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Rodríguez Molins, M.; Ingolotti Hetter, LP.; Barber Sanchís, F.; Salido Gregorio, MA.; R. Sierra, M.; Puente, J. (2014). A genetic algorithm for robust berth allocation and quay crane assignment. Progress in Artificial Intelligence. 2(4): 177-192. doi:10.1007/s13748-014-0056-3. A Genetic Algorithm for Robust Berth Allocation and Quay Crane AssignmentAbstract 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 in order 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 times.

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Maritime container terminals are facilities where cargo containers are transshipped between ships or between ships and land vehicles (tucks or trains). These terminals involve a large number of complex and combinatorial problems. One of them is related to the Container Stacking Problem. A container yard is a type of temporary store where containers await further transport by truck, train or vessel. The main efficiency problem for an individual stack is to ensure easy access to containers at the expected time of transfer.Stacks are 'last-in, first-out' storage structures where containers are stocked in the order they arrive. But they should be retrieved from the stack in the order (usually different) they should be shipped. This retrieval operation should be efficiently performed, since berthing time of vessels and the terminal operations should be optimized. To do this, cranes can relocate containers in the stacks to minimize the rearrangements required to meet the expected order of demand for containers.In this paper, we present a domain-dependent heuristically guided planner for obtaining the optimized reshuffling plan, given a stacking state and a container demand. The planner can also be used for finding the best allocation of containers in a yard-bay in order to minimize the number of reshuffles as well as to be used for simulation tasks and obtaining conclusions about possible yard configurations.