• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2009.06a
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• pp.177-178
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• 2009

자동화 컨테이너 터미널의 장치장 재정돈이란 장치장의 컨테이너를 장치장 크레인의 유휴 시간을 이용해 적하 작업 효율을 높일 수 있는 위치로 재배치하는 작업을 의미한다. 이때 재정돈에 사용한 수 있는 크레인의 유휴시간이 제한적이므로, 본 논문에서는 전체 컨테이너 중 일부를 재정돈 대상 컨테이너로 선택하여 재정돈 계획을 수립하는 방안을 제안한다. 재정돈 컨테이너 선택 시 사용된 선택 기준과 한 번에 선택하는 컨테이너의 범위에 따라 네 가지 재정돈 대상 컨테이너 선택 방안을 제시하였으며, 선택된 재정돈 컨테이너를 대상으로 휴리스틱 알고리즘을 이용해 크레인의 작업 계획을 수립하였다. 시뮬레이션 시스템을 이용한 실험 결과 유휴 시간이 제한되어 있는 경우 재정돈 대상 컨테이너를 선택 후 재정돈 계획을 수립하는 방안이 전체 컨테이너를 대상으로 재정돈 계획을 수립 후 주어진 유휴시간 동안 해당 계획에 따라 재정돈을 수행하는 방안보다 더 좋은 적하 작업 효율을 보임을 확인하였다.

• Journal of Navigation and Port Research
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• v.33 no.10
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• pp.715-722
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• 2009

In Automated Container Terminal, the remarshaling is the task of rearranging containers for the effective exporting operations. Since available time for the remarshaling is usually restricted, all containers cannot be remarshaled. Therefore, this paper proposes a method that selects some containers from all export containers and then establishes the remarshaling plan with only the selected containers. The experimental results using simulation system shows that our proposal planning method that plans after selecting remarshaling containers is better than plans with all loading containers and remarshals during the available time.

• Journal of Navigation and Port Research
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• v.33 no.6
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• pp.443-450
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• 2009

In this paper, we propose optimizing a remarshaling plan in an automated stacking yard using a cooperative coevolutionary algorithm (CCEA). Remarshaling is the preparation task of rearranging the containers in such a way that the delay are minimized at the time of loading. A plan for remarshaling can be obtained by the following steps: first determining the target slots to which the individual containers are to be moved and then determining the order of movement of those containers. Where a given problem can be decomposed into some subproblems, CCEA efficiently searches subproblems for a solution. In our CCEA, the remarshaling problem is decomposed into two subproblems: one is the subproblem of determining the target slots and the other is that of determining the movement priority. Simulation experiments show that our CCEA derives a plan which is better in the efficiency of both loading and remarshaling compared to other methods which are not based on the idea of problem decomposition.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.357-364
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• 2007

장치장 재정돈이란 장치장 크레인의 유휴시간을 활용하여 최대의 효율로 적하작업을 할 수 있도록 장치장의 컨테이너들을 미리 재배치하는 작업을 말한다. 본 논문에서는 수직 배치 자동화 컨테이너 터미널의 장치장을 대상으로 블록 내 재정돈 작업 계획을 수립하는 알고리즘을 제안한다. 제안된 알고리즘은 장치장 재정돈 작업 계획을 목표 장치형태 결정 및 크레인 작업 계획의 2단계로 나누어 수립한다. 시뮬레이션 시스템을 이용하여 재정돈을 수행하기 전과 후의 장치장에 대해 적하 작업 효율을 비교한 결과 재정돈 후 안벽 크레인의 대기 시간이 감소하고, 장치장의 재취급 작업 수가 줄어드는 등 적하 작업 효율이 향상되는 것을 확인하였다.

• Journal of KIISE:Software and Applications
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• v.33 no.7
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• pp.624-635
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• 2006

This paper applies simulated annealing algorithm to the problem of generating a plan for intra-block remarshalling with multiple transfer cranes. Intra-block remarshalling refers to the task of rearranging containers scattered around within a block into certain designated target areas of the block so that they can be efficiently loaded onto a ship. In generating a remarshalling plan, the predetermined container loading sequence should be considered carefully to avoid re-handlings that may delay the loading operations. In addition, the required time for the remarshalling operation itself should be minimized. A candidate solution in our search space specifies target locations of the containers to be rearranged. A candidate solution is evaluated by deriving a container moving plan and estimating the time needed to execute the plan using two cranes with minimum interference. Simulation experiments have shown that our method can generate efficient remarshalling plans in various situations.

