A genetic algorithm for robust berth allocation and quay crane assignment

Scheduling problems usually obtain the optimal solutions assuming that the environment is deterministic. However, actually the environment is dynamic and uncertain. Thus, the initial data could change and the initial schedule obtained might be unfeasible. To overcome this issue, a proactive approach is presented for scheduling problems without any previous knowledge about the incidences that can occur. In this paper, we consider the berth allocation problem and the quay crane assignment problem as a representative example of scheduling problems where a typical objective is to minimize the service time. The robustness is introduced within this problem by means of buffer times that should be maximized to absorb possible incidences or breakdowns. Therefore, this problem becomes a multi-objective optimization problem with two opposite objectives: minimizing the total service time and maximizing the robustness or buffer time

Real Time Recovery in Berth Allocation Problem in Bulk Ports

El actor español Cosme Pérez constituye un caso único en la historia del teatro. Tras crear el personaje de Juan Rana, a comienzos de la década de los años treinta del siglo xvii (única auténtica máscara, equiparable a las de la commedia dell'arte italiana, del teatro español), actor y personaje llegaron a fundirse de tal manera que el público ya no pudo concebir la posibilidad de que otro actor "se atreviera" a interpretarlo. Tras la muerte de Cosme, la extraordinaria fama alcanzada por Rana durante casi cuarenta años sobre las tablas motivó a algunos dramaturgos a intentar resucitarlo escénicamente, para perpetuar su éxito. En este articulo se estudian esos intentos, la suerte que corrieron y las consecuencias que el paso del tiempo tuvo en él, convirtiéndolo en un personaje diverso al primigenio

Assessment of Quay and Yard Transshipment Operations Under Proximity Limitations in Multi-Terminal Container Ports

The assignment of storage locations and space has a considerable impact on the performance of container terminals. This holds especially in multi-terminal transshipment ports where the planning of inbound and outbound container flows needs to consider space limitations and travel distances for reallocations, causing both intra- and inter-terminal transports. Thus, in this work, we study the impact of closeness limitations on quay and yard areas when conducting transshipment operations at multi-terminal transshipment ports. In doing so, a mathematical formulation and several scenarios covering different distance policies for limiting the allocation of containers before vessel loading or unloading operations are assessed. At a tactical level, this paper provides insights on assignment decisions while assessing distance-based policies that can be incorporated in practice

The berth allocation problem at port terminals : a column generation framework

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

Optimization in container liner shipping

We will give an overview of several decision problem encountered in liner shipping. We will cover problems on the strategic, tactical and operational planning levels as well as problems that can be considered at two planning levels simultaneously. Furthermore, we will shortly discuss some related problems in terminals, geographical bottlenecks for container ships and provide an overview of operations research methods used in liner shipping problems. Thereafter, the decision problems will be illustrated using a case study for six Indonesian ports