Kwang Ryel Ryu

Deriving stacking strategies for export containers with uncertain weight information

In a container terminal, export containers are usually classified into one of a few weight groups and those belonging to the same group are stored together on a same stack. The reason for this stacking by weight groups is that it becomes easy to have heavier containers be loaded onto a ship before lighter ones, which is important for the balancing of the ship. However, since the weight information available at the time of container arrival is only an estimate, containers belonging to different weight groups are often stored together on a same stack. This becomes the cause of extra moves, or re-handlings, of containers at the time of loading to fetch out the heavier containers placed under the lighter ones. In this paper, we propose a method based on a simulated annealing search to derive a good stacking strategy for containers with uncertain weight information. Simulation experiments have shown that our strategies more effectively reduce the number of re-handlings than the traditional same-weight-group-stacking strategy. Also, additional experiments have shown that further improvement can be obtained if we increase the accuracy of the weight classification by applying machine learning.

A beam search algorithm for the load sequencing of outbound containers in port container terminals

Abeam search algorithm was applied to solve the load-sequencing problem in port container terminals. The algorithm was used to maximize the operational efficiency of transfer cranes and quay cranes (QCs) while satisfying various constraints on stacking containers onto vessels. The load-sequencing problem consisted of two decision-making subproblems. In the first subproblem, a pickup schedule was constructed in which the travel route of a transfer crane (TC) as well as the number of containers it must pick up at each yard-bay are determined. In the second subproblem, the load sequence for individual containers was determined. This study suggested a search scheme in which an algorithm to solve the second subproblem is imbedded into the algorithm for the first subproblem. Numerical experiments using practical data were performed to test the performance of the developed algorithm.

Deadlock prevention for automated guided vehicles in automated container terminals

Automated guided vehicles (AGVs) are an important component for automating container terminals. When utilizing AGVs to transport containers from one position to another in a container terminal, deadlocks are a serious problem that must be solved before real operations can take place. This study assumes that the traveling area for AGVs is divided into a large number of grid-blocks, and, as a method of traffic control, grid-blocks are reserved in advance when AGVs are running. The first purpose of the reservation is to make room between AGVs and to prevent deadlocks. The objective of this study is to develop an efficient deadlock prediction and prevention algorithm for AGV systems in automated container terminals. Because the size of an AGV is much larger than the size of a grid-block on a guide path, this study assumes that an AGV may occupy more than one grid-block at a time. This study proposes a method for reserving grid-blocks in advance to prevent deadlocks. A graphical representation method is suggested for a reservation schedule and a priority table is suggested to maintain priority consistency among grid-blocks. It is shown that the priority consistency guarantees deadlock-free reservation schedules for AGVs to cross the same area at the same time. The proposed method was tested in a simulation study.

Real-time scheduling for twin RMGs in an automated container yard

This paper proposes heuristic-based and local-search-based real-time scheduling methods for twin rail-mounted gantry (RMG) cranes working in a block at an automated container terminal. The methods reschedule the cranes in real time for a given fixed-length look-ahead horizon whenever an RMG finishes a job. One difficulty with this problem is that sometimes additional rehandling of containers needs to be carried out in order to complete a requested job, especially when other containers are stacked on top of the target container. These rehandlings are the main cause of the delay of the crane operations, leading to extended waiting of automated guided vehicles (AGVs) or external trucks that co-work with the cranes. By treating the rehandling operations as independent jobs in our solution methods, we can greatly facilitate the cooperation between the two RMGs. Through this cooperation, the workload of the two RMGs can be better balanced and interference can be more easily avoided, thereby maximizing crane utilization. Simulation experiments show that the waiting times of AGVs and external trucks are significantly reduced due to the increased utilization through cooperation.

