Stowage Planning System for Ferry Ro-Ro Ships Using Particle Swarm Optimization Method

Stowage planning involves distributing cargo on board a ship, including quantity, weight, and destination details. It consists of collecting cargo manifest data, planning cargo location on decks, and calculating stability until the vessel is declared safe for sailing. Finding the ideal solution to real-world situations in this stowage planning problem is challenging and frequently requires a very long computing period. The Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms known for its efficient performance. PSO has been extended to complex optimization problems due to its fast convergence and easy implementation. In this study, the Particle Swarm Optimization (PSO) method is implemented to automate stowage arrangements on ships considering three factors (width, length, and weight of the vehicle). This system was evaluated with KMP Legundi vehicle manifest data and four load cases of 12 different vehicle types that can be loaded on Ferry / Ro-Ro Ships. It provides complete vehicle layouts and allows interactive changes for stowage planners, ensuring speed and accuracy in arranging ship cargo.Stowage planning involves distributing cargo on board a ship, including quantity, weight, and destination details. It consists of collecting cargo manifest data, planning cargo location on decks, and calculating stability until the vessel is declared safe for sailing. Finding the ideal solution to real-world situations in this stowage planning problem is challenging and frequently requires a very long computing period. The Particle Swarm Optimization (PSO) algorithm is one of the evolutionary algorithms known for its efficient performance. PSO has been extended to complex optimization problems due to its fast convergence and easy implementation. In this study, the Particle Swarm Optimization (PSO) method is implemented to automate stowage arrangements on ships considering three factors (width, length, and weight of the vehicle). This system was evaluated with KMP Legundi vehicle manifest data and four load cases of 12 different vehicle types that can be loaded on Ferry / Ro-Ro Ships. It provides complete vehicle layouts and allows interactive changes for stowage planners, ensuring speed and accuracy in arranging ship cargo

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

Column Generation for the Container Relocation Problem

Container terminals offer transfer facilities to move containers from vessels to trucks, trains and barges and vice versa. Within the terminal the container yard serves as a temporary buffer where incoming containers are piled up in stacks. Only the topmost container of each stack can be accessed. If another container has to be retrieved, containers stored above it must be relocated first. Containers need to be transported to a ship or to trucks in a predefined sequence as fast as possible. Generally, this sequence does not match the stacking order within the yard. Therefore, a sequence of retrieval and relocation movements has to be determined that retrieves containers from the bay in the prescribed order with a minimum number of relocations. This problem is known as the container relocation problem. We apply an exact and a heuristic column generation approach to this problem. First results are very promising since both approaches provide very tight lower bounds on the minimum number of relocations

The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the ‘big data’ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research

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

Un algoritmo en línea para el problema de apilamiento de contenedores

El manejo eficiente de carga es un elemento clave para un puerto marítimo pueda competir y proveer adecuados niveles de servicio a sus usuarios. El desempeño de un puerto depende del tiempo de permanencia de la nave, que está condicionado por la eficiencia en las operaciones de carga y descarga de las naves. En el patio, los contenedores son almacenados temporalmente para ser cargados a la nave o despachados a los usuarios externos con un alto impacto en los tiempos de atención de las naves. Este artículo propone una política para stacking de contenedores, considerando las características particulares de un terminal de contenedores en Chile. Para medir el desempeño de este procedimiento, se propone una cota superior para el número de despejes de un contenedor en función de la capacidad de los bloques. Se presentan resultados numéricos en comparación con la cota superior, mostrando un buen desempeño del procedimiento propuesto.Efficient cargo handling is a key element for a maritime port to compete and provide good service levels to its users. The performance of a port is related to ship-turnaround, which is conditioned by the ships loading and unloading operational efficiency. At the yard, containers are temporarily stacked in order to later either load them onto a ship or dispatch them to external users. Stacking has a strong impact on ships’ service times. This paper proposes a container stacking policy, considering the particular characteristics of a container terminal in Chile. In order to measure the performance of the procedure, an upper bound for the number of re-handles of containers is estimated as a function of the block’s capacity. Numerical results are provided in comparison to an upper bound, and a good performance by the proposed procedure is demonstrated

Improving container terminal efficiency: New models and algorithms for Premarshalling and Stowage Problems

