• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2011.06a
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• pp.216-217
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• 2011

The problem of automated stacking cranes (ASC) dispatching in container terminals is addressed in this paper. We propose a heuristic-based ASC dispatching approach which adopts multi-criteria decision strategy. By aggregating different criteria the proposed strategy can consider multiple aspects of the dispatching situation and make robust decision in various situations. A multi-objective evolutionary algorithm (MOEA) is adopted to tune the weights associated to each criteria to minimize both the quay crane delay and external truck delay. The proposed approach is validated by comparison with different dispatching heuristics and simulation results obtained confirms its effectiveness.

• Journal of Navigation and Port Research
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• v.36 no.5
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• pp.387-394
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• 2012

In an automated container terminal, automated stacking cranes(ASCs) take charge of handling of containers in a block of the stacking yard. This paper proposes a multi-criteria strategy to solve the problem of job dispatching of twin ASCs which are identical to each another in size and specification. To consider terminal situation from different angles, the proposed method evaluates candidate jobs through various factors and it dispatches the best score job to a crane by doing a weighted sum of the evaluated values. In this paper, we derive the criteria for job dispatching strategy, and we propose a genetic algorithm to optimize weights for aggregating evaluated results. Experimental results are shown that it is suitable for real time terminal with lower computational cost and the strategy using various criteria improves the efficiency of the container terminal.

• Journal of Navigation and Port Research
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• v.38 no.2
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• pp.155-162
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• 2014

In container terminals, stacking yard is the place where import and export containers are temporarily stored before being loaded onto or after being discharged from a ship. Since all the containers go through the stacking yard in their logistic flow, the productivity of the terminal critically depends on efficient operation of stacking yard, which again depends on how well the stacking locations of the incoming containers are determined. However, a good location for stacking an incoming container later can turn out to be a bad one when that container is to be fetched out of the stacking yard, especially if some rehandling is required. This means that good locations for the containers are changing over time. Therefore, in most container terminals, the so-called remarshaling is done to move the containers from bad location to good locations. Although there are many previous works on remarshaling, they all assume that the remarshaling can be done separately from the main jobs when the cranes are idle for rather a long period of time. However, in reality, cranes are hardly available for a period long enough for remarshaling. This paper proposes a crane dispatching strategy that allows remarshaling jobs to be mixed together with the main jobs whenever an opportunity is detected. Experimental results by simulation reveals that the proposed method effectively contributes to the improvement of terminal productivity.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.11
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• pp.1106-1110
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• 2010

This paper proposes a method of optimizing a stacking strategy for an automated container terminal using multi-objective evolutionary algorithms (MOEAs). Since the yard productivities of seaside and landside are conflicting objectives to be optimized, it is impossible to maximize them simultaneously. Therefore, we derive a Pareto optimal set instead of a single best solution using an MOEA. Preliminary experiments showed that the population is frequently stuck in local optima because of the difficulty of the given problem depending on the yard occupancy rate. To cope with this problem, we propose another method of simultaneously optimizing two problems with different difficulties so that diverse solutions can be preserved in the population. Experimental results showed the proposed method can derive better stacking policies than the compared method solving a single problem given the same computational costs.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.359-367
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• 2005

In order to increase the productivity of container terminals, automation is being considered seriously in nowadays. A yard is usually automated by running autumated RMGs (rail mounted gantries) which may require somewhat a different stacking strategy to archive a better performance. In this paper, we present a simulation model for RMGs and summarize experimental results with two different stacking strategies applied to a horizontal block which has two non-crossable RMGs. The concentrating strategy, which stacks containers belong to a single ship together and dedicateds each RMG to either ship services or external truck services, showed a good performance in ship unloading. In the contrast, the distributing strategy, which partitions a block into two regions and binds each RMG to one of the regions to improve the productivity of ship services by running each RMG alternately, is suggested for blocks of exporting.

• The KIPS Transactions:PartB
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• v.11B no.1
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• pp.63-70
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• 2004

Port operation is largely divided into gate operation, yard operation and berth operation. Operation strategy and optimal resource allocation for three parts are important in the productivity of the port operation.. Especially the resource allocation planning in berth operation needs optimization, because it is directly connected with the processing time in shipping. Berth planning is not independent on recourse allocation but interrelated with yard stacking area allocation. Therefore, we design the optimized module of berth planning and give priority to interrelationship with yard space allocation, while existing studies design independent resource allocation in berth planning. We suggest constraints by mathematical method, and they are related to yard stacking area allocation with existing constraints. Then we look for solutions, use tabu search to optimize them, and design optimized the berth planning module. In the performance test of optimized module design of berth planning, we find that the berth planning with yard stacking area allocation takes less processing time than without yard stacking area allocation.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2005.11a
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• pp.442-450
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• 2005

One of the important tactical problems for the efficient operation of container terminals is to determine the usage of storage space There are two different strategies for stacking containers; mixing strategy. in which outbound containers and inbound containers are mixed in the same block. and segregating strategy, in which outbound containers and inbound containers are stacked in blocks different from each other. The performance of space allocation strategies also depends on the types of handling equipment in the yard and the number of handling equipment allocated to each block. A simulation model is developed considering various handling characteristics of yard cranes. Performances of various space and equipment allocation strategies are evaluated by using the simulation model.

• Journal of Intelligence and Information Systems
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• v.12 no.3
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• pp.47-65
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• 2006

These days, the number of automated container terminals is increasing to encourage the productivity of the container terminal inside and outside of the country. So far, there have been a lot of researches on the operating one type of equipments in a container terminal. However, there is still room for further improvement as none of equipment works itself but cooperate each other to finish a job, which means synchronization among the equipments is necessary. Among lots of equipments in a terminal, this paper concerns with the operation of ATCs (Automated Transfer Crane) and YTs (Yard Truck). The purpose of this paper is to find the efficient heuristic methods for operating ATCs and YTs that can set up a schedule in a real time. Moreover, using simulation this paper shows the efficient stacking strategy to decide the location of containers to be put and the proper selection range of YTs.

• The KIPS Transactions:PartA
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• v.10A no.5
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• pp.519-528
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• 2003

Container terminals need decisions in the course of daily-24 hour and 365 day - operations, and all these decisions are inter-related. The ultimate goal of Decision Support System is to minimize ship loading/unloading time, resources used to handle the workload, and congestion on the roads inside the terminal. It is also to make the best possible use of the storage space available. Therefore, the necessity of decision support tools are emphasized to enhance the operational efficiency of container shipping terminals more, because of limits and complexity of these decisions. So, in thia paper, we draw evaluation items for Decision Support Systems and suggest optimization strategy of evaluation items which have the greatest influence on Decision Support system, that is, yard stacking allocation, RTGC deployment among blocks, and YT allocation to QCs. We also estimate the efficiency of Decision Support System design by simulation using G2 language, comparing ship loading/unloading time.

• Journal of Intelligence and Information Systems
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• v.12 no.1
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• pp.125-137
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• 2006

One of the important tactical problems for the efficient operation of container terminals is to determine the usage of storage space There are two different strategies for stacking containers; mixing strategy, in which outbound containers and inbound containers are mixed in the same block, and segregating strategy, in which outbound containers and inbound containers are stacked in blocks different from each other The performance of space allocation strategies also depends on the types of handling equipment in the yard and the number of handling equipment allocated to each block. A simulation model is developed considering various handling characteristics of yard cranes. Performances of various space and equipment allocation strategies are evaluated by using the simulation model.