A genetic algorithm for robust berth allocation and quay crane assignment

The final publication is available at Springer via http://dx.doi.org/10.1007/s13748-014-0056-3[EN] 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 times.This research was supported by the Spanish Government under research projects TIN2010-20976-C02-01 and TIN2010-20976-C02-02 (Min. de Ciencia e Innovación, Spain), the project PIRSES-GA-2011-294931 (FP7-PEOPLE-2011-IRSES) and the predoctoral FPU fellowship (AP2010-4405).Rodríguez Molins, M.; Ingolotti Hetter, LP.; Barber Sanchís, F.; Salido Gregorio, MA.; Sierra, MR.; Puente, J. (2014). A genetic algorithm for robust berth allocation and quay crane assignment. Progress in Artificial Intelligence. 2(4):177-192. https://doi.org/10.1007/s13748-014-0056-3S1771922