Digital twin-based dynamic co-scheduling with AGV energy management in sea-rail intermodal automated container terminals

To fully leverage the advantages of sea-rail intermodal transport, the automation upgrade of the railway center station (RCS) is essential for enabling seamless connectivity between the RCS and the terminal via automated guided vehicles (AGVs). This transformation introduces complex scheduling challenges for sea-rail intermodal automated container terminals (SRIACTs), including multi-directional container flows, coordination among diverse equipment, and AGV charging requirements with battery management. To address these challenges, this paper investigates the multi-equipment collaborative scheduling problem in SRIACTs with consideration of AGV charging. A mixed-integer programming model is formulated with sequencing, timing, and energy constraints, aiming to jointly minimize makespan and total charging time. To improve computational efficiency in large-scale cases, an improved genetic algorithm based on a decomposition-iteration framework is developed according to problem-specific features. Furthermore, to address operational uncertainties, a digital twin-based hybrid rescheduling framework is extended to enable real-time monitoring, disturbance detection, and rapid response to AGV status and battery levels, thereby enhancing system resilience and scheduling flexibility. Extensive numerical experiments are conducted to validate the effectiveness of the proposed algorithm and rescheduling framework. On this basis, comparative analyses are performed on bi-objective formulations and the flexible charging strategy. Additionally, sensitivity analyses examine the impacts of key factors, including objective weights, charging thresholds, rescheduling thresholds, and the number and layout of charging facilities. The findings provide valuable insights for terminal operators in formulating integrated scheduling strategies, optimizing AGV charging plans, and scientifically deploying charging infrastructure during the RCS automation process, thereby promoting sustainable and intelligent terminal operations.