Tailoring Instances of the 1D Bin Packing Problem for Assessing Strengths and Weaknesses of Its Solvers

  • Ivan AmayaEmail author
  • José Carlos Ortiz-Bayliss
  • Santiago Enrique Conant-Pablos
  • Hugo Terashima-Marín
  • Carlos A. Coello Coello
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11102)


Solvers for different combinatorial optimization problems have evolved throughout the years. These can range from simple strategies such as basic heuristics, to advanced models such as metaheuristics and hyper-heuristics. Even so, the set of benchmark instances has remained almost unaltered. Thus, any analysis of solvers has been limited to assessing their performance under those scenarios. Even if this has been fruitful, we deem necessary to provide a tool that allows for a better study of each available solver. Because of that, in this paper we present a tool for assessing the strengths and weaknesses of different solvers, by tailoring a set of instances for each of them. We propose an evolutionary-based model and test our idea on four different basic heuristics for the 1D bin packing problem. This, however, does not limit the scope of our proposal, since it can be used in other domains and for other solvers with few changes. By pursuing an in-depth study of such tailored instances, more relevant knowledge about each solver can be derived.


1D bin packing problem Genetic algorithm Instance generation 


  1. 1.
    Beasley, J.: OR-library: distributing test problems by electronic mail. J. Oper. Res. Soc. 41(11), 1069–1072 (1990)CrossRefGoogle Scholar
  2. 2.
    Drake, J.H., Swan, J., Neumann, G., Özcan, E.: Sparse, continuous policy representations for uniform online bin packing via regression of interpolants. In: Hu, B., López-Ibáñez, M. (eds.) EvoCOP 2017. LNCS, vol. 10197, pp. 189–200. Springer, Cham (2017). Scholar
  3. 3.
    Gomez, J.C., Terashima-Marín, H.: Evolutionary hyper-heuristics for tackling bi-objective 2D bin packing problems. Genet. Program. Evol. Mach. 19, 151–181 (2017). Scholar
  4. 4.
    van Hemert, J.I.: Evolving binary constraint satisfaction problem instances that are difficult to solve. In: Proceedings of the 2003 IEEE Congress on Evolutionary Computation (CEC 2003), pp. 1267–1273. IEEE Press (2003)Google Scholar
  5. 5.
    van Hemert, J.I.: Evolving combinatorial problem instances that are difficult to solve. Evol. Comput. 14(4), 433–462 (2006)CrossRefGoogle Scholar
  6. 6.
    Knowles, J.D., Corne, D.W.: Approximating the nondominated front using the pareto archived evolution strategy. Evol. Comput. 8(2), 149–172 (2000)CrossRefGoogle Scholar
  7. 7.
    Koch, T., et al.: MIPLIB 2010. Math. Program. Comput. 3(2), 103–163 (2011)MathSciNetCrossRefGoogle Scholar
  8. 8.
    López-Camacho, E., Terashima-Marín, H., Ross, P.: A hyper-heuristic for solving one and two-dimensional bin packing problems. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2011), pp. 257–258 (2011).
  9. 9.
    Lust, T., Teghem, J.: The multiobjective multidimensional knapsack problem: a survey and a new approach. Int. Trans. Oper. Res. 19(4), 495–520 (2012)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Martello, S., Pisinger, D., Vigo, D.: The three-dimensional bin packing problem. Oper. Res. 48(2), 256–267 (2000)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Martello, S., Toth, P.: Knapsack Problems: Algorithms and Computer Implementations. Wiley, Hoboken (1990)zbMATHGoogle Scholar
  12. 12.
    Özcan, E., Parkes, A.J.: Policy matrix evolution for generation of heuristics. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation - GECCO 2011, p. 2011 (2011).
  13. 13.
    Petursson, K.B., Runarsson, T.P.: An evolutionary approach to the discovery of hybrid branching rules for mixed integer solvers. In: Proceedings - 2015 IEEE Symposium Series on Computational Intelligence, SSCI 2015, pp. 1436–1443 (2016)Google Scholar
  14. 14.
    Pisinger, D.: Where are the hard knapsack problems? Comput. Oper. Res. 32(9), 2271–2284 (2005)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Smith-Miles, K., van Hemert, J.: Discovering the suitability of optimisation algorithms by learning from evolved instances. Ann. Math. Artif. Intell. 61(2), 87–104 (2011)MathSciNetCrossRefGoogle Scholar
  16. 16.
    Smith-Miles, K., van Hemert, J., Lim, X.Y.: Understanding TSP difficulty by learning from evolved instances. In: Blum, C., Battiti, R. (eds.) LION 2010. LNCS, vol. 6073, pp. 266–280. Springer, Heidelberg (2010). Scholar
  17. 17.
    Sosa-Ascencio, A., Terashima-Marín, H., Ortiz-Bayliss, J.C., Conant-Pablos, S.E.: Grammar-based selection hyper-heuristics for solving irregular bin packing problems. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion - GECCO 2016 Companion, pp. 111–112. ACM Press, New York (2016).
  18. 18.
    Zitzler, E., Laumanns, M., Thiele, L.: SPEA2: improving the strength pareto evolutionary algorithm. In: Evolutionary Methods for Design Optimization and Control with Applications to Industrial Problems, pp. 95–100 (2001)Google Scholar
  19. 19.
    Zitzler, E., Thiele, L.: Multiobjective evolutionary algorithms: a comparative case study and the strength pareto approach. IEEE Trans. Evol. Comput. 3(4), 257–271 (1999)CrossRefGoogle Scholar

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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Engineering and SciencesTecnologico de MonterreyMonterreyMexico
  2. 2.CINVESTAV-IPN (Evolutionary Computation Group)Mexico CityMexico

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