This paper presents a chaotic optimal thermodynamic evolutionary algorithm (COTEA) designed to address the integrated scheduling problems of berth allocation, ship unloader scheduling, and yard allocation at bulk cargo terminals. Our proposed COTEA introduces a thermal transition crossover method that effectively circumvents local optima in the scheduling solution process. Additionally, the method innovatively combines a good point set with chaotic dynamics within an integrated initialization framework, thereby cultivating a robust and exploratory initial population for the optimization algorithm. To further enhance the selection process, our paper proposes a refined parental selection protocol that employs a quantified hypervolume contribution metric to discern superior candidate solutions. Postevolution, our algorithm employs a Cauchy inverse cumulative distribution-based neighborhood search to effectively explore and enhance the solution spaces, significantly accelerating the convergence speed during the scheduling solution process. The proposed method is adept at achieving multiobjective optimization, simultaneously improving the service level and reducing costs for bulk cargo terminals, which in turn boosts their competitiveness. The effectiveness of our COTEA is demonstrated through extensive numerical simulations.

本文提出了一种混沌最优热力学进化算法（COTEA），旨在解决散货码头泊位分配、卸船机调度和堆场分配的综合调度问题。我们提出的 COTEA 引入了一种热转换交叉方法，可以有效地规避调度求解过程中的局部最优。此外，该方法创新地将良好的点集与混沌动力学结合在集成的初始化框架内，从而为优化算法培养了稳健且具有探索性的初始种群。为了进一步增强选择过程，我们的论文提出了一种改进的父母选择协议，该协议采用量化的超体积贡献指标来辨别优秀的候选解决方案。进化后，我们的算法采用基于柯西逆累积分布的邻域搜索来有效地探索和增强解空间，显着加快了调度求解过程中的收敛速度。该方法善于实现多目标优化，同时提高散货码头的服务水平并降低成本，从而提高散货码头的竞争力。我们的 COTEA 的有效性通过广泛的数值模拟得到了证明。