关键词: 物流工程, 双循环调度, 两阶段随机规划, 自动化集装箱码头, 条件风险价值

Abstract: Existing U-shaped dual-cycle scheduling models typically assume deterministic operation times. However, variations in vessel arrival times and equipment service durations may cause delays in export-container retrieval, yard transportation, and vessel or truck loading operations, which tend to accumulate along the operational chain and are difficult to capture during scheduling. To mitigate the risk of repeated postponements in container retrieval, yard transfer, and loading completion under time-varying operating conditions, this paper proposes a two-stage stochastic scheduling framework. In the first stage, task sequences and equipment resource allocations are jointly determined; in the second stage, operation completion times are evaluated under different arrival and service scenarios. Conditional Value-at-Risk (CVaR) is incorporated to characterize adverse operational outcomes with significant impacts on vessel-and truck-loading schedules. Computational experiments demonstrate that the proposed approach consistently produces stable solutions across different problem scales. Compared with the first-come-first served policy and a priori robust scheduling strategy, it achieves superior performance in terms of completion-time distributions, system operating conditions under severe disruptions, and delay propagation control. The average number of cascading propagation layers is reduced from 3.42 to 1.27, and the proportion of disruption-affected operations decreases from 38.6% to 11.8%. By accounting for completion-time risk during task sequencing and resource allocation, the proposed approach mitigates the accumulation of waiting times across interconnected equipment operations and limits the propagation of delays to downstream activities.

Key words: logistics engineering, dual-cycle scheduling, two-stage stochastic programming, automated container terminal; conditional value-at-risk (CVaR)