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Genetic Programming for Job Shop Scheduling

  • Su NguyenEmail author
  • Mengjie Zhang
  • Mark Johnston
  • Kay Chen Tan
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 779)

Abstract

Designing effective scheduling rules or heuristics for a manufacturing system such as job shops is not a trivial task. In the early stage, scheduling experts rely on their experiences to develop dispatching rules and further improve them through trials-and-errors, sometimes with the help of computer simulations. In recent years, automated design approaches have been applied to develop effective dispatching rules for job shop scheduling (JSS). Genetic programming (GP) is currently the most popular approach for this task. The goal of this chapter is to summarise existing studies in this field to provide an overall picture to interested researchers. Then, we demonstrate some recent ideas to enhance the effectiveness of GP for JSS and discuss interesting research topics for future studies.

Keywords

Genetic programming Job shop scheduling Heuristic 

References

  1. 1.
    Asano, M., Ohta, H.: A heuristic for job shop scheduling to minimize total weighted tardiness. Comput. Ind. Eng. 42, 137–147 (2002)CrossRefGoogle Scholar
  2. 2.
    Balas, E., Vazacopoulos, A.: Guided local search with shifting bottleneck for job shop scheduling. Manage. Sci. 44, 262–275 (1998)CrossRefGoogle Scholar
  3. 3.
    Banzhaf, W., Nordin, P., Keller, R., Francone, F.: Genetic Programming: An Introduction. Morgan Kaufmann, San Francisco (1998)CrossRefGoogle Scholar
  4. 4.
    Beni, G., Wang, J.: Swarm intelligence in cellular robotic systems. In: Dario, P., Sandini, G., Aebischer, P. (eds.) Robots and Biological Systems: Towards a New Bionics? NATO ASI Series, vol. 102, pp. 703–712. Springer, Berlin, Heidelberg (1993).  https://doi.org/10.1007/978-3-642-58069-7_38CrossRefGoogle Scholar
  5. 5.
    Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Artificial Systems. Oxford University Press, Inc., New York, NY, USA (1999). http://portal.acm.org/citation.cfm?id=328320
  6. 6.
    Branke, J., Hildebrandt, T., Scholz-Reiter, B.: Hyper-heuristic evolution of dispatching rules: a comparison of rule representations. Evol. Comput. (2014) (in press). ( https://doi.org/10.1162/EVCO_a_00131)
  7. 7.
    Burke, E.K., Hyde, M.R., Kendall, G., Ochoa, G., Ozcan, E., Woodward, J.R.: Exploring hyper-heuristic methodologies with genetic programming. In: Mumford, C., Jain, L. (eds.) Computational Intelligence, Intelligent Systems Reference Library, vol. 1, pp. 177–201. Springer, Berlin, Heidelberg (2009)Google Scholar
  8. 8.
    Cheng, V.H.L., Crawford, L.S., Menon, P.K.: Air traffic control using genetic search techniques. In: McClamroch, N.H., Sano, A., Gruebel, G. (eds.) In: Proceedings of the 1999 IEEE International Conference on Control Applications, vol. 1, pp. 249–254. IEEE Press, Piscataway, NJ (1999)Google Scholar
  9. 9.
    Chiang, T.C., Shen, Y.S., Fu, L.C.: A new paradigm for rule-based scheduling in the wafer probe centre. Int. J. Prod. Res. 46(15), 4111–4133 (2008)CrossRefGoogle Scholar
  10. 10.
    Dimopoulos, C., Zalzala, A.M.S.: Investigating the use of genetic programming for a classic one-machine scheduling problem. Adv. Eng. Softw. 32(6), 489–498 (2001)CrossRefGoogle Scholar
  11. 11.
