Robust Metaheuristics under Uncertainty for Berth Allocation and Quay Crane Assignment: A Review

面向不确定性泊位分配与岸桥调度问题的鲁棒元启发式算法综述

Abstract: The berth allocation and quay crane assignment problem (BACAP) is a representative port-terminal scheduling problem in maritime transportation and freight logistics, where vessel arrivals, berth positions, service durations, and quay-crane availability are tightly coupled.

摘要： 泊位分配与岸桥调度问题（BACAP）是海运和货运物流中具有代表性的港口码头调度问题，其中船舶到达、泊位位置、服务时长以及岸桥可用性等因素紧密耦合。

Under uncertainties such as arrival deviations, handling-time fluctuations, and resource disruptions, schedules optimized under nominal assumptions may become fragile during execution, motivating the study of robust metaheuristic optimization for BACAP in port-terminal operations.

在面对到达偏差、装卸时间波动和资源中断等不确定性时，基于名义假设优化出的调度方案在执行过程中可能变得脆弱，这促使了针对港口码头运营中 BACAP 鲁棒元启发式优化的研究。

Although population-based metaheuristics have been widely used for BACAP and related port-scheduling problems, existing studies remain fragmented in their uncertainty representations, robustness criteria, search mechanisms, and empirical evaluation protocols.

尽管基于种群的元启发式算法已被广泛应用于 BACAP 及相关港口调度问题，但现有研究在不确定性表示、鲁棒性准则、搜索机制以及实证评估协议方面仍处于碎片化状态。

To the best of our knowledge, this paper provides the first focused review dedicated to robust population-based metaheuristics for BACAP under uncertainty.

据我们所知，本文是首篇专门针对不确定环境下 BACAP 鲁棒种群元启发式算法的专题综述。

We first summarize uncertainty sources and information representations in BACAP, and then organize existing methods from a mechanism-oriented perspective, covering solution representation and decoding, robust evaluation and selection, robustness-guided search dynamics, and feasibility preservation and recovery.

我们首先总结了 BACAP 中的不确定性来源及信息表示方式，随后从机制导向的角度梳理了现有方法，涵盖了方案表示与解码、鲁棒评估与选择、鲁棒性引导的搜索动态，以及可行性保持与恢复等内容。

We further present a benchmark suite for uncertain BACAP to support controlled empirical comparison and report illustrative baseline results by combining representative metaheuristics with different robustness strategies.

此外，我们还提出了一个针对不确定 BACAP 的基准测试集，以支持受控的实证比较，并通过结合代表性元启发式算法与不同的鲁棒性策略，报告了说明性的基准测试结果。

Finally, we identify open challenges related to benchmark extension, robustness-aware search design, time-adaptive robustness, and non-stationary uncertainty.

最后，我们指出了与基准扩展、鲁棒感知搜索设计、时间自适应鲁棒性以及非平稳不确定性相关的开放性挑战。