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Genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling

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Evolutionary Computation in Combinatorial Optimization (EvoCOP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12102))

Abstract

Dynamic flexible job shop scheduling (DFJSS) is a very valuable practical application problem that can be applied in many fields such as cloud computing and manufacturing. In DFJSS, machine assignment and operation sequencing decisions need to be made simultaneously in dynamic environments with unpredicted events such as new job arrivals. Scheduling heuristic is an ideal candidate for solving the DFJSS problem due to its efficiency and simplicity. Genetic programming (GP) has been successfully applied to evolve scheduling heuristics for job shop scheduling automatically. However, GP has a huge search space, and the traditional search algorithms do not utilise effectively the information obtained from the evolutionary process. This paper proposes a new method to make better use of the information during the evolutionary process of GP to further enhance the ability of GP. To be specific, this paper proposes two adaptive search strategies based on the frequency of features in promising individuals to guide GP to evolve effective rules. This paper examines the proposed algorithm on six different DFJSS scenarios. The results show that the proposed GP with adaptive search can converge faster and achieve significantly better performance than the GP without adaptive search in most scenarios while no worse in all other scenarios without increasing the computational cost.

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References

  1. Manne, A.S.: On the job-shop scheduling problem. Oper. Res. 8(2), 219–223 (1960)

    Article  MathSciNet  Google Scholar 

  2. 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). https://doi.org/10.1007/s10951-006-5591-8

    Article  MATH  Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. Nguyen, S.B.S., Zhang, M.: A hybrid discrete particle swarm optimisation method for grid computation scheduling. In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 483–490. IEEE (2014)

    Google Scholar 

  5. Brucker, P., Schlie, R.: Job-shop scheduling with multi-purpose machines. Computing 45(4), 369–375 (1990). https://doi.org/10.1007/BF02238804

    Article  MathSciNet  MATH  Google Scholar 

  6. Yska, D., Mei, Y., Zhang, M.: Genetic programming hyper-heuristic with cooperative coevolution for dynamic flexible job shop scheduling. In: Castelli, M., Sekanina, L., Zhang, M., Cagnoni, S., García-Sánchez, P. (eds.) EuroGP 2018. LNCS, vol. 10781, pp. 306–321. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77553-1_19

    Chapter  Google Scholar 

  7. Zhang, F., Mei, Y., Zhang, M.: Genetic programming with multi-tree representation for dynamic flexible job shop scheduling. In: Mitrovic, T., Xue, B., Li, X. (eds.) AI 2018. LNCS (LNAI), vol. 11320, pp. 472–484. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03991-2_43

    Chapter  Google Scholar 

  8. Durasevic, M., Jakobovic, D.: A survey of dispatching rules for the dynamic unrelated machines environment. Expert Syst. Appl. 113, 555–569 (2018)

    Article  Google Scholar 

  9. Koza, J.R., Poli, R.: Genetic programming. In: Burke, E.K., Kendall, G. (eds.) Search Methodologies, pp. 127–164. Springer, Boston (2005). https://doi.org/10.1007/0-387-28356-0_5

    Chapter  Google Scholar 

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

    Google Scholar 

  11. Nguyen, S., Zhang, M., Johnston, M., Tan, K.C.: Genetic programming for evolving due-date assignment models in job shop environments. Evol. Comput. 22(1), 105–138 (2014)

    Article  Google Scholar 

  12. Branke, J., Nguyen, S., Pickardt, C.W., Zhang, M.: Automated design of production scheduling heuristics: a review. IEEE Trans. Evol. Comput. 20(1), 110–124 (2016)

    Article  Google Scholar 

  13. 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.L., Jain, L.C. (eds.) Computational Intelligence. ISRL, vol. 1, pp. 177–201. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-01799-5_6

    Chapter  Google Scholar 

  14. Burke, E.K., Hyde, M.R., Kendall, G., Woodward, J.R.: A genetic programming hyper-heuristic approach for evolving 2-D strip packing heuristics. IEEE Trans. Evol. Comput. 14(6), 942–958 (2010)

