Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm

• Published in: Complexity (New York, N.Y.) Vol. 2018; no. 2018; pp. 1 - 12
• Main Authors: Zhu, Stuart X., Xu, He, Yu, Caoyang, Xiang, Xianbo
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; John Wiley & Sons, Inc; Wiley
• Subjects: Adaptive algorithms; Algorithms; Analysis; Computers; Containers; Genetic algorithms; Genetic research; Heuristic; Heuristic methods; Industrial engineering; Loading operations; Operations research; Shipping industry; Utilization
• ISSN: 1076-2787; 1099-0526; 1099-0526
• DOI: 10.1155/2018/2024184

This paper studies an optimized container loading problem with the goal of maximizing the 3D space utilization. Based on the characteristics of the mathematical loading model, we develop a dedicated placement heuristic integrated with a novel dynamic space division method, which enables the design of the adaptive genetic algorithm in order to maximize the loading space utilization. We use both weakly and strongly heterogeneous loading data to test the proposed algorithm. By choosing 15 classic sets of test data given by Loh and Nee as weakly heterogeneous data, the average space utilization of our algorithm reaching 70.62% outperforms those of 13 algorithms from the related literature. Taking a set of test data given by George and Robinson as strongly heterogeneous data, the space utilization in this paper can be improved by 4.42% in comparison with their heuristic algorithm.