The service time of the vessels is one of the main indicators of ports’ competitiveness. This, together with the increasing volume of bulk transportation, make the efficient management of scarce resources such as berths a crucial option for enhancing the productivity of the overall terminal. In real scenarios, the information available to port operators may vary once the planning has been elaborated. Unforeseen events, errors, or modifications in the available information can lead to inefficient terminal management and the initial scheduling might become unfeasible. This implies that the use of deterministic approaches may not be enough to maximize productivity. Therefore, in this work, proactive simulation–optimization approaches that utilize the information collected during the simulation for guiding the optimization search to provide robust solutions are proposed. Moreover, a multi-objective approach based on the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) for jointly tackling the problem objective as well as the deviations because of stochastic changes is developed. Finally, we also investigate the contribution of time management strategies such as buffers to absorb stochastic modifications and hence increase solutions’ robustness. The computational results indicate, on the one hand, the benefit of integrating both types of objectives (i.e., deterministic and stochastic) to guide the simulation–optimization process, and on the other hand, the benefit of using the multi-objective approaches like NSGA-II. Finally, the incorporation of buffers leads to better performance in terms of reducing penalty costs due to disruptions, shortening the planning risks related to only considering deterministic planning.

船只的服务时间是港口竞争力的主要指标之一。加上散装运输量的增加，有效管理稀有资源（例如泊位）成为提高整个码头生产率的关键选择。在实际情况下，一旦制定了计划，港口运营商可用的信息可能会有所不同。不可预见的事件，错误或对可用信息的修改可能会导致终端管理效率低下，并且初始调度可能变得不可行。这意味着使用确定性方法可能不足以使生产率最大化。因此，在这项工作中，提出了一种主动仿真-优化方法，该方法利用在仿真过程中收集的信息来指导优化搜索以提供可靠的解决方案。此外，开发了一种基于非支配排序遗传算法II（NSGA-II）的多目标方法，用于共同解决问题目标以及随机变化引起的偏差。最后，我们还研究了时间管理策略（例如缓冲区）对吸收随机修改的影响，从而提高了解决方案的健壮性。计算结果表明，一方面，整合两种类型的目标（即确定性和随机性）以指导仿真优化过程的好处，另一方面，使用诸如NSGA的多目标方法的好处-II。最后，