    El-Bouri, A., Balakrishnan, S., Popplewell, N.: Sequencing jobs on a single machine: a neural network approach. Eur. J. Oper. Res. 126(3), 474–490 (2000)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Essafi, I., Mati, Y., Dauzère-Pérès, S.: A genetic local search algorithm for minimizing total weighted tardiness in the job-shop scheduling problem. Comput. Oper. Res. 35(8), 2599–2616 (2008)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, 2nd edn. Springer, Germany (2006)zbMATHGoogle Scholar
  14. 14.
    Garey, M.R., Johnson, D.S., Sethi, R.: The complexity of flowshop and jobshop scheduling. Math. Oper. Res. 1(2), 117–129 (1976)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Geiger, C.D., Uzsoy, R., Aytuğ, H.: Rapid modeling and discovery of priority dispatching rules: an autonomous learning approach. J. Sched. 9(1), 7–34 (2006)CrossRefGoogle Scholar
  16. 16.
    Giffler, B., Thompson, G.L.: Algorithms for solving production-scheduling problems. Oper. Res. 8(4), 487–503 (1960)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Goncalves, J.F., de Magalhaes Mendes, J.J., Resende, M.G.C.: A hybrid genetic algorithm for the job shop scheduling problem. Eur. J. Oper. Res. 167(1), 77–95 (2005)MathSciNetCrossRefGoogle Scholar
  18. 18.
    Hildebrandt, T., Heger, J., Scholz-Reiter, B.: Towards improved dispatching rules for complex shop floor scenarios—a genetic programming approach. In: Pelikan, M., Branke, J. (eds.) In: GECCO’10: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 257–264. ACM Press, New York (2010)Google Scholar
  19. 19.
    Hildebrandt, T., Branke, J.: On using surrogates with genetic programming. Technical Report, Warwick Business School (2014)Google Scholar
  20. 20.
    Holthaus, O., Rajendran, C.: Efficient jobshop dispatching rules: further developments. Prod. Plann. Control 11(2), 171–178 (2000)CrossRefGoogle Scholar
  21. 21.
    Hunt, R., Johnston, M., Zhang, M.: Evolving “less-myopic” scheduling rules for dynamic job shop scheduling with genetic programming. In: GECCO’14: Proceedings of Genetic and Evolutionary Computation Conference (2014) (to appear)Google Scholar
  22. 22.
    Ingimundardottir, H., Runarsson, T.P.: Supervised learning linear priority dispatch rules for job-shop scheduling. In: Coello Coello, C.A. (ed.) Learning and Intelligent Optimization, LNCS, vol. 6683, pp. 263–277. Springer, Berlin, Heidelberg (2011)CrossRefGoogle Scholar
  23. 23.
    Jakobović, D., Budin, L.: Dynamic scheduling with genetic programming. In: Collet, P., Tomassini, M., Ebner, M., Gustafson, S., Ekárt, A. (eds.) Genetic Programming, LNCS, vol. 3905, pp. 73–84. Springer, Berlin, Heidelberg (2006)CrossRefGoogle Scholar
  24. 24.
    Jakobović, D., Marasović, K.: Evolving priority scheduling heuristics with genetic programming. Appl. Soft Comput. 12(9), 2781–2789 (2012)CrossRefGoogle Scholar
  25. 25.
    Jayamohan, M.S., Rajendran, C.: New dispatching rules for shop scheduling: a step forward. Int. J. Prod. Res. 38, 563–586 (2000)CrossRefGoogle Scholar
  26. 26.
    Jayamohan, M.S., Rajendran, C.: Development and analysis of cost-based dispatching rules for job shop scheduling. Eur. J. Oper. Res. 157(2), 307–321 (2004)CrossRefGoogle Scholar
  27. 27.
    Jedrzejowicz, P., Ratajczak-Ropel, E.: Agent-based gene expression programming for solving the RCPSP/max problem. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds.) Adaptive and Natural Computing Algorithms. Lecture Notes in Computer Science, vol. 5495, pp. 203–212. Springer, Berlin, Heidelberg (2009)CrossRefGoogle Scholar
  28. 28.
    Johnston, M., Liddle, T., Zhang, M.: A relaxed approach to simplification in genetic programming. In: Esparcia-Alcázar, A.I., Ekárt, A., Silva, S., Dignum, S., Şima Uyar, A. (eds.) Genetic Programming, LNCS, vol. 6021, pp. 110–121. Springer, Berlin, Heidelberg (2010)CrossRefGoogle Scholar
