This study considers the Dynamic Minimum Cost Hybrid Berth Allocation Problem (DMCHBAP) with fixed handling times of vessels. The objective function to be minimized consists of three components: costs of positioning, waiting, and tardiness of completion for all vessels. A mathematical formulation of DMCHBAP, based on Mixed Integer Linear Programming (MILP), is proposed and used within the framework of commercial CPLEX 12.3 solver. As the speed of finding high-quality solutions is of crucial importance for an efficient and reliable decision support system in container terminal, two population-based metaheuristic approaches to DMCHBAP are proposed: combined Genetic Algorithm (cGA) and improvement-based Bee Colony Optimization (BCOi). Both cGA and BCOi are evaluated and compared against each other and against state-of-the-art solution methods for DMCHBAP on five sets of problem instances. The conducted computational experiments and statistical analysis indicate that population-based metaheuristic methods represent promising approaches for DMCHBAP and similar problems in maritime transportation.

中文翻译：

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动态最小成本混合泊位分配问题的基于种群的元启发式算法

本研究考虑了具有固定船舶处理时间的动态最低成本混合泊位分配问题 (DMCHBAP)。要最小化的目标函数由三个部分组成：所有船只的定位成本、等待成本和完成延迟。基于混合整数线性规划 (MILP) 的 DMCHBAP 数学公式被提出并在商业 CPLEX 12.3 求解器的框架内使用。由于找到高质量解决方案的速度对于集装箱码头高效可靠的决策支持系统至关重要，因此提出了两种基于人群的 DMCHBAP 元启发式方法：组合遗传算法（CGA）和基于改进的蜂群优化（BCOi）。两个都CGA 和 BCOi 在五组问题实例上相互评估和比较，并与 DMCHBAP 的最先进的解决方法进行比较。进行的计算实验和统计分析表明，基于群体的元启发式方法代表了 DMCHBAP 和海上运输中类似问题的有希望的方法。