• 대한산업공학회지
• /
• 제38권2호
• /
• pp.157-172
• /
• 2012

In most container terminals, containers are piled up and stored in a yard in order to utilize the space efficiently. Hence, it requires unproductive container-handling operations to retrieve a container that is not placed on the top of a container stack. As a result, to streamline container-loading operations by which containers are transferred from a yard to a vessel, it is necessary to pre-marshal (i.e., shuffle in advance) containers in accordance with container-loading plan. We propose $A^*$ algorithm to find the optimal container-relocation sequence for the intra-bay container pre-marshalling problem. To work out the heuristic estimate for the proposed $A^*$ algorithm, we introduce the container rearrangement problem and obtain the lower bound of the length of the optimal relocation sequence. The performance of the algorithm is validated extensively by the numerical experiments on the problem instances that are given in the previous studies and generated randomly with various parameters.

• 한국컴퓨터정보학회논문지
• /
• 제20권10호
• /
• pp.61-68
• /
• 2015

This paper deals with the pre-marshalling problem that the containers of container yard at container terminal are relocated in consensus sequence of loading schedule of container vessel. This problem is essential to improvement of competitive power of terminal. This problem has to relocate the all of containers in a bay with minimum number of movement. There are various algorithms such as metaheuristic as genetic algorithm and heuristic algorithm in order to find the solution of this problem. Nevertheless, there is no unique general algorithm that is suitable for various many data. And the main drawback of metaheuristic methods are not the solution finding rule but can be find the approximated solution with many random trials and by coincidence. This paper can be obtain the solution with O(m) time complexity that this problem deals with bin packing problem for m stack bins with descending order of take out ranking. For various experimental data, the proposed algorithm can be obtain the optimal solutions for all of data. And to conclude, this algorithm can be show that most simple and general algorithm with simple optimal solution finding rule.