Comparison of operations of AGVs and ALVs in an automated container terminal

In an automated container terminal, the automated guided vehicles (AGVs) and the automated lifting vehicles (ALVs) are the most popular candidates to be used for transporting containers between the quayside and the storage yard. In this paper, we compare the operational productivities of the two types of vehicles when used in combination with the quay cranes of various performances. We assume a flexible path layout in which the vehicles can move almost freely in any vertical and horizontal directions. The traffic control scheme employed in our simulation finds a minimum- time route and schedules the travel to avoid deadlocks. Simulation experiments show that the ALVs reach the same productivity level as the AGVs using much less number of vehicles due to its self-lifting capability. However, the results also reveal that the AGVs eventually catch up the performance of the ALVs in most cases if the number of vehicles given is large enough. An exception is when the tandem double-trolley QCs are used for loading, in which case the AGVs cannot catch up the ALVs no matter how many more vehicles are added.

Planning for intra-block remarshalling in a container terminal

Intra-block remarshalling in a container terminal refers to the task of rearranging export containers scattered around within a block into designated target bays of the same block. Since the containers must be loaded onto a ship following a predetermined order, the rearrangement should be done in a way that containers to be loaded earlier are placed on top of those to be loaded later to avoid re-handlings. To minimize time to complete a remarshalling task, re-handlings should be avoided during remarshalling. Moreover, when multiple yard cranes are used for remarshalling, interference between the cranes should be minimized. In this paper, we present a simulated annealing approach to the efficient finding of a good intra-block remarshalling plan, which is free from re-handlings at the time of loading as well as during remarshalling.

Generating a rehandling-free intra-block remarshaling plan for an automated container yard

Intra-block remarshaling in a container terminal refers to the task of rearranging the export containers, which are usually scattered around within a block, into designated target bays within the same block. Since the containers must be loaded onto a ship following a predetermined order, the rearrangement should be performed in such a way that the containers to be loaded first are placed on top of those to be loaded later in order to avoid rehandling. To minimize the time required to complete a remarshaling task, rehandling should also be avoided during the remarshaling operations. Moreover, when multiple stacking cranes are used for the remarshaling, the interference between cranes should be minimized. This paper presents a method to efficiently search for an intra-block remarshaling plan which is free from rehandling during both the loading operation and remarshaling, and which minimizes the interference between the stacking cranes.

Planning for remarshaling in an automated container terminal using cooperative coevolutionary algorithms

The productivity of a container terminal is highly dependent on the efficiency of loading the containers onto the vessels. The efficiency of container loading depends on how the containers are stacked in the storage yard. Remarshaling refers to the preparatory task of rearranging the containers to maximize the efficiency of loading. In this paper, we propose cooperative coevolutionary algorithms (CCEAs) to derive a plan for remarshaling in an automated container terminal. CCEAs efficiently search for a solution in a reduced search space by decomposing a problem into subproblems. Our CCEA decomposes the problem into two subproblems: one for determining where to move the containers and the other for determining the movement priority. Simulation experiments show that our CCEA can derive a better plan in terms of the efficiency of both loading and remarshaling than other methods which are not based on the notion of problem decomposition.

Determination of storage locations for incoming containers of uncertain weight

In container terminals, heavier containers are loaded onto a ship before lighter ones to keep the ship balanced. To achieve efficient loading, terminal operators usually classify incoming export containers into a few weight groups and group containers belonging to the same weight group in the same stack. However, since the weight information available at the time of the container’s arrival is only an estimate, a stack often includes containers belonging to different weight groups. This mix of weight groups necessitates extra crane works or container re-handlings during the loading process. This paper employs a simulated annealing algorithm to derive a more effective stacking strategy to determine the storage locations of incoming containers of uncertain weight. It also presents a method of using machine learning to reduce occurrences of re-handling by increasing classification accuracy. Experimental results have shown that the proposed methods effectively reduce the number of re-handlings than the traditional same-weight-group-stacking (SWGS) strategy.

Optimization of container load sequencing by a hybrid of ant colony optimization and tabu search

Many algorithms that solve optimization problems are being developed and used. However, large and complex optimization problems still exist, and it is often difficult to obtain the desired results with one of these algorithms alone. This paper applies tabu search and ant colony optimization method to the container load sequencing problem. We also propose a hybrid algorithm, which can combine the merits of these two algorithms by running them alternately. Experiments have shown that the proposed hybrid algorithm is superior to both tabu search and ant colony optimization individually.