期刊
COMPUTERS & INDUSTRIAL ENGINEERING
卷 89, 期 -, 页码 80-87出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2014.07.012
关键词
Container loading; Genetic algorithm; Multi-objective genetic algorithm; Fuzzy logic controller; Cutting and packing
资金
- National Science Council Taiwan [NSC100-2628-E-007-017-MY3, NSC102-2221-E-007-057-MY3, NSC102-2811-E-007-005]
- Advanced Manufacturing and Service Management Research Center of National Tsing Hua University [103N2075E1]
- Japan Society of Promotion of Science (JSPS) [24510219]
- Grants-in-Aid for Scientific Research [24510219] Funding Source: KAKEN
The container loading problem (CLP) has important industrial and commercial application for global logistics and supply chain. Many algorithms have been proposed for solving the 2D/3D container loading problem, yet most of them consider single objective optimization. In practice, container loading involves optimizing a number of objectives. This study aims to develop a multi-objective multi-population biased random-key genetic algorithm for the three-dimensional single container loading problem. In particular, the proposed genetic algorithm applied multi-population strategy and fuzzy logic controller (PLC) to improve efficiency and effectiveness. Indeed, the proposed approach maximizes the container space utilization and the value of total loaded boxes by employing Pareto approach and adaptive weights approach. Numerical experiments are designed to compare the results between the proposed approach and existing approaches in hard and weak heterogeneous cases to estimate the validity of this approach. The results have shown practical viability of this approach. This study concludes with discussions of contributions and future research directions. (C) 2014 Elsevier Ltd. All rights reserved.
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