This paper investigates the robust berth allocation problem in container terminals. To handle the uncertainties in vessel arrivals, the problem is formulated as a scenario-based two-stage stochastic programming model. Furthermore, we introduce the time buffers to the model. We then develop an algorithm for time buffer insertion, which accommodates the adaptive search procedure for the time buffer into the Particle Swarm Optimization (PSO) algorithm. Different from the traditional PSO algorithm, a core operator is designed with a modified version to take the intelligent time buffer insertion approach. The results of the numerical experiments show that the proposed method consistently provides an improved performance in terms of solution quality, compared with the previous studies and the one with a traditional operator in the PSO algorithm.

本文研究了集装箱码头的稳健泊位分配问题。为了处理船舶到港的不确定性，该问题被表述为基于场景的两阶段随机规划模型。此外，我们将时间缓冲区引入模型。然后，我们开发了一种用于时间缓冲区插入的算法，该算法将时间缓冲区的自适应搜索过程纳入粒子群优化 (PSO) 算法。不同于传统的PSO算法，核心算子设计了一个修改版本，采用智能时间缓冲区插入的方式。数值实验结果表明，与先前的研究和 PSO 算法中使用传统算子的方法相比，所提出的方法在解决方案质量方面始终提供改进的性能。