Storage strategy of outbound containers with uncertain weight by data-driven hybrid genetic simulated annealing algorithm

• Published in: PloS one Vol. 18; no. 4; p. e0277890
• Main Authors: Wang, Ruoqi, Li, Jiawei, Bai, Ruibin, Wang, Lei
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 07.04.2023; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Biology and Life Sciences; Boxes; Cargo ships; Container ships; Containers; Convergence; Cranes; Dumping; Energy consumption; Engineering and Technology; Genetic algorithms; Genetic research; Iterative methods; Linear programming; Management; Mathematical models; Models, Theoretical; Mutation; Numerical experiments; Objective function; Optimization; Packing problem; Physical Sciences; Ports; Research and Analysis Methods; Scheduling; Ships; Simulated annealing; Space allocation; Stacking; Storage; Stowage (onboard equipment); Transportation; Unloading; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0277890

It is necessary to ensure the ship’s stability in container ship stowage and loading and unloading containers. This work aims to reduce the container dumping operation at the midway port and improve the efficiency of ship transportation. Firstly, the constraint problem of the traditional container ship stacking is introduced to realize the multi-condition mathematical model of the container ship, container, and wharf. Secondly, a Hybrid Genetic and Simulated Annealing Algorithm (HGSAA) model is proposed for the container stacking and loading stacking in the yard. The specific container space allocation and multi-yard crane adjustment scheme are studied. Finally, the effectiveness of the multi-condition container ship stowage model is verified by numerical experiments by changing the number of outbound containers, storage strategies, storage yards, and bridges. The experimental results show that the HGSAA mode converges to 106.1min at the 751 st iteration. Of these, the non-loading and unloading time of yard bridge 1 is 3.43min. The number of operating boxes is 25. The non-loading and unloading time of yard bridge 2 is 3.2min, and the operating box volume is 25 boxes. The objective function of the genetic algorithm converges when it iterates to generation 903 and 107.9min. Among them, the non-loading and unloading time of yard bridge 1 is 4.1min. The non-loading and unloading time of yard bridge 2 is 3.1min. Therefore, the proposed HGSAA has a faster convergence speed than the genetic algorithm and can obtain relatively good results. The proposed container stacking strategy can effectively solve the specific container allocation and multi-yard crane scheduling problems. The finding provides a reference for optimizing container scheduling and improving shipping transportation efficiency.