Abstract
In this work, an optimization method for 3D container loading problem with multiple constraints is proposed. The method consists of a genetic algorithm to generate an arrangement of cargoes and a fitness evaluation using physics simulation. The fitness function considers not only the maximization of container density or value but also a few different constraints such as stability and fragility of the cargoes during transportation. We employed a container shaking simulation to include the effect of the constraints to the fitness evaluation. We verified that the proposed method successfully provides the optimal cargo arrangement to the small-scale problem with 10 cargoes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
GameWorks PhysX overview. https://developer.nvidia.com/gameworks-physx-overview
Unity real-time development platform. https://unity.com/
Olsen, A.: Penalty functions and the knapsack problem. In: Proceedings of the 1st International Conference on Evolutionary Computation (1994)
Bortfeldt, A., Gehring, H.: Applying tabu search to container loading problems. In: Kischka, P., Lorenz, H.-W., Derigs, U., Domschke, W., Kleinschmidt, P., Möhring, R. (eds.) Operations Research Proceedings 1997. Operations Research Proceedings (GOR (Gesellschaft für Operations Research e.V.)), vol. 1997, pp. 553–558. Springer, Heidelberg (1994). https://doi.org/10.1007/978-3-642-58891-4_84
Bortfeldt, A., Wäscher, G.: Constraints in container loading - a state-of-the-art review. Eur. J. Oper. Res. 229, 1–20 (2013)
Cabrera-Guerrero, G., Lagos, C., Castaneda, C.C., Johnson, F., Paredes, F., Cabrera, E.: Parameter tuning for local-search-based matheuristic methods. Complexity 2017, 15 (2017). ArticleID1702506
Deb, K.: Multi-objective Optimization Using Evolutionary Algorithms. Wiley, Chichester (2001)
Dornas, A.H., Martins, F.V.C., Sarubbi, J.F.M., Wanner, E.F.: Real - polarized genetic algorithm for the three - dimensional bin packing problem. In: Proceedings of GECCO 2017, Berlin, Germany (2017)
Holland, J.H.: Genetic algorithms. Sci. Am. 267(1), 66–73 (1992)
Raidl, G.R., Kodydek, G.: Genetic algorithms for the multiple container packing problem. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 875–884. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056929
Ramos, A.G., Jacob, J., Justo, J.F., Oliveira, J.F., Rodrigues, R., Gomes, A.M.: Cargo dynamic stability in the container loading problem - a physics simulation tool approach. Int. J. Simul. Process Model. 12, 29–41 (2017)
Wu, Y., Li, W., Goh, M., de Souza, R.: Three-dimensional bin packing problem with variable bin height. Eur. J. Oper. Res. 202(2), 347–355 (2010)
Yamazaki, H., Sakanushi, K., Nakatake, S., Kajitani, Y.: The 3D-packing by meta data structure and packing heuristics. Trans. Fundam. E83-A(4), 639–645 (2000)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Nishiyama, S., Lee, C., Mashita, T. (2020). Designing a Flexible Evaluation of Container Loading Using Physics Simulation. In: Dorronsoro, B., Ruiz, P., de la Torre, J., Urda, D., Talbi, EG. (eds) Optimization and Learning. OLA 2020. Communications in Computer and Information Science, vol 1173. Springer, Cham. https://doi.org/10.1007/978-3-030-41913-4_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-41913-4_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-41912-7
Online ISBN: 978-3-030-41913-4
eBook Packages: Computer ScienceComputer Science (R0)