Abstract
Cutting and packing problems have important applications to the transportation of cargo. Many algorithms have been proposed for solving the 2D/3D cutting stock problems but most of them consider single objective optimization. The goal of the problem here proposed is to load the boxes that would provide the highest total volume and weight to the container, without exceeding the container limits. These two objectives are conflicting because the volume of a box is usually not proportional to its weight. This work deals with a multi-objective formulation of the 3D Packing Problem (3DPP). We propose to apply multi-objective evolutionary algorithms in order to obtain a set of non-dominated solutions, from which the final users would choose the one to be definitely carried out. For doing an extensive study, it would be necessary to use more problem instances. Instances to deal with the multi-objective 3DPP are non-existent. For this purpose, we have implemented an instance generator.
References
Bischoff, E.E., Janetz, F., Ratcliff, M.S.W.: Loading pallets with non-identical items. Eur. J. Oper. Res. 84(3), 681–692 (1995)
Bischoff, E., Ratcliff, M.: Issues in the development of approaches to container loading. Omega 23(4), 377–390 (1995)
Bortfeldt, A., Gehring, H.: A tabu search algorithm for weakly heterogeneous container loading problems. OR Spectr. 20(4), 237–250 (1998)
Bortfeldt, A., Gehring, H.: A hybrid genetic algorithm for the container loading problem. Eur. J. Oper. Res. 131(1), 143–161 (2001)
Bortfeldt, A., Wäscher, G.: Container loading problems - a state-of-the-art review. In: Working Paper 1, Otto-von-Guericke-Universität Magdeburg, April 2012
Burke, E.K., Hyde, M.R., Kendall, G., Woodward, J.: Automating the packing heuristic design process with genetic programming. Evol. Comput. 20(1), 63–89 (2012)
Davies, A., Bischoff, E.E.: Weight distribution considerations in container loading. Eur. J. Oper. Res. 114(3), 509–527 (1999)
Dereli, T., Das, S.G.: A hybrid simulated annealing algorithm for solving multi-objective container loading problems. Appl. Artif. Intell. Int. J. 24(5), 463–486 (2010)
Ding, X., Han, Y., Zhang, X.: A discussion of adaptive genetic algorithm solving container-loading problem. Periodical Ocean Univ. China 34(5), 844–848 (2004)
Eley, M.: Solving container loading problems by block arrangement. Eur. J. Oper. Res. 141(2), 393–409 (2002)
Faina, L.: A global optimization algorithm for the three-dimensional packing problem. Eur. J. Oper. Res. 126(2), 340–354 (2000)
Gehring, H., Bortfeldt, A.: A genetic algorithm for solving the container loading problem. Int. Trans. Oper. Res. 4(5–6), 401–418 (1997)
Gonalves, J.F., Resende, M.G.: A parallel multi-population biased random-key genetic algorithm for a container loading problem. Comput. Oper. Res. 39(2), 179–190 (2012)
González, Y., Miranda, G., León, C.: A multi-level filling heuristic for the multi-objective container loading problem. In: International Joint Conference SOCO 2013-CISIS 2013-ICEUTE 2013 - Salamanca, Spain, Proceedings, pp. 11–20, 11–13 September 2013
He, K., Huang, W.: An efficient placement heuristic for three-dimensional rectangular packing. Comput. Oper. Res. 38, 227–233 (2011)
Hifi, M.: Exact algorithms for unconstrained three-dimensional cutting problems: a comparative study. Comput. Oper. Res. 31, 657–674 (2004)
Huang, W., He, K.: A caving degree approach for the single container loading problem. Eur. J. Oper. Res. 196(1), 93–101 (2009)
Ivancic, N., Mathur, K., Mohanty, B.B.: An integer programming based heuristic approach to the three-dimensional packing problem. J. Manuf. Oper. Manage. 2, 268–289 (1989)
Jin, Z., Ohno, K., Du, J.: An efficient approach for the three dimensional container packing problem with practical constraints. Asia Pac. J. Oper. Res. 21(3), 279–295 (2004)
Junqueira, L., Morabito, R., Yamashita, S.D.: Mip-based approaches for the container loading problem with multi-drop constraints. Ann. Oper. Res. 199(1), 51–75 (2012). http://dx.doi.org/10.1007/s10479-011-0942-z
Kang, K., Moon, I., Wang, H.: A hybrid genetic algorithm with a new packing strategy for the three-dimensional bin packing problem. Appl. Math. Comput. 219(3), 1287–1299 (2012)
León, C., Miranda, G., Segura, C.: METCO: a parallel plugin-based framework for multi-objective optimization. Int. J. Artif. Intell. Tools 18(4), 569–588 (2009)
Liu, J., Yue, Y., Dong, Z., Maple, C., Keech, M.: A novel hybrid tabu search approach to container loading. Comput. Oper. Res. 38, 797–807 (2011)
Loh, T.H., Nee, A.Y.C.: A packing algorithm for hexahedral boxes, pp. 115–126 (1992)
Moura, A., Oliveira, J.F.: A GRASP approach to the container-loading problem. IEEE Intell. Syst. 20(4), 50–57 (2005)
Parreño, F., Alvarez-Valdes, R., Oliveira, J.F., Tamarit, J.M.: Neighborhood structures for the container loading problem: a VNS implementation. J. Heuristics 16(1), 1–22 (2010)
Pisinger, D.: Heuristics for the container loading problem. Eur. J. Oper. Res. 141(2), 382–392 (2002)
Ren, J., Tian, Y., Sawaragi, T.: A tree search method for the container loading problem with shipment priority. Eur. J. Oper. Res. 214(3), 526–535 (2011)
Scheithauer, G.: Algorithms for the container loading problem. In: Gaul, W., Bachem, A., Habenicht, W., Runge, W., Stahl, W.W. (eds.) Operations Research Proceedings 1991, vol. 1991, pp. 445–452. Springer, Heidelberg (1992)
Zhao, X., Bennell, J., Bektas, T., Dowsland, K.: A comparative review of 3d container loading algorithms, April 2014
Acknowledgment
This work was funded by the Spanish Ministry of Science and Technology as part of the ‘Plan Nacional de i+d+i’ (tin2011-25448).
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González, Y., Miranda, G., León, C. (2017). An Instance Generator for the Multi-Objective 3D Packing Problem. In: Graña, M., López-Guede, J.M., Etxaniz, O., Herrero, Á., Quintián, H., Corchado, E. (eds) International Joint Conference SOCO’16-CISIS’16-ICEUTE’16. SOCO CISIS ICEUTE 2016 2016 2016. Advances in Intelligent Systems and Computing, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-47364-2_37
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