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Human-Derived Heuristic Enhancement of an Evolutionary Algorithm for the 2D Bin-Packing Problem

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Parallel Problem Solving from Nature – PPSN XVI (PPSN 2020)

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Abstract

The 2D Bin-Packing Problem (2DBPP) is an NP-Hard combinatorial optimisation problem with many real-world analogues. Fully deterministic methods such as the well-known Best Fit and First Fit heuristics, stochastic methods such as Evolutionary Algorithms (EAs), and hybrid EAs that combine the deterministic and stochastic approaches have all been applied to the problem. Combining derived human expertise with a hybrid EA offers another potential approach. In this work, the moves of humans playing a gamified version of the 2DBPP were recorded and four different Human-Derived Heuristics (HDHs) were created by learning the underlying heuristics employed by those players. Each HDH used a decision tree in place of the mutation operator in the EA. To test their effectiveness, these were compared against hybrid EAs utilising Best Fit or First Fit heuristics as well as a standard EA using a random swap mutation modified with a Next Fit heuristic if the mutation was infeasible. The HDHs were shown to outperform the standard EA and were faster to converge than – but ultimately outperformed by – the First Fit and Best Fit heuristics. This shows that humans can create competitive heuristics through gameplay and helps to understand the role that heuristics can play in stochastic search.

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References

  1. Wäscher, G., Haußner, H., Schumann, H.: An improved typology of cutting and packing problems. Eur. J. Oper. Res. 183(3), 1109–1130 (2007)

    Article  Google Scholar 

  2. Berkey, J.O., Wang, P.Y.: Two-dimensional finite bin-packing algorithms. J. Oper. Res. Soc. 38(5), 423–429 (1987). https://doi.org/10.1057/jors.1987.70

    Article  MATH  Google Scholar 

  3. Dósa, G., Sgall, J.: First fit bin packing: a tight analysis. In: 30th International Symposium on Theoretical Aspects of Computer Science (STACS 2013), Dagstuhl, Germany, 2013, vol. 20, pp. 538–549. https://doi.org/10.4230/LIPIcs.STACS.2013.538

  4. Dósa, G., Sgall, J.: Optimal analysis of best fit bin packing. In: Esparza, J., Fraigniaud, P., Husfeldt, T., Koutsoupias, E. (eds.) ICALP 2014. LNCS, vol. 8572, pp. 429–441. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-43948-7_36

    Chapter  Google Scholar 

  5. Oliveira, Ó., Gamboa, D.: Adaptive sequence-based heuristic for the two-dimensional non-guillotine bin packing problem. In: Madureira, A.M., Abraham, A., Gandhi, N., Varela, M.L. (eds.) HIS 2018. AISC, vol. 923, pp. 370–375. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-14347-3_36

    Chapter  Google Scholar 

  6. López-Camacho, E., Ochoa, G., Terashima-Marín, H., Burke, E.K.: An effective heuristic for the two-dimensional irregular bin packing problem. Ann. Oper. Res. 206(1), 241–264 (2013). https://doi.org/10.1007/s10479-013-1341-4

    Article  MathSciNet  MATH  Google Scholar 

  7. Falkenauer, E., Delchambre, A.: A genetic algorithm for bin packing and line balancing. In: Proceedings of 1992 IEEE International Conference on Robotics and Automation, 1992, vol. 2, pp. 1186–1192. https://doi.org/10.1109/robot.1992.220088

  8. Lam, G.T., Ho, V.A., Logofatu, D., Badica, C.: Considerations on using genetic algorithms for the 2D bin packing problem: a general model and detected difficulties. In: 2017 21st International Conference on System Theory, Control and Computing (ICSTCC), pp. 303–308 (2017). https://doi.org/10.1109/icstcc.2017.8107051

  9. Kucukyilmaz, T., Kiziloz, H.E.: Cooperative parallel grouping genetic algorithm for the one-dimensional bin packing problem. Comput. Ind. Eng. 125, 157–170 (2018). https://doi.org/10.1016/j.cie.2018.08.021

    Article  Google Scholar 

  10. Luo, F., Scherson, I.D., Fuentes, J.: A novel genetic algorithm for bin packing problem in jMetal. In: 2017 IEEE International Conference on Cognitive Computing (ICCC), pp. 17–23 (2017). https://doi.org/10.1109/ieee.iccc.2017.10

  11. Parreño, F., Alvarez-Valdes, R., Oliveira, J.F., Tamarit, J.M.: A hybrid GRASP/VND algorithm for two- and three-dimensional bin packing. Ann. Oper. Res. 179(1), 203–220 (2010). https://doi.org/10.1007/s10479-008-0449-4

    Article  MathSciNet  MATH  Google Scholar 

  12. Hong, S., Zhang, D., Lau, H.C., Zeng, X., Si, Y.-W.: A hybrid heuristic algorithm for the 2D variable-sized bin packing problem. Eur. J. Oper. Res. 238(1), 95–103 (2014). https://doi.org/10.1016/j.ejor.2014.03.049

    Article  MathSciNet  MATH  Google Scholar 

  13. Zhang, D., Che, Y., Ye, F., Si, Y.-W., Leung, S.C.H.: A hybrid algorithm based on variable neighbourhood for the strip packing problem. J. Comb. Optim. 32(2), 513–530 (2016). https://doi.org/10.1007/s10878-016-0036-6

    Article  MathSciNet  MATH  Google Scholar 

  14. Zhao, C., Jiang, L., Teo, K.L.: A hybrid chaos firefly algorithm for three-dimensional irregular packing problem. J. Ind. Manag. Optim. 16(1), 409 (2020). https://doi.org/10.3934/jimo.2018160

