Most of the operational problems in container terminals are strongly interconnected. In this paper, we study the integrated berth allocation and quay crane assignment problem in seaport container terminals. We will extend the current stateof-the-art by proposing novel set partitioning models. To improve the performance of the set partitioning formulations, a number of variable reduction techniques are proposed. Furthermore, we analyze the eects of dierent discretization schemes and the impact of using a time-variant/invariant quay crane allocation policy. Computational experiments show that the proposed models signicantly improve the benchmark solutions of the current state-of-art optimal approaches.

Abstract. Eco-Efficient stowage plans that are both competitive and sustainable have become a priority for the shipping industry. Stowage planning is NP-hard and is a challenging optimization problem in practice. We propose a new 2-phase approach that generates near-optimal stowage plans and fulfills industrial time and quality requirements. Our approach combines an integer programming model for assigning groups of containers to storage areas of the vessel over multiple ports, and a constraint programming and local search procedure for stowing individual containers.

This paper presents the exible containership loading problem for seaport container terminals. The integrated management of loading operations, planning of the transport vehicles to use and their scheduling is what we dene as the Flexible Ship Loading Problem (FSLP). The exibility comes from a cooperative agreement between the terminal operator and the liner shipping company, specifying that the terminal has the right to decide which specic container to load for each slot obeying the class-based stowage plan received from the liner. We formulate a mathematical model for the problem. Then we present various modelling enhancements and a mathematical model to obtain strong lower bounds. We also propose a heuristic algorithm to solve the problem. It is shown that enhancements improve the performance of formulation signicantly, and the heuristic eciently generates high-quality solutions. Results also point out that substantial cost savings can be achieved by integrating the ship loading operations.

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This work presents a generalization of the Slot Planning Problem which raises when the liner shipping industry needs to plan the placement of containers within a vessel (stowage planning). State-of-the-art stowage planning relies on a heuristic decomposition where containers are first distributed in clusters along the vessel. For each of those clusters a specific position for each container must be found. Compared to previous studies, we have introduced two new features: the explicit handling of rolled out containers and the inclusion of separations rules for dangerous cargo. We present a novel integer programming formulation and a Greedy Randomized Adaptive Search Procedure (GRASP) to solve the problem. The approach is able to find high-quality solution within 1 second. We also provide comparison with the state-of-the-art on an existing and a new set of benchmark instances.