The ever increasing demand for container transportation has led to the congestion of maritime container terminals in the world. In this work, the two interrelated problems of berth and quay crane scheduling are considered in an integrated multiobjective mathematical model. A special character of this model is that the arrival times of vessels and the failure (working) times of quay cranes are not deterministic and can vary based on some scenarios. Hence, a robust model is devised for the problem having three objectives of minimising the deviations from target berthing locations and times as well as departure delays of all vessels. This robust optimisation seeks to minimise the value of the objectives regarding all the scenarios. An exact solution approach based on the 𝜖-constraint method by the Gurobi software is applied. Moreover, regarding the complexity of the problem, two Simulated Annealing (SA) based metaheuristics, namely a Multi-Objective Simulated Annealing (MOSA) and a Pareto Simulated Annealing (PSA) approach are adapted with a novel solution encoding scheme. The three methods are compared based on some multiobjective metrics and a statistical test. The advantage of the integration of berth and quay crane scheduling is examined as well.

对集装箱运输的不断增长的需求导致世界海上集装箱码头的拥挤。在这项工作中，在一个集成的多目标数学模型中考虑了泊位和码头起重机调度这两个相互关联的问题。该模型的一个特殊特征是船只的到达时间和码头起重机的失效（工作）时间不是确定性的，并且会根据某些情况而变化。因此，针对该问题设计了鲁棒的模型，其具有三个目标，该目标最小化与目标停泊位置和时间的偏差以及所有船只的离港延误。这种强大的优化旨在使有关所有方案的目标价值最小化。基于该准确的解决方案方法ε使用Gurobi软件的-constraint方法。此外，关于问题的复杂性，两种新颖的基于解决方案编码方案的方法都适用于基于模拟退火（SA）的元启发式算法，即多目标模拟退火（MOSA）和帕累托模拟退火（PSA）方法。基于一些多目标指标和统计检验对这三种方法进行了比较。泊位和码头起重机调度相结合的优势也得到了检验。