Managing optimum workload through terminal appointment system (TAS) : Case of Jakarta International Container Terminal

The Dissertation is a study of optimizing terminal through Terminal Appointment System (TAS), comparing the impact of unscheduled truck arrival with regulated truck arrival to the optimization process. A brief look is taken at present growth in container business. The rapid growth of world container trade, especially in Asia brings economic potentials for the countries and also challenges for container terminals. Capacity limitation in accommodating the trade growth forces the terminal to optimize their existing equipment and facilities. The evaluation of the existing performance of each component of the terminal operation, covering quay, yard and gate operation, is a starting point to identify the crucial problem in the optimization process. Many terminals implement Terminal Appointment System (TAS) to optimize their operation. This dissertation discuss the benefit and the impact of TAS implementation to the container terminal operation. The concluding chapter examines the impact of TAS solution in optimizing terminal operation. Some recommendations are made concerning the implementation of TAS

Strategies for dynamic appointment making by container terminals

We consider a container terminal that has to make appointments with barges dynamically, in real-time, and partly automatic. The challenge for the terminal is to make appointments with only limited knowledge about future arriving barges, and in the view of uncertainty and disturbances, such as uncertain arrival and handling times, as well as cancellations and no-shows. We illustrate this problem using an innovative implementation project which is currently running in the Port of Rotterdam. This project aims to align barge rotations and terminal quay schedules by means of a multi-agent system. In this\ud paper, we take the perspective of a single terminal that will participate in this planning system, and focus on the decision making capabilities of its intelligent agent. We focus on the question how the terminal operator can optimize, on an operational level, the utilization of its quay resources, while making reliable appointments with barges, i.e., with a guaranteed departure time. We explore two approaches: (i) an analytical approach based on the value of having certain intervals within the schedule and (ii) an approach based on sources of exibility that are naturally available to the terminal. We use simulation to get insight in the benefits of these approaches. We conclude that a major increase in utilization degree could be achieved only by deploying the sources of exibility, without harming the waiting time of barges too much

Relating Planner Task Performance for Container Terminal Operations to Multi-Tasking Skills and Personality Type

Planning the operations within a container terminal is a complex task. It requires planners to demonstrate adaptive behavior while handling stressful, complex, and unexpected situations in today’s dynamic and technology dependent workplace. This paper aims at investigating the role of multi-tasking ability, moderated by an individual’s personality type, in predicting planner task performance using simulation gaming methods. Hierarchical regression analysis results demonstrate that the direct effect of multi-tasking ability on performance is positive and significant. With one exception, the personality traits do not significantly intensify or lessen the impact of multi-tasking in predicting task performance. The personality trait, openness to experience, significantly lessens the impact of multi-tasking ability on performance. Our results suggest that container terminal operators may benefit by considering the above-mentioned results while allocating planning tasks to their employees and new recruits. The instruments used in this research could also be used for evaluating and training candidate planners

Research on the Prediction of Quay Crane Resource Hour based on Ensemble Learning

In Container terminals, a quay crane’s resource hour is affected by various complex nonlinear factors, and it is not easy to make a forecast quickly and accurately. Most ports adopt the empirical estimation method at present, and most of the studies assumed that accurate quay crane’s resource hour could be obtained in advance. Through the ensemble learning (EL) method, the influence factors and correlation of quay crane’s resources hour were analyzed based on a large amount of historical data. A multi-factor ensemble learning estimation model based quay crane’s resource hour was established. Through a numerical example, it is finally found that Adaboost algorithm has the best effect of prediction, with an error of 1.5%. Through the example analysis, it comes to a conclusion: the error is 131.86% estimated by the experience method. It will lead that subsequent shipping cannot be serviced as scheduled, increasing the equipment wait time and preparation time, and generating additional cost and energy consumption. In contrast, the error based Adaboost learning estimation method is 12.72%. So Adaboost has better performance

Sea Container Terminals

Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations

Dynamic Quay Crane Allocation

We introduce simple rules for quay cranes to handle containers along a berth where vessels arrive continuously in time. We first analyze a model where workload is continuous. Our analysis shows that if the system is configured properly, it will always converge to a state with the maximum possible throughput regardless of external disruptions or changes in workload. Numerical simulations based on a discrete workload model suggest that, by following the same rules, the system can still converge to state with throughput that is very close to its upper bound

Evaluating impact of truck announcements on container stacking efficiency

Container stacking rules are an important factor in container terminal efficiency. We build on prior research and use a discrete-event simulation model to evaluate the impact of a truck announcement system on the performance of online container stacking rules. The information that is contained in the announcement, i.e., the expected departure time for an import container, can be used to schedule pre-emptive remarshall moves. These moves can then be performed when the workload is low in order to decrease the export time and the crane workload at peak times