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A Genetic Programming Framework for Heuristic Generation for the Job-Shop Scheduling Problem

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Abstract

The Job-Shop Scheduling problem is a combinatorial optimization problem present in many real-world applications. It has been tackled with a colorful palette of techniques from different paradigms. Particularly, hyper-heuristics have attracted the attention of researchers due to their promising results in various optimization scenarios, including job-shop scheduling. In this study, we describe a Genetic-Programming-based Hyper-heuristic approach for automatically producing heuristics (dispatching rules) when solving such a problem. To do so, we consider a set of features that characterize the jobs within a scheduling instance. By using these features and a set of mathematical functions that create interactions between such features, we facilitate the construction of new heuristics. We present empirical evidence that heuristics produced by our approach are competitive. This conclusion arises from comparing the makespan of schedules obtained from our proposed method against those of some standard heuristics, over a set of synthetic Job-Shop Scheduling problem instances.

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References

  1. Adams, J., Balas, E., Zawack, D.: The shifting bottleneck procedure for job shop scheduling. Manage. Sci. 34(3), 391–401 (1988)

    Article  MathSciNet  Google Scholar 

  2. Balas, E., Vazacopoulos, A.: Guided local search with shifting bottleneck for job shop scheduling. Manage. Sci. 44(2), 262–275 (1998)

    Article  Google Scholar 

  3. 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). https://doi.org/10.1016/j.cie.2017.09.042

    Article  Google Scholar 

  4. Bratley, P., Fox, B.L., Schrage, L.E.: A Guide to Simulation. Springer Science & Business Media, Berlin (2011)

    MATH  Google Scholar 

  5. Burke, E.K., Hyde, M.R., Kendall, G.: Providing a memory mechanism to enhance the evolutionary design of heuristics. In: IEEE Congress on Evolutionary Computation, pp. 1–8. IEEE (2010)

    Google Scholar 

  6. Burke, E.K., Hyde, M.R., Kendall, G., Woodward, J.: Automatic heuristic generation with genetic programming: evolving a jack-of-all-trades or a master of one. In: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pp. 1559–1565 (2007)

    Google Scholar 

  7. Chaurasia, S.N., Sundar, S., Jung, D., Lee, H.M., Kim, J.H.: An evolutionary algorithm based hyper-heuristic for the job-shop scheduling problem with no-wait constraint. In: Yadav, N., Yadav, A., Bansal, J.C., Deep, K., Kim, J.H. (eds.) Harmony Search and Nature Inspired Optimization Algorithms. AISC, vol. 741, pp. 249–257. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0761-4_25

    Chapter  Google Scholar 

  8. Chong, C.S., Low, M.Y.H., Sivakumar, A.I., Gay, K.L.: A bee colony optimization algorithm to job shop scheduling. In: Proceedings of the 2006 Winter Simulation Conference, pp. 1954–1961. Winter Simulatrion Conference, Monterey, California December 2006. https://doi.org/10.1109/WSC.2006.322980

  9. Cowling, P., Kendall, G., Soubeiga, E.: A hyperheuristic approach to scheduling a sales summit. In: Burke, E., Erben, W. (eds.) PATAT 2000. LNCS, vol. 2079, pp. 176–190. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-44629-X_11

    Chapter  Google Scholar 

  10. Crowston, W.B., Glover, F., Trawick, J.D., et al.: Probabilistic and Parametric Learning Combinations of Local Job Shop Scheduling Rules. Technical report, Carnegie inst of tech pittsburgh pa graduate school of industrial administration (1963)

    Book  Google Scholar 

  11. Cruz-Duarte, J.M., Ivan, A., Ortiz-Bayliss, J.C., Conant-Pablos, S.E., Terashima-Marín, H.: A primary study on hyper-heuristics to customise metaheuristics for continuous optimisation. In: 2020 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8 (2020)

    Google Scholar 

  12. 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, pp. 350–359. Springer International Publishing, Cham (2020)

