Abstract
The proposed research focuses on multiple-container packing problems with considerations of multiple constraints. The space utilization, stability, load bearing, and loading sequence of objects are also considered in order to make results more practicable. Clustering technology and genetic algorithm are combined to solve the proposed problems. At the beginning, clustering algorithm is applied to classify data objects into different groups with varied characteristics, such as dimension of objects, unloading sequence of objects, and capacity of containers. Then, genetic algorithm combines with heuristic rules is used to pack data objects into containers respectively. The stable packing, space utilization, unhindered unloading, and load bear limitation are the major considerations in this stage. A computer system based on the proposed algorithm was developed. Thousands of cases were simulated and analyzed to evaluate the performance of the proposed research and prove the applicability in real world.
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Lin, JL., Chang, CH., Yang, JY. (2006). A Study of Optimal System for Multiple-Constraint Multiple-Container Packing Problems. In: Ali, M., Dapoigny, R. (eds) Advances in Applied Artificial Intelligence. IEA/AIE 2006. Lecture Notes in Computer Science(), vol 4031. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11779568_127
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DOI: https://doi.org/10.1007/11779568_127
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-35453-6
Online ISBN: 978-3-540-35454-3
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