Abstract-This paper proposes a developed simulation model for stacking containers in a container terminal through developing and applying a genetic algorithm (GA) for containers location assignment with minimized total lifting time and increased service efficiency of the container terminals. The application obtained from the genetic algorithm shows the appropriate containers location assignment based on the arrival of containers at a container terminal and correlates it with the order of the containers loaded onto container ships. The solutions from the genetic algorithm were used in the model application. Observations were made on container total lifting time using the simulation model based on a genetic algorithm for finding the best configuration of containers in a bay and the solution of simulation based on the First-In, First-Storage (FIFS) rule. Experimental results showed that the simulation model based on the genetic algorithm is more efficient than the simulation model based on the FIFS rule.

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This paper focuses on storage location assignment and exported container relocation in container yard of container terminal with the objective of minimizing the number of container lifting. On the lifting steps, the truck with yard crane should be chosen in order to deliver a container from container yard to container ship, and this action can reduce container ship's docking time and increase effectiveness in container terminal service. In this paper, a genetic algorithm (GA) in container storage assignment and a heuristic for the container relocation determination are adopted. Also, the current practice including first-in-first-stored (FIFS) and simple relocation (SR) is used to compare the effectiveness of the GA and the proposed heuristic (RH). The experimental result presented that the proposed method is able to construct the effective solutions of storage location assignment of exported containers, and it reduces the number of relocations of exported container effectively.

This paper reports the results of a study to use the ant colony optimization (ACO) in solving an export container stacking and storage problem of a container terminal port. Three different methods of solutions based on different initial solution techniques and the current method used by the port were used to test forty sample problems which were classified into four problem types representing different patterns of container arrivals. The results of this study revealed that the three methods based on the ACO could be used to improve the total time taken for stacking and storage of the export containers for all problem types. Among the methods investigated, the heuristic-based ant colony optimization (BACO) method proved to be the most efficient for all problem types, with the relative improvements over the current method ranging from 25.4-38.7%.