    Google Scholar 

  13. Fattahi, P., Messi Bidgoli, M., Samouei, P.: An improved tabu search algorithm for job shop scheduling problem trough hybrid solution representations. J. Qual Eng. Product. Optim. 3(1), 13–26 (2018). https://doi.org/10.22070/jqepo.2018.1360.1035

  14. Fisher, H.: Probabilistic learning combinations of local job-shop scheduling rules. Ind. Sched. 225–251 (1963)

    Google Scholar 

  15. 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. IEEE (June 2019). https://doi.org/10.1109/CEC.2019.8790296, https://ieeexplore.ieee.org/document/8790296/

  16. Grendreau, M., Potvin, J.: Handbook of Metaheuristics. International Series in Operations Research & Management Science, vol. 146 (2010)

    Google Scholar 

  17. Blackstone, J.H., Phillips, D.T., Hogg, G.: A state-of-the-art survey of dispatching rules for manufacturing job shop operations. Int. J. Product. Res. 20, 27–45 (1982)

    Article  Google Scholar 

  18. Huang, K.L., Liao, C.J.: Ant colony optimization combined with taboo search for the job shop scheduling problem. Comput. Oper. Res. 35(4), 1030–1046 (2008)

    Article  MathSciNet  Google Scholar 

  19. dao-er ji, R.Q., Wang, Y.: A new hybrid genetic algorithm for job shop scheduling problem. Comput. Oper. Res. 39(10), 2291–2299 (2019). https://doi.org/10.1016/j.cor.2011.12.005

    Article  MathSciNet  MATH  Google Scholar 

  20. Koza, J.R., Koza, J.R.: Genetic Programming: on the Programming of Computers by Means of Natural Selection, vol. 1. MIT press, Cambridge (1992)

    MATH  Google Scholar 

  21. Lara-Cárdenas, E., Sánchez-Díaz, X., Amaya, I., Ortiz-Bayliss, J.C.: 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, pp. 150–161. Springer International Publishing, Cham (2019)

    Chapter  Google Scholar 

  22. Lin, J.: Backtracking search based hyper-heuristic for the flexible job-shop scheduling problem with fuzzy processing time. Eng. Appl. Artif. Intell. 77186–196, (2019). https://doi.org/10.1016/j.engappai.2018.10.008

  23. Masood, A., Mei, Y., Chen, G., Zhang, M.: Many-objective genetic programming for job-shop scheduling. In: 2016 IEEE Congress on Evolutionary Computation (CEC), pp. 209–216. IEEE, Vancouver, Canada (July 2016). https://doi.org/10.1109/CEC.2016.7743797

  24. Miyashita, K.: Job-shop scheduling with genetic programming. In: Proceedings of the 2nd Annual Conference on Genetic and Evolutionary Computation, pp. 505–512. GECCO 2000, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA (2000)

    Google Scholar 

  25. Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Genetic programming for job shop scheduling. In: Bansal, J.C., Singh, P.K., Pal, N.R. (eds.) Evolutionary and Swarm Intelligence Algorithms. SCI, vol. 779, pp. 143–167. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91341-4_8

    Chapter  Google Scholar 

  26. Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Manag. Sci. 42(6), 797–813 (1996)

    Article  Google Scholar 

  27. Sánchez, M., Cruz-Duarte, J.M., Ortiz-Bayliss, J.C., Ceballos, H., Terashima-Marín, H., Amaya, I.: A systematic review of hyper-heuristics on combinatorial optimization problems. IEEE Access 8(1), 1–28 (2020). https://doi.org/10.1109/access.2020.3009318

    Article  Google Scholar 

  28. Sha, D., Hsu, C.Y.: A hybrid particle swarm optimization for job shop scheduling problem. Comput. Ind. Eng. 51(4), 791–808 (2006)

    Article  Google Scholar 

  29. Taillard, E.: Benchmarks for basic scheduling problems. Euro. J. Oper. Res. 64(2), 278–285 (1993). https://doi.org/10.1016/0377-2217(93)90182-M, project Management and Scheduling

