This paper introduces a new optimization model that integrates the multi-port stowage planning problem with the container relocation problem. This problem is formulated as a binary mathematical programming model that must find the containers' move sequence so that the number of relocations during the whole journey of a ship, as well as the associated port yards is minimized. Modeling by binary variables to represent the cargo status in a ship and yards makes the problem very complex to be solved by exact methods. To the best of our knowledge, this integrated model has not been developed yet as that such problems are always addressed in a partitioned or hierarchical way. A demonstration of the benefits of an integrated approach is given. The model is solved in two different commercial solvers and the results for randomly generated instances are presented and compared to the hierarchical approach. Two heuristics approaches are proposed to quickly generate feasible solutions for warm-starting the model. Extensive computational tests are performed and the results indicate that the solution approaches can reach optimal solutions for small sized instances and good quality solutions on real-scale based instances within reasonable computation time. This is a promising model to support decisions in

The greater flow of containers in global supply chains requires ever-increasing productivity at port terminals. The research found in the literature has focused on optimizing specific parts of port operations but has ignored important features, such as the stack-wise organization of containers in a container ship and port yard and the effects of interconnection among the operations in both places. The objective of this paper is to show the importance of designing an integrated plan of the container relocation problem at the port yard with the stowage planning problem for loading and unloading a ship through ports. Both individual problems are NP-Complete, and exact approaches for each problem are only able to find optimal or feasible solutions for small instances. We describe a simulation-optimization methodology that combines simulation, a genetic algorithm, and a new solution representation based on rules. The test results show that the solution from the integrated plan is mutually beneficial for port yards and ship owners.

The efficiency of a port terminal is essential to allow the increase of the flow of containers in a global supply chain. In this work, it is proposed the integration of the problem of moving containers in the yard with the ship stowage plan problem. In literature it is proven that both problems are NP-Complet, therefore, it is proposed to adapt a method that has been successfully employed in solving the problem of the storage plan: the rules representation. The rules define over n ports, how the sequence of loading and unloading containers will occur and aims to reduce the number of unnecessary movements. The practical contribution is given by the reduction in the amount of information needed to represent the decision-making process through a mathematical model. In addition, the rules representation have the advantage of using a very compact representation that ensures the generation of feasible solutions. The results obtained with numerical examples show that with low computational time it has been possible to obtain sequences of feasible movements.

Nesta tese é desenvolvido um modelo de otimização que integra o problema do plano de estiva para múltiplos portos com o problema de realocação de contêineres no pátio portuário, chamado de CRP-MPSP. O CRP-MPSP é formulado como um modelo puramente binário, que deverá encontrar uma sequência de movimentação dos contêineres de forma que o número de remanejamentos, considerando toda a jornada de um navio e os pátios portuários associados, seja minimizado. O uso de variáveis binárias para representar o estado do navio e dos pátios torna este um problema de alta complexidade. A modelagem aqui apresentada ainda não foi desenvolvida na literatura como um modelo uniĄcado, visto que tais problemas tendem a ser tratados de forma particionada. Duas regras heurísticas são propostas para gerar soluções factíveis que serão utilizadas como warm-start pelo método de solução exato. O CRP-MPSP é implementado em dois solvers comerciais diferentes e os resultados para instâncias geradas aleatoriamente são apresentados. Extensivos testes computacionais são realizados. Os resultados indicam que a abordagem de solução pode obter soluções ótimas em instâncias de tamanho pequeno e soluções de boa qualidade em instâncias de tamanho maior, em tempo computacional razoável.

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