Competition among container ports continues to increase as the differentiation of hub ports and feeder ports progresses. Managers in many container terminals are trying to attract carriers by automating handling equipment, providing and speeding up various services, and furnishing the most current information on the flow of containers. At the same time, however, they are trying to reduce costs by utilizing resources efficiently, including human resources, berths, container yards, quay cranes, and various yard equipment. When planning berth usage, the berthing time and the exact position of each vessel at the wharf, as well as various quay side resources are usually determined in the process. Several variables must be considered, including the length overall (LOA) and arrival time of each vessel, the number of containers for discharging and loading, and the storage location of outbound/inbound containers to be loaded onto/discharged from the corresponding vessel. Furthermore, we aim to propose berthing plan that will be "robust", since the actual arrival time of each vessel can vary substantially from forecast. This is particular important for vessels from priority customers (called priority vessels hereon), who have been promised berth-on-arrival (i.e. within two hours of arriving) service guarantee in their contract with PSA. A robust plan will also helps to minimize the frequent updates (changes) to berthing plan that have repercussion in resource and sta deployment within the terminal. Thus, the problem reduces to one of finding a berthing plan, so that priority vessels can be berthed-on-arrival with high probability, and the vessels can be berthed as close to their preferred locations as possible, to reduce the cost of transporting the containers within the terminal. In this paper, we described an approach to address this problem. / Singapore-MIT Alliance (SMA)

Le problème d'allocation de postes d'amarrage (PAPA) est l'un des principaux problèmes de décision aux terminaux portuaires qui a été largement étudié. Dans des recherches antérieures, le PAPA a été reformulé comme étant un problème de partitionnement généralisé (PPG) et résolu en utilisant un solveur standard. Les affectations (colonnes) ont été générées a priori de manière statique et fournies comme entrée au modèle %d'optimisation. Cette méthode est capable de fournir une solution optimale au problème pour des instances de tailles moyennes. Cependant, son inconvénient principal est l'explosion du nombre d'affectations avec l'augmentation de la taille du problème, qui fait en sorte que le solveur d'optimisation se trouve à court de mémoire. Dans ce mémoire, nous nous intéressons aux limites de la reformulation PPG. Nous présentons un cadre de génération de colonnes où les affectations sont générées de manière dynamique pour résoudre les grandes instances du PAPA. Nous proposons un algorithme de génération de colonnes qui peut être facilement adapté pour résoudre toutes les variantes du PAPA en se basant sur différents attributs spatiaux et temporels. Nous avons testé notre méthode sur un modèle d'allocation dans lequel les postes d'amarrage sont considérés discrets, l'arrivée des navires est dynamique et finalement les temps de manutention dépendent des postes d'amarrage où les bateaux vont être amarrés. Les résultats expérimentaux des tests sur un ensemble d'instances artificielles indiquent que la méthode proposée permet de fournir une solution optimale ou proche de l'optimalité même pour des problème de très grandes tailles en seulement quelques minutes. / The berth allocation problem (BAP) is one of the key decision problems at port terminals and it has been widely studied. In previous research, the BAP has been formulated as a generalized set partitioning problem (GSPP) and solved using standard solver. The assignments (columns) were generated a priori in a static manner and provided as an input to the optimization model. The GSPP approach is able to solve to optimality relatively large size problems. However, a main drawback of this approach is the explosion in the number of feasible assignments of vessels with increase in problem size which leads in turn to the optimization solver to run out of memory. In this research, we address the limitation of the GSPP approach and present a column generation framework where assignments are generated dynamically to solve large problem instances of the berth allocation problem at port terminals. We propose a column generation based algorithm to address the problem that can be easily adapted to solve any variant of the BAP based on different spatial and temporal attributes. We test and validate the proposed approach on a discrete berth allocation model with dynamic vessel arrivals and berth dependent handling times. Computational experiments on a set of artificial instances indicate that the proposed methodology can solve even very large problem sizes to optimality or near optimality in computational time of only a few minutes.