  30. Türkyılmaz, A., Şenvar, Ö., Ünal, I., Bulkan, S.: A research survey: heuristic approaches for solving multi objective flexible job shop problems. J. Intell. Manufact. February 2020. https://doi.org/10.1007/s10845-020-01547-4, http://link.springer.com/10.1007/s10845-020-01547-4

  31. Uckun, S., Bagchi, S., Kawamura, K., Miyabe, Y.: Managing genetic search in job shop scheduling. IEEE Intell. Syst. 8(5), 15–24 (1993)

    Google Scholar 

  32. Wang, L., Cai, J.C, Ming, L.: An adaptive multi-population genetic algorithm for job-shop scheduling problem. Adv. Manufact. 1–8 (2016). https://doi.org/10.1007/s40436-016-0140-y

  33. Wang, L., Zheng, D.Z.: An effective hybrid optimization strategy for job-shop scheduling problems. Comput. Oper. Res. 28(6), 585–596 (2001)

    Article  MathSciNet  Google Scholar 

  34. Yska, D., Mei, Y., Zhang, M.: Feature construction in genetic programming hyper-heuristic for dynamic flexible job shop scheduling. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion on - GECCO 2018, pp. 149–150. ACM Press, New York, USA (2018). https://doi.org/10.1145/3205651.3205741

  35. Zhang, J., Ding, G., Zou, Y., Qin, S., Fu, J.: Review of job shop scheduling research and its new perspectives under Industry 4.0. J. Intell. Manufact. 30(4), 1809–1830 (2017). https://doi.org/10.1007/s10845-017-1350-2

    Article  Google Scholar 

  36. Zhou, Y., Yang, J.J., Zheng, L.Y.: Hyper-Heuristic Coevolution of Machine Assignment and Job Sequencing Rules for Multi-Objective Dynamic Flexible Job Shop Scheduling. IEEE Access 7, 68–88 (2019). https://doi.org/10.1109/ACCESS.2018.2883802

    Article  Google Scholar 

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Acknowledgments

This research was partially supported by CONACyT Basic Science Project under grant 287479 and ITESM Research Group with Strategic Focus on Intelligent Systems.

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Authors and Affiliations

  1. Tecnologico de Monterrey, School of Engineering and Sciences, Av. Eugenio Garza Sada 2501, 64849, Monterrey, NL, Mexico

    E. Lara-Cárdenas, X. Sánchez-Díaz, I. Amaya, J. M. Cruz-Duarte & J. C. Ortiz-Bayliss

Authors
  1. E. Lara-Cárdenas
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  2. X. Sánchez-Díaz
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  3. I. Amaya
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  4. J. M. Cruz-Duarte
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  5. J. C. Ortiz-Bayliss
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Corresponding author

Correspondence to J. C. Ortiz-Bayliss .

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Editors and Affiliations

  1. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Lourdes Martínez-Villaseñor

  2. Universidad Autónoma Metropolitana, Mexico City, Mexico

    Oscar Herrera-Alcántara

  3. Facultad de Ingeniería, Universidad Panamericana, Mexico City, Mexico

    Hiram Ponce

  4. Universidad Autónoma del Estado de Hidalgo, Hidalgo, Mexico

    Félix A. Castro-Espinoza

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Lara-Cárdenas, E., Sánchez-Díaz, X., Amaya, I., Cruz-Duarte, J.M., Ortiz-Bayliss, J.C. (2020). A Genetic Programming Framework for Heuristic Generation for the Job-Shop Scheduling Problem. In: Martínez-Villaseñor, L., Herrera-Alcántara, O., Ponce, H., Castro-Espinoza, F.A. (eds) Advances in Soft Computing. MICAI 2020. Lecture Notes in Computer Science(), vol 12468. Springer, Cham. https://doi.org/10.1007/978-3-030-60884-2_21

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  • DOI: https://doi.org/10.1007/978-3-030-60884-2_21

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