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1 January 2015 A Double-Population Genetic Algorithm for ASC Loading Sequence Optimization in Automated Container Terminals
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Abstract

Shu, F.; Mi, W.; Li, X.; Zhao, N.; Mi, C., and Yang, X., 2015. A double-population genetic algorithm for ASC loading sequence optimization in automated container terminals.

An automated container terminal (ACT) emphasizes the autonomy of equipment scheduling. The scheduling of the automated stacking crane (ASC) as major equipment has an important impact on the yard operating efficiency. In the export process, ASC cooperated with the quay crane in loading operations. Although traditionally the loading sequence of ASC is generated directly in terms of the stowage plan, with the trend towards automated intelligent terminals, the ASC is becoming increasingly the content of dynamic decision. This paper presents a method of dynamic planning ASC loading sequence after the stowage plan. The method took reshuffling number and ASC movement frequency into account, considered the target vessel slots defined by the stowage plan as constraints, and established a multi-objective mathematical model. Taking into account the problem with NP-hard characteristics, this paper proposed a double-population genetic algorithm based on a general genetic algorithm, establishing a solution mechanism by leveraging genetic exchanges between populations to change the convergence speed. With the completion of a certain bay in the block, it online adjusted the next block collection, and eventually formed a solution of real-time loading sequence. Experiments demonstrated that the algorithm could dynamically obtain the near optimal solution of the problem, adapt the timeliness demand of ASC dynamic decision, and improve the intelligence of the equipment control system (ECS).

© 2015 Coastal Education and Research Foundation
Fan Shu, Weijian Mi, Xun Li, Ning Zhao, Chao Mi, and Xiaoming Yang "A Double-Population Genetic Algorithm for ASC Loading Sequence Optimization in Automated Container Terminals," Journal of Coastal Research 73(sp1), 64-70, (1 January 2015). https://doi.org/10.2112/SI73-012.1
Received: 3 August 2014; Accepted: 17 November 2014; Published: 1 January 2015
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