El desarrollo del contenedor ha revolucionado el comercio marítimo de mercancías, permitiendo la manipulación de carga de diversos tipos y dimensiones con un costo reducido y disminuyendo el costo de importación de muchos productos, En la actualidad, aproximadamente el 90\% de la carga no a granel en todo el mundo se transporta en buques portacontenedores, cuyas capacidades han llegado a sobrepasar los 20000 TEUs (\emph{Twenty-foot Equivalent Unit}, unidad de medida correspondiente a un contenedor normalizado de 20 pies). Las terminales de contenedores tienen que hacer frente al creciente volumen de carga transportada, al aumento del tamaño de las naves y a las alianzas de las navieras. En este contexto, deben competir por menos servicios de barcos cada vez más grandes. Para ello, deben aumentar su eficiencia, optimizando los recursos existentes. En esta tesis se estudian dos problemas de optimización combinatoria, el problema de premarshalling y el problema de la estiba, que surgen en el patio y en el muelle de las terminales de contenedores, antes y durante las operaciones de carga y descarga de los buques, y cuya resolución deriva en una disminución del tiempo de atraque y, por lo tanto, en un aumento de la eficiencia de las terminales. El problema de premarshalling prepara el patio de contenedores antes de la llegada del buque, usando las grúas de patio cuando la carga de trabajo es mínima, con el fin de evitar un mayor número de recolocaciones a la llegada del buque y así acelerar los tiempos de servicio. El objetivo clásico de este problema ha sido reducir al mínimo el número de movimientos necesarios para eliminar los contenedores que bloquean la retirada de otros dentro de una bahía. De este modo, el número de movimientos se ha tomado como un indicador del tiempo de grúa. No obstante, en esta tesis se prueba que considerando como objetivo el tiempo real que la grúa emplea en realizar los movimientos, se puede reducir hasta un 24\% el tiempo total empleado. Para la resolución de ambos problemas, el premarshalling con función objetivo clásica y el premarshalling con la nueva función objetivo, se han desarrollado diversos modelos matemáticos y algoritmos Branch and Bound con nuevas cotas superiores e inferiores, reglas de dominancia y algoritmos heurísticos integrados en el proceso de ramificación. Por lo que respecta al problema de la estiba, se ha estudiado el problema multi-puerto que busca obtener un plan de estiba del barco de modo que se reduzca al mínimo el número total de movimientos improductivos en las operaciones de carga y descarga a lo largo de la ruta en la que presta servicio. Comenzamos estudiando el problema simplificado, en el que no se consideran restricciones de tamaño ni de peso de los contenedores, y progresivamente se van introducido restricciones más realistas, desarrollando modelos matemáticos, heurísticas, metaheurísticas y mateheurísticas. Estos procedimientos son capaces de resolver instancias de gran tamaño correspondientes a los barcos de mayor capacidad que se encuentran actualmente en el sector.The development of containers has revolutionized maritime trade by making it possible to handle various types and sizes of cargo at a reduced cost, lowering the import cost of many products to such an extent that it is sometimes cheaper to transport goods to the other side of the world than to produce them locally. Nowadays, about 90 per cent of non-bulk cargo worldwide is carried on container ships with capacities exceeding 20,000 TEUs (Twenty-foot Equivalent Units). Container terminals have to cope with the increase in the volumes of cargo transported, the ever-larger ships, and the consolidation of shipping companies. In this context, they have to compete for fewer calls of larger ships. Since they cannot simply increase the number of cranes indefinitely, they have to improve efficiency by optimizing the available resources. This thesis studies two combinatorial optimization problems, the premarshalling problem and the stowage problem. These problems arise in the yard and the seaside of container terminals, before and during the loading and unloading operations of the ships, and make it possible to reduce the berthing time and thus to increase container terminal efficiency. The premarshalling problem prepares the container yard before the arrival of the ship, using the yard cranes when the workload at the terminal is at a minimum to rearrange the yard in order to avoid container relocations when the vessel arrives and to speed up the service times. The classic objective of this problem is to minimize the number of movements required to remove containers blocking the retrieval of others within a bay. Thus, the number of movements has been used as an indicator of crane time. However, this thesis shows that considering the real time that the crane takes to perform the movements as the target, the total time spent by the crane can be cut down up to 24 per cent. To solve both problems, premarshalling with the classic objective function and premarshalling with the new objective function, this thesis develops several mathematical models and branch and bound algorithms with new upper and lower bounds, dominance rules and heuristic algorithms integrated in the branching process. With regard to the stowage problem, the multi-port problem is addressed, seeking to obtain a stowage plan for the ship so as to minimize the total number of unproductive moves in the loading/unloading operations along the trade route of the ship. We start with a simplified problem, in which no size and weight constraints are considered, and progressively introduce more realistic constraints, developing mathematical models, metaheuristics, and matheuristics. These procedures are able to solve very large instances, corresponding to the largest ships in service