不确定环境下用于泊位分配和岸桥调度的鲁棒元启发式算法：综述

中文摘要 AI 辅助

泊位分配与岸桥调度问题（BACAP）是海事运输和货运物流中典型的港口码头调度问题，涉及船舶到港、泊位位置、服务时长及岸桥可用性的紧密耦合。在到港偏差、装卸时长波动、资源中断等不确定因素下，基于名义假设优化的调度方案在执行时可能变得脆弱，因此需研究港口码头运营中BACAP的鲁棒元启发式优化。尽管基于种群的元启发式算法已广泛应用于BACAP及相关港口调度问题，但现有研究在不确定性表征、鲁棒性准则、搜索机制和实证评估方案方面仍较为零散。据所知，本文是首篇专门针对不确定环境下BACAP的鲁棒种群元启发式算法的综述。首先总结BACAP中的不确定性来源与信息表征，再从机制导向视角梳理现有方法，涵盖解表示与解码、鲁棒性评估与选择、鲁棒性引导的搜索动态，以及可行性保持与恢复。此外，本文提出用于不确定BACAP的基准套件以支持受控实证比较，并结合代表性元启发式算法与不同鲁棒性策略报告说明性基准结果。最后，本文指出与基准扩展、鲁棒性感知搜索设计、时间自适应鲁棒性及非平稳不确定性相关的开放挑战。

英文摘要

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. 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. Although population-based metaheuristics have been widely used for BACAP and related port-scheduling problems, existing studies remain fragmented in their uncertainty repre?sentations, robustness criteria, search mechanisms, and empir?ical evaluation protocols. To the best of our knowledge, this paper provides the first focused review dedicated to robust population-based metaheuristics for BACAP under uncertainty. We first summarize uncertainty sources and information repre?sentations 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. 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. Finally, we identify open chal?lenges related to benchmark extension, robustness-aware search design, time-adaptive robustness, and non-stationary uncertainty.

发表机构

• School of Artificial Intelligence, Nanjing University of Information Science and Technology（南京信息工程大学人工智能学院）
• Institute of Cyberspace Security, School of Computer Science and Technology, Harbin Institute of Technology（哈尔滨工业大学计算机科学与技术学院网络空间安全学院）

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