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

Discrete-Event Control and Optimization of Container Terminal Operations

This thesis discusses the dynamical modeling of complex container terminal operations. In the current literature, the systems are usually modeled in static way using linear programming techniques. This setting does not completely capture the dynamic aspects in the operations, where information about external factors such as ships and trucks arrivals or departures and also the availability of terminal's equipment can always change. We propose dynamical modeling of container terminal operations using discrete-event systems (DES) modeling framework. The basic framework in this thesis is the DES modeling for berth and quay crane allocation problem (BCAP) where the systems are not only dynamic, but also asynchronous. We propose a novel berth and QC allocation method, namely the model predictive allocation (MPA) which is based on model predictive control principle and rolling horizon implementation. The DES models with asynchronous event transition is mathematically analyzed to show the efficacy of our method. We study an optimal input allocation problem for a class of discrete-event systems with dynamic input sequence (DESDIS). We show that in particular, the control input can be obtained by the minimization/maximization of the present input sequence only. We have shown that the proposed approach performed better than the existing method used in the studied terminal and state-of-the-art methods in the literature

Simultaneous allocation and scheduling of quay cranes, yard cranes, and trucks in dynamical integrated container terminal operations

We present a dynamical modeling of integrated (end-to-end) container terminal operations using finite state machine (FSM) framework where each state machine is represented by a discrete-event system (DES) formulation. The hybrid model incorporates the operations of quay cranes (QC), internal trucks (IT), and yard cranes (YC) and also the selection of storage positions in container yard (CY) and vessel bays. The QC and YC are connected by the IT in our models. As opposed to the commonly adapted modeling in container terminal operations, in which the entire information/inputs to the systems are known for a defined planning horizon, in this research we use real-time trucks, crane, and container storage operations information, which are always updated as the time evolves. The dynamical model shows that the predicted state variables closely follow the actual field data from a container terminal in Tanjung Priuk, Jakarta, Indonesia. Subsequently, using the integrated container terminal hybrid model, we proposed a model predictive algorithm (MPA) to obtain the near-optimal solution of the integrated terminal operations problem, namely the simultaneous allocation and scheduling of QC, IT, and YC, as well as selecting the storage location for the inbound and outbound containers in the CY and vessel. The numerical experiment based on the extensive Monte Carlo simulation and real dataset show that the MPA outperforms by 3-6% both of the policies currently implemented by the terminal operator and the state-of-the-art method from the current literature

Load sequencing for double-stack trains

Les trains à empilement double sont une composante majeure du réseau de transport ferroviaire pour les conteneurs intermodaux dans certains marchés comme celui de l’Amérique du Nord. Le séquençage du chargement représente un problème opérationnel auquel font face les opérateurs de grues dans les cours de chargement lorsqu’ils ont pour tâche de placer les conteneurs sur un train. Le séquençage du chargement consiste à trouver une séquence de mouvements permettant d’extraire les conteneurs des piles dans lesquels ils sont entreposés afin de les placer sur le train. Le séquençage du chargement est interrelié avec la planification du chargement, processus dans lequel des conteneurs sont assignés à des placements spécifiques sur les wagons, afin de former un plan de chargement pour guider le séquençage. Le travail dans ce mémoire s’articule autour d’un article scientifique sur l’optimisation du séquençage du chargement pour les trains à empilement double. Dans cet article sont présentés des algorithmes basés sur la programmation dynamique, ainsi qu’une stratégie tirant avantage de plans de chargement développés afin de solutionner le séquençage pour des instances de chargement réalistes. Les résultats montrent que les heuristiques suggérées fonctionnent bien même pour des instances de grande taille. Ces dernières présentent une légère perte en qualité des solutions mais un temps d’exécution nettement inférieur aux méthodes exactes faisant défaut pour des instances de grande taille. L’analyse démontre également que l’utilisation de plans de chargement plus flexibles permet d’améliorer la qualité des solutions avec toutes les méthodes, ceci se faisant au coût d’un temps d’éxecution supérieur et l’absence d’une garantie de solution pour les heuristiques. Finalement, la planification et le séquençage simultané sont comparés avec l’approche successive utilisant les algorithmes developpés afin d’évaluer la performance relative des deux approches.Double-stack trains are an important component of the railroad transport network for containerized cargo in specific markets such as the North American one. The load sequencing is an operational problem commonly faced in rail terminals by crane operators when tasked with loading containers on the railcars of a train. The load sequencing problem aims to find an efficient sequence of container retrievals in the storage yard, where containers are stored in piles while awaiting departure by train. Load sequencing is interrelated with load planning, the assignment of containers to specific locations on the train, forming a load plan which guides the load sequencing. The work in this thesis is centered around a scientific paper on the optimization of load sequencing for double-stack trains. This paper proposes algorithms based on dynamic programming and a strategy leveraging the load plans, and assesses their performance in terms of computing time, tractability and solution quality on realistic instance sizes. The results show that the heuristics suggested to solve the load sequencing scale well for realistic instance size, managing to achieve a significantly reduced computing time with a small loss in solution quality compared to exact methods, which would often falter for larger instances. The analysis also illustrates how using a flexible load plan in the load sequencing significantly improves solution quality at the cost of greater computing requirements and lack of guaranteed solution for the heuristics. Finally, the paper compares the performance resulting from the successive application of load planning and sequencing with jointly performing the load planning and sequencing