Abstract
The three-dimensional Open Dimension Packing problem (3D-ODPP) is a real-world driven optimization problem that aims at the minimization of package volume in right-size packaging systems. The problem can be found in many industrial scenarios, such as e-commerce secondary packaging. The objective of the 3D-ODPP is to find out the length, width, and height of the cardboard box that can be used to pack a given set of or products so that the volume of the box is minimal. Many literature researches have focused on exact methods to deal with the 3D-ODPP. Despite the fact that the exact methods are capable of finding the global solution, their applications are very limited in terms of problem size and computational time because the 3D-ODPP is NP-hard in the strong sense. In addition, constructive and meta-heuristic methods for solving the 3D-ODPP have not been discussed frequently in the literature and remain a gap in the state-of-the-art.
This paper proposes a genetic algorithm that deals with the 3D-ODPP. The genetic process is to find out the packing sequence and the orientation of products. To construct the solution, a new greedy-search product placement algorithm is developed. This placement algorithm is used to determine the position where each product is placed and to calculate the volume of the package. Literature instances are tested and the obtained solutions are compared with that given by existing exact methods. The experiments show that the proposed algorithm has the capacity of solving the 3D-ODPP in a reasonable time and gives competitive solutions compared with the benchmark methods, especially for problems with many products.
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Truong, C.T.T., Amodeo, L., Yalaoui, F. (2021). A Genetic Algorithm for the Three-Dimensional Open Dimension Packing Problem. In: Dorronsoro, B., Amodeo, L., Pavone, M., Ruiz, P. (eds) Optimization and Learning. OLA 2021. Communications in Computer and Information Science, vol 1443. Springer, Cham. https://doi.org/10.1007/978-3-030-85672-4_15
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DOI: https://doi.org/10.1007/978-3-030-85672-4_15
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