  29. 29.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA (1992)zbMATHGoogle Scholar
  30. 30.
    Kreipl, S.: A large step random walk for minimizing total weighted tardiness in a job shop. J. Sched. 3, 125–138 (2000)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Kuczapski, A.M., Micea, M.V., Maniu, L.A., Cretu, V.I.: Efficient generation of near optimal initial populations to enhance genetic algorithms for job-shop scheduling. Inf. Technol. Control 39(1), 32–37 (2010)Google Scholar
  32. 32.
    van Laarhoven, P.J.M., Aarts, E.H.L., Lenstra, J.K.: Job shop scheduling by simulated annealing. Oper. Res. 40(1), 113–125 (1992)MathSciNetCrossRefGoogle Scholar
  33. 33.
    Lourenco, H.R.: Job-shop scheduling: computational study of local search and large-step optimization methods. Eur. J. Oper. Res. 83(2), 347–364 (1995)CrossRefGoogle Scholar
  34. 34.
    McKay, K.N., Safayeni, F.R., Buzacott, J.A.: Job-shop scheduling theory: what is relevant? Interfaces 18, 84–90 (1988)CrossRefGoogle Scholar
  35. 35.
    Miyashita, K.: Job-shop scheduling with genetic programming. In: Whitley, D., Goldberg, D., Cantu-Paz, E., Spector, L., Parmee, I., Beyer, H.G. (eds.) In: GECCO 2000: Proceedings of the Genetic and Evolutionary Computation Conference, pp. 505–512. Morgan Kaufmann, San Francisco (2000)Google Scholar
  36. 36.
    Nguyen, S., Zhang, M., Johnston, M., Tan, K.: Learning iterative dispatching rules for job shop scheduling with genetic programming. Int. J. Adv. Manuf. Technol. 67(1–4), 85–100 (2013)CrossRefGoogle Scholar
  37. 37.
    Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: A computational study of representations in genetic programming to evolve dispatching rules for the job shop scheduling problem. IEEE Trans. Evol. Comput. 17(5), 621–639 (2013)CrossRefGoogle Scholar
  38. 38.
    Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Dynamic multi-objective job shop scheduling: a genetic programming approach. In: Etaner-Uyar, A.Ş., Özcan, E., Urquhart, N. (eds.) Automated Scheduling and Planning, Studies in Computational Intelligence, vol. 505, pp. 251–282. Springer, Berlin, Heidelberg (2013)CrossRefGoogle Scholar
  39. 39.
    Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Learning reusable initial solutions for multi-objective order acceptance and scheduling problems with genetic programming. In: Krawiec, K., Moraglio, A., Hu, T., Etaner-Uyar, A.Ş., Hu, B. (eds.) Genetic Programming, LNCS, vol. 7831, pp. 157–168. Springer, Berlin, Heidelberg (2013)CrossRefGoogle Scholar
  40. 40.
    Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Automatic design of scheduling policies for dynamic multi-objective job shop scheduling via cooperative coevolution genetic programming. IEEE Trans. Evol. Comput. 18(2), 193–208 (2014)CrossRefGoogle Scholar
  41. 41.
    Nguyen, S.: Automatic design of dispatching rules for job shop scheduling with genetic programming. Ph.D. thesis, Victoria University of Wellington (2013)Google Scholar
  42. 42.
    Nie, L., Gao, L., Li, P., Li, X.: A GEP-based reactive scheduling policies constructing approach for dynamic flexible job shop scheduling problem with job release dates. J. Intell. Manuf. 24(4), 763–774 (2013)CrossRefGoogle Scholar
  43. 43.
    Nie, L., Shao, X., Gao, L., Li, W.: Evolving scheduling rules with gene expression programming for dynamic single-machine scheduling problems. Int. J. Adv. Manuf. Technol. 50(5–8), 729–747 (2010)CrossRefGoogle Scholar
  44. 44.
