing Techniques and Implementations (ICSCTI), pp. 7–14 (2015)
Ghedjati, F.: Genetic algorithms for the job-shop scheduling problem with unrelated parallel constraints: heuristic mixing method machines and precedence. Comput. Ind. Eng. 37, 39–42 (1999)
Hou, S., Liu, Y., Wen, H., Chen, Y.: A self-crossover genetic algorithm for job shop scheduling problem. In: IEEE International Conference on Industrial Engineering and Engineering Management, pp. 549–554 (2011)
Blackstone, J.H., Phillips, D.T., Hogg, G.L.: A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int. J. Prod. Res. 20, 27–45 (1982)
Adams, J., Balas, E., Zawack, D.: The shifting bottleneck procedure for job shop scheduling. Manag. Sci. 34, 391–401 (1988)
Epstein, S.L., Freuder, E.C., Wallace, R., Morozov, A., Samuels, B.: The adaptive constraint engine. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 525–540. Springer, Heidelberg (2002). https://doi.org/10.1007/3-540-46135-3_35
Petrovic, S., Qu, R.: Case-based reasoning as a heuristic selector in a hyper-heuristic for course timetabling problems. In: Proceedings of the 6th International Conference on Knowledge-Based Intelligent Information Engineering Systems and Applied Technologies, KES 2002, vol. 82, pp. 336–340 (2002)
O’Mahony, E., Hebrard, E., Holland, A., Nugent, C., O’Sullivan, B.: Using case-based reasoning in an algorithm portfolio for constraint solving. In: Irish Conference on Artificial Intelligence and Cognitive Science, pp. 210–216 (2008)
Ortiz-Bayliss, J.C., Terashima-Marín, H., Conant-Pablos, S.E.: Combine and conquer: an evolutionary hyper-heuristic approach for solving constraint satisfaction problems. Artif. Intell. Rev. 46, 327–349 (2016)
Sim, K., Hart, E., Paechter, B.: A lifelong learning hyper-heuristic method for bin packing. Evol. Comput. 23, 37–67 (2015)
Malitsky, Y.: Evolving instance-specific algorithm configuration. In: Malitsky, Y. (ed.) Instance-Specific Algorithm Configuration, pp. 93–105. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11230-5_9
Zhao, F., Zhang, J., Zhang, C., Wang, J.: An improved shuffled complex evolution algorithm with sequence mapping mechanism for job shop scheduling problems. Expert Syst. Appl. 42, 3953–3966 (2015)
Peng, B., Lü, Z., Cheng, T.: A tabu search/path relinking algorithm to solve the job shop scheduling problem. Comput. Oper. Res. 53, 154–164 (2015)
Cheng, T.C.E., Peng, B., Lü, Z.: A hybrid evolutionary algorithm to solve the job shop scheduling problem. Ann. Oper. Res. 242, 223–237 (2016)
Gao, L., Li, X., Wen, X., Lu, C., Wen, F.: A hybrid algorithm based on a new neighborhood structure evaluation method for job shop scheduling problem. Comput. Ind. Eng. 88, 417–429 (2015)
Neyshabur, B., Bhojanapalli, S., McAllester, D., Srebro, N.: Exploring generalization in deep learning. In: Advances in Neural Information Processing Systems, pp. 5947–5956 (2017)
Olson, M., Wyner, A., Berk, R.: Modern neural networks generalize on small data sets. In: Advances in Neural Information Processing Systems, pp. 3619–3628 (2018)
Ortiz-Bayliss, J.C., Terashima-Marín, H., Conant-Pablos, S.E.: Neural networks to guide the selection of heuristics within constraint satisfaction problems. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Ben-Youssef Brants, C., Hancock, E.R. (eds.) MCPR 2011. LNCS, vol. 6718, pp. 250–259. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21587-2_27
Tyasnurita, R., Özcan, E., John, R.: Learning heuristic selection using a time delay neural network for open vehicle routing. In: IEEE Congress on Evolutionary Computation (CEC), pp. 1474–1481 (2017)
Li, J., Burke, E.K., Qu, R.: Integrating neural networks and logistic regression to underpin hyper-heuristic search. Knowl.-Based Syst. 24, 322–330 (2011)
Ortiz-Bayliss, J.C., Terashima-Marín, H., Conant-Pablos, S.E.: A neuro-evolutionary hyper-heuristic approach for constraint satisfaction problems. Cogn. Comput. 8, 429–441 (2016)
Taillard, E.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64, 278–285 (1993)
Garza-Santisteban, F., et al.: A simulated annealing hyper-heuristic for job shop scheduling problems. In: 2019 IEEE Congress on Evolutionary Computation (CEC), pp. 57–64 (2019)
Acknowledgment
This research was partially supported by CONACyT Basic Science Project under grant 287479 and ITESM Research Group with Strategic Focus on Intelligent Systems.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Lara-Cárdenas, E., Sánchez-Díaz, X., Amaya, I., Ortiz-Bayliss, J.C. (2019). Improving Hyper-heuristic Performance for Job Shop Scheduling Problems Using Neural Networks. In: Martínez-Villaseñor, L., Batyrshin, I., Marín-Hernández, A. (eds) Advances in Soft Computing. MICAI 2019. Lecture Notes in Computer Science(), vol 11835. Springer, Cham. https://doi.org/10.1007/978-3-030-33749-0_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-33749-0_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-33748-3
Online ISBN: 978-3-030-33749-0
eBook Packages: Computer ScienceComputer Science (R0)