• Journal of Navigation and Port Research
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• v.34 no.6
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• pp.503-509
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• 2010

Remarshaling is referred to a preparatory task of rearranging containers piled up in a stacking yard to improve the efficiency of loading. Selective remarshaling is required because the time for remarshaling known as large time-consuming task is not enough to remarshal all containers. In this research, we compare two previous researches in more objectively way: heuristic and genetic algorithm based approaches. In addition, we prove the effect of iterative reselection method on dwindling the gap between plan and execution due to the uncertainty of crane operation during execution. Simulation experiments under realistic uncertainty model show that heuristic approaches which have few computational complexity can cope with the uncertainty well when reselection interval is short, but genetic algorithm based approach has advantages when reselection interval that can ensure appropriate number of evolutions is given because of computational complexity for search.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.31-46
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• 2008

A stacking yard of a container terminal is a space for temporarily storing the containers that are carried in or imported until they are carried out or exported. If the containers are stacked in an inappropriate way, the efficiency of operation at the time of loading decreases significantly due to the rehandlings. The remarshaling is the task of rearranging containers during the idle time of transfer crane for the effective loading operations. This paper proposes a method of planning for remarshaling in a yard block of an automated container terminal. Our method conducts a search in two stages. In the first stage, the target stacking configuration is determined in such a way that the throughput of loading is maximized. In the second stage, the crane schedule is determined so that the remarshaling task can be completed as fast as possible in moving the containers from the source configuration to the target configuration. Simulation experiments have been conducted to compare the efficiency of loading operations before and after remarshaling. The results show that our remarshaling plan is really effective in increasing the efficiency of loading operation.

• Journal of Navigation and Port Research
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• v.40 no.3
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• pp.113-120
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• 2016

If there are containers stacked upon the container to be fetched out of a container yard to vessel, rehandling which moves those containers to other places temporarily is needed. In order to avoid such rehandling, remarshalling which rearranges containers should be done before the vessel arrives. The remarshalling plan is commonly generated within a bay. It happens, however, that the generation of the intra-bay remarshalling plan within the permitted time is not possible because of bad stacking conditions. This paper presents the remarshalling algorithm which uses the empty slots of the neighboring bay as a temporary storage space. Simulation experiments have shown that the presented algorithm can generate the remarshalling plan within the permitted time under any staking conditions.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2004.11a
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• pp.287-295
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• 2004

컨테이너 터미널의 적하 작업은 사전에 계획된 순서대로 컨테이너를 장치장(yard)에서 하나씩 반출하여 선박에 선적하는 작업이다. 만일 현재 반출하고자 하는 컨테이너의 상단에 다른 컨테이너들이 장치되어 있다면, 부득이하게 위에 놓여 있는 컨테이너들을 다른 곳으로 옮겨야 하는 재취급(rehandling)이 발생하고 이로 인하여 반출 작업은 지연된다. 본 논문에서는 적하 계획이 수립되어 장치장의 한 베이(bay)내 컨테이너들의 반출 순서가 결정되었을 때, 반출 시작 시점까지 남는 유휴 시간을 활용하여 재취급이 발생하지 않도록 컨테이너들을 재정돈(이적, 리마샬링)하는 방안을 제안한다. 본 제안 방안은 분지 한계법(branch and bound)을 적용하여 컨테이너 이동 계획을 탐색하며, 생성된 해는 최소의 컨테이너 이동 횟수가 보장된다. 4 단 6 열과 5 단 9 열의 장치장 구조를 대상으로 실험한 결과 99% 이상의 상황에서 허용할 수 있는 시간 내에 계획을 수립할 수 있음을 확인하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.334-336
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• 2001

세선화 알고리즘의 간접 기법으로 제시된 자기구성 특징 그래프(Self-Organizing feature Graph : SOG) 기법은 안정된 세선화 결과를 가지는 장점이 있으나 학습 알고리즘에서 전체 노드를 재정돈하는 과정이 내포되어 있다. 본 본문에서는 학습 알고리즘의 재정돈 과정을 대신하는 점증적 정돈기법을 제안하고 이 기법을 세선화 알고리즘에 결합하여 실험하고 분석하였다. 제안된 알고리즘은 기존의 SO를 적용한 결과와 같은 우수한 세선화 결과를 얻으며 학습시간은 O((logM)$^3$)인 복잡도를 가진다.