    Article  Google Scholar 

  15. Hyde, M.R.: A genetic programming hyper-heuristic approach to automated packing. Ph.D. thesis, University of Nottingham, UK (2010)

    Google Scholar 

  16. Bader-El-Den, M.B., Poli, R., Fatima, S.: Evolving timetabling heuristics using a grammar-based genetic programming hyper-heuristic framework. Memetic Comput. 1(3), 205–219 (2009). https://doi.org/10.1007/s12293-009-0022-y

    Article  Google Scholar 

  17. Pillay, N., Banzhaf, W.: A genetic programming approach to the generation of hyper-heuristics for the uncapacitated examination timetabling problem. In: Neves, J., Santos, M.F., Machado, J.M. (eds.) EPIA 2007. LNCS (LNAI), vol. 4874, pp. 223–234. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-77002-2_19

    Chapter  Google Scholar 

  18. Zhang, F., Mei, Y., Zhang, M.: A new representation in genetic programming for evolving dispatching rules for dynamic flexible job shop scheduling. In: Liefooghe, A., Paquete, L. (eds.) EvoCOP 2019. LNCS, vol. 11452, pp. 33–49. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16711-0_3

    Chapter  Google Scholar 

  19. Zhang, F., Mei, Y., Zhang, M.: A two-stage genetic programming hyper-heuristic approach with feature selection for dynamic flexible job shop scheduling. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), pp. 347–355. IEEE (2019)

    Google Scholar 

  20. Durasević, M., Jakobović, D.: Evolving dispatching rules for optimising many-objective criteria in the unrelated machines environment. Genet. Program Evolvable Mach. 19(1), 9–51 (2017). https://doi.org/10.1007/s10710-017-9310-3

    Article  Google Scholar 

  21. Hildebrandt, T., Heger, J., Scholz-Reiter, B.: Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach. In: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pp. 257–264. ACM (2010)

    Google Scholar 

  22. Davis, J.P., Eisenhardt, K.M., Bingham, C.B.: Developing theory through simulation methods. Acad. Manag. Rev. 32(2), 480–499 (2007)

    Article  Google Scholar 

  23. Mei, Y., Zhang, M., Nguyen, S.: Feature selection in evolving job shop dispatching rules with genetic programming. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference (GECCO), pp. 365–372 (2016)

    Google Scholar 

  24. Gilpin, L.H., Bau, D., Yuan, B.Z., Bajwa, A., Specter, M., Kagal, L.: Explaining explanations: an overview of interpretability of machine learning. In: 5th IEEE International Conference on Data Science and Advanced Analytics (DSAA), pp. 80–89 (2018)

    Google Scholar 

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Authors and Affiliations

  1. School of Engineering and Computer Science, Victoria University of Wellington, PO BOX 600, Wellington, 6140, New Zealand

    Fangfang Zhang, Yi Mei & Mengjie Zhang

  2. Centre for Data Analytics and Cognition, La Trobe University, Melbourne, VIC, 3086, Australia

    Su Nguyen

Authors
  1. Fangfang Zhang
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  2. Yi Mei
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  3. Su Nguyen
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  4. Mengjie Zhang
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Correspondence to Fangfang Zhang .

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Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    Luís Paquete

  2. Aberystwyth University, Aberystwyth, UK

    Christine Zarges

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Zhang, F., Mei, Y., Nguyen, S., Zhang, M. (2020). Genetic Programming with Adaptive Search Based on the Frequency of Features for Dynamic Flexible Job Shop Scheduling. In: Paquete, L., Zarges, C. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2020. Lecture Notes in Computer Science(), vol 12102. Springer, Cham. https://doi.org/10.1007/978-3-030-43680-3_14

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  • DOI: https://doi.org/10.1007/978-3-030-43680-3_14

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  • Online ISBN: 978-3-030-43680-3

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