Ri Choe

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.

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.

Dual-population genetic algorithm for nonstationary optimization

In order to solve nonstationary optimization problems efficiently, evolutionary algorithms need sufficient diversity to adapt to environmental changes. The dual-population genetic algorithm (DPGA) is a novel evolutionary algorithm that uses an extra population called the reserve population to provide additional diversity to the main population through crossbreeding. Preliminary experimental results on various periods and degrees of environmental change have shown that the distance between the two populations of DPGA is one of the most important factors that affect its per-formance. However, it is very difficult to determine the best popu-lation distance without prior knowledge about the given problem. This paper proposes a new DPGA that uses two reserve populations (DPGA2). The reserve populations are at different distances from the main population. The information inflow from the reserve populations is controlled by survival selection. Experimental results show that DPGA2 shows a better performance than other evolutionary algorithms for nonstationary optimization problems without relying on prior knowledge about the problem.

Adjusting population distance for the dual-population genetic algorithm

A dual-population genetic algorithm (DPGA) is a new multipopulation genetic algorithm that solves problems using two populations with different evolutionary objectives. The main population is similar to that of an ordinary genetic algorithm, and it evolves in order to obtain suitable solutions. The reserve population evolves to maintain and offer diversity to the main population. The two populations exchange genetic materials using interpopulation crossbreeding. This paper proposes a new fitness function of the reserve population based on the distance to the main populations. The experimental results have shown that the performance of DPGA is highly related to the distance between the populations and that the best distance differs for each problem. Generally, it is difficult to decide the best distance between the populations without prior knowledge about the problem. Therefore, this paper also proposes a method to dynamically adjust the distance between the populations using the distance between good parents, i.e., the parents that generated good offspring.

Real-time scheduling for non-crossing stacking cranes in an automated container terminal

This paper proposes a local-search-based real-time scheduling method for non-crossing stacking cranes in an automated container terminal. Considering the dynamic property of the yard crane operation and real-time constraints, the method builds a new crane schedule for a fixed-length look-ahead horizon whenever a new crane job is requested. One difficulty in crane scheduling is that sometimes additional crane operations need to be done to complete a requested job, especially when other containers are stacked on top of the requested container. We use a redundant and variable-length representation of a candidate solution for search to accommodate those additional operations. Simulation experiment shows that the local-search-based method outperforms heuristic-based method in real-time situations.

Real-time scheduling of twin stacking cranes in an automated container terminal using a genetic algorithm

We address the problem of scheduling twin automated stacking cranes (ASCs) used in automated container terminals. By extending the previous works, we show that it is important to make explicit the hidden jobs needed to prepare for the main requested jobs. Since the preparatory jobs can be done by any of the two ASCs, appropriate assignment of these jobs can help to promote cooperation and avoid interference between the two ASCs. The proposed genetic algorithm (GA) performs search within the framework of iterative rescheduling to cope with the uncertainty of ASC operation. To boost the search performance under tight real-time constraint of iterative rescheduling, our GA uses some of the solutions of the previous iteration to initialize the population of the current iteration. It has also been shown that our GA performs more robustly than other algorithm such as simulated annealing in an uncertain environment.

Queue-based local scheduling and global coordination for real-time operation control in a container terminal

To maximize the productivity of a container terminal, the operations of various types of equipments should be optimized and synchronized in real time. However, use of optimization techniques such as mathematical programming or search-based meta-heuristics becomes difficult when given a large-scaled problem due to their high computational cost. Addressing this problem, the queue-based local scheduling and global coordination method proposed in this paper stands as a viable alternative. The method consists of the following steps. First, separate schedules are locally generated for each equipment type using a queue-based dispatching heuristic which pays attention to the queue lengths of the quay cranes (QCs) under service. Next, the schedules are executed via a simulation and a notable QC delay is identified. Based on the analysis on the causes of this delay, some compromising adjustments are made to the priorities of relevant jobs. Then, the localized scheduling followed by the adjustment is repeated until the termination condition is met. Adopting simple heuristics in the local scheduling phase, the overall process easily meets the real-time constraint, yet producing an integrated schedule with a better global perspective than the myopic heuristic-only approach.

Deadlock-free Routing of an AGV in Accelerated Motion

In the environment where multiple AGVs(Automated Guided Vehicles) operate concurrently in limited space, collisions, deadlocks, and livelocks which have negative effect on the productivity of AGVs occure more frequently. The accelerated motion of an AGV is also one of the factors that make the AGV routing more difficult because the accelerated motion makes it difficult to estimate the vehicle`s exact travel time. In this study, we propose methods of avoiding collisions, deadlocks, and livelocks using OAR(Occupancy Area Reservation) table, and selecting best route by estimating the travel time of an AGV in accelerated motion. A set of time-driven simulation works validated the effectiveness of the proposed methods.

Automated Stacking Crane Dispatching Strategy in a Container Terminal using Genetic Algorithm

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.