    Article  MathSciNet  MATH  Google Scholar 

  15. Laterre, A., Fu, Y., Jabri, M.K., Cohen, A.-S., Kas, D., Hajjar, K.: Ranked reward: enabling self-play reinforcement learning for bin packing, p. 10 (2019)

    Google Scholar 

  16. Pillay, N., Qu, R.: Packing Problems. In: Hyper-Heuristics: Theory and Applications, pp. 67–73. Springer International Publishing, Cham (2018)

    Google Scholar 

  17. López-Camacho, E., Terashima-Marín, H., Ross, P.: A hyper-heuristic for solving one and two-dimensional bin packing problems. In: Proceedings of the 13th Annual Conference Companion on Genetic and Evolutionary Computation, Dublin, Ireland, pp. 257–258 (2011). https://doi.org/10.1145/2001858.2002003

  18. Gomez, J.C., Terashima-Marín, H.: Evolutionary hyper-heuristics for tackling bi-objective 2D bin packing problems. Genet. Program. Evolvable Mach. 19(1), 151–181 (2018). https://doi.org/10.1007/s10710-017-9301-4

  19. Hassan, A., Pillay, N.: Hybrid metaheuristics: an automated approach. Expert Syst. Appl. 130, 132–144 (2019). https://doi.org/10.1016/j.eswa.2019.04.027

    Article  Google Scholar 

  20. Blum, C., Schmid, V.: Solving the 2D bin packing problem by means of a hybrid evolutionary algorithm. Procedia Comput. Sci. 18, 899–908 (2013). https://doi.org/10.1016/j.procs.2013.05.255

    Article  Google Scholar 

  21. Kaaouache, M.A., Bouamama, S.: Solving bin packing problem with a hybrid genetic algorithm for VM placement in cloud. Procedia Comput. Sci. 60, 1061–1069 (2015). https://doi.org/10.1016/j.procs.2015.08.151

    Article  Google Scholar 

  22. Beyaz, M., Dokeroglu, T., Cosar, A.: Hybrid heuristic algorithms for the multiobjective load balancing of 2D bin packing problems. In: Abdelrahman, O.H., Gelenbe, E., Gorbil, G., Lent, R. (eds.) Information Sciences and Systems 2015. LNEE, vol. 363, pp. 209–220. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-22635-4_19

    Chapter  Google Scholar 

  23. Laabadi, S., Naimi, M., El Amri, H., Achchab, B.: A crow search-based genetic algorithm for solving two-dimensional bin packing problem. In: Benzmüller, C., Stuckenschmidt, H. (eds.) KI 2019. LNCS (LNAI), vol. 11793, pp. 203–215. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30179-8_17

    Chapter  Google Scholar 

  24. Johns, M.B., Mahmoud, H.A., Walker, D.J., Ross, N.D.F., Keedwell, E.C., Savic, D.A.: Augmented evolutionary intelligence: combining human and evolutionary design for water distribution network optimisation. In: Proceedings of the Genetic and Evolutionary Computation Conference, Prague, Czech Republic, pp. 1214–1222 (2019). https://doi.org/10.1145/3321707.3321814

  25. Ross, N.D.F., Johns, M.B., Keedwell, E.C., Savic, D.A.: Human-evolutionary problem solving through gamification of a bin-packing problem. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Prague, Czech Republic, pp. 1465–1473 (2019). https://doi.org/10.1145/3319619.3326871

  26. Darejeh, A., Salim, S.S.: Gamification solutions to enhance software user engagement—a systematic review. Int. J. Hum.-Comput. Interact. 32(8), 613–642 (2016). https://doi.org/10.1080/10447318.2016.1183330

    Article  Google Scholar 

  27. Morschheuser, B., Hamari, J., Koivisto, J.: Gamification in crowdsourcing: a review. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), pp. 4375–4384 (2016). https://doi.org/10.1109/hicss.2016.543

  28. Suh, A., Wagner, C., Liu, L.: Enhancing user engagement through gamification. J. Comput. Inf. Syst. 58(3), 204–213 (2018). https://doi.org/10.1080/08874417.2016.1229143

    Article  Google Scholar 

  29. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

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Acknowledgments

This work was supported by Skipworth Engelhardt Asset Management Strategists Limited (SEAMS) and the Human-Computer Optimisation for Water Systems Planning and Management (HOWS) project funded by the Engineering and Physical Sciences Research Council (EPSRC) – grant EP/P009441/1.

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Authors and Affiliations

  1. University of Exeter, Exeter, EX4 4QF, UK

    Nicholas Ross, Ed Keedwell & Dragan Savic

  2. KWR, Nieuwegein, Netherlands

    Dragan Savic

Authors
  1. Nicholas Ross
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  2. Ed Keedwell
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  3. Dragan Savic
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Corresponding author

Correspondence to Nicholas Ross .

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Editors and Affiliations

  1. Leiden University, Leiden, The Netherlands

    Thomas Bäck

  2. Leiden University, Leiden, The Netherlands

    Mike Preuss

  3. Leiden University, Leiden, The Netherlands

    André Deutz

  4. Sorbonne University, Paris, France

    Hao Wang

  5. Sorbonne University, Paris, France

    Carola Doerr

  6. Leiden University, Leiden, The Netherlands

    Michael Emmerich

  7. University of Münster, Münster, Germany

    Heike Trautmann

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Ross, N., Keedwell, E., Savic, D. (2020). Human-Derived Heuristic Enhancement of an Evolutionary Algorithm for the 2D Bin-Packing Problem. In: Bäck, T., et al. Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science(), vol 12270. Springer, Cham. https://doi.org/10.1007/978-3-030-58115-2_29

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  • DOI: https://doi.org/10.1007/978-3-030-58115-2_29

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  • Online ISBN: 978-3-030-58115-2

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