    Nie, L., Bai, Y., Wang, X., Liu, K.: Discover scheduling strategies with gene expression programming for dynamic flexible job shop scheduling problem. In: Tan, Y., Shi, Y., Ji, Z. (eds.) Adv. Swarm Intell. 7332, 383–390 (2012)Google Scholar
  45. 45.
    Nowicki, E., Smutnicki, C.: A fast taboo search algorithm for the job shop problem. Manage. Sci. 42, 797–813 (1996)CrossRefGoogle Scholar
  46. 46.
    Ouelhadj, D., Petrovic, S.: A survey of dynamic scheduling in manufacturing systems. J. Sched. 12(4), 417–431 (2009)MathSciNetCrossRefGoogle Scholar
  47. 47.
    Petrovic, S., Fayad, C., Petrovic, D., Burke, E., Kendall, G.: Fuzzy job shop scheduling with lot-sizing. Ann. Oper. Res. 159, 275–292 (2008)MathSciNetCrossRefGoogle Scholar
  48. 48.
    Pickardt, C.W., Hildebrandt, T., Branke, J., Heger, J., Scholz-Reiter, B.: Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems. Int. J. Prod. Econ. 145(1), 67–77 (2013)CrossRefGoogle Scholar
  49. 49.
    Pinedo, M.L.: Scheduling: Theory, Algorithms, and Systems, 3rd edn. Springer, New York (2008)zbMATHGoogle Scholar
  50. 50.
    Pinedo, M., Singer, M.: A shifting bottleneck heuristic for minimizing the total weighted tardiness in a job shop. Naval Res. Logistics 46(1), 1–17 (1999)MathSciNetCrossRefGoogle Scholar
  51. 51.
    Ponnambalam, S.G., Ramkumar, V., Jawahar, N.: A multiobjective genetic algorithm for job shop scheduling. Prod. Plann. Control 12(8) (2001)CrossRefGoogle Scholar
  52. 52.
    Potts, C.N., Strusevich, V.A.: Fifty years of scheduling: a survey of milestones. J. Oper. Res. Soc. 60(Supplement 1), 41–68 (2009). http://www.palgrave-journals.com/jors/journal/v60/ns1/abs/jors20092a.htmlCrossRefGoogle Scholar
  53. 53.
    Sels, V., Gheysen, N., Vanhoucke, M.: A comparison of priority rules for the job shop scheduling problem under different flow time- and tardiness-related objective functions. Int. J. Prod. Res. 50(15), 4255–4270 (2011)CrossRefGoogle Scholar
  54. 54.
    Sha, D., Hsu, C.Y.: A hybrid particle swarm optimization for job shop scheduling problem. Comput. Ind. Eng. 51(4), 791–808 (2006)CrossRefGoogle Scholar
  55. 55.
    Tay, J.C., Ho, N.B.: Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems. Comput. Ind. Eng. 54(3), 453–473 (2008)CrossRefGoogle Scholar
  56. 56.
    Wong, P., Zhang, M.: Algebraic simplification of gp programs during evolution. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation. pp. 927–934. GECCO’06 (2006)Google Scholar
  57. 57.
    Xing, L.N., Chen, Y.W., Wang, P., Zhao, Q.S., Xiong, J.: A knowledge-based ant colony optimization for flexible job shop scheduling problems. Appl. Soft Comput. 10(3), 888–896 (2010)CrossRefGoogle Scholar
  58. 58.
    Yamada, T., Nakano, R.: A genetic algorithm with multi-step crossover for job-shop scheduling problems. In: GALESIA: First International Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications. pp. 146–151 (1995)Google Scholar
  59. 59.
    Yin, W.J., Liu, M., Wu, C.: Learning single-machine scheduling heuristics subject to machine breakdowns with genetic programming. In: Sarker, R., Reynolds, R., Abbass, H., Tan, K.C., McKay, B., Essam, D., Gedeon, T. (eds.) In: The 2003 Congress on Evolutionary Computation (CEC 2003), vol. 2, pp. 1050–1055. IEEE Press, Piscataway, NJ (2003)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Su Nguyen
    • 1
    Email author
  • Mengjie Zhang
    • 2
  • Mark Johnston
    • 3
  • Kay Chen Tan
    • 4
  1. 1.La Trobe UniversityBundooraAustralia
  2. 2.Victoria University of WellingtonWellingtonNew Zealand
  3. 3.University of WorcesterWorcesterUK
  4. 4.City University of Hong KongKowloon TongHong Kong

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