Integrated optimization of dynamic deployment and scheduling for rail-mounted gantry cranes in sea-rail intermodal port with on-dock rails

• We introduce a RMGC profile that adapts to dynamic handling demand in railway operation area. • An integrated model for RMGC deployment, space partitioning, and task assignment and sequencing is presented. • A LBBD algorithm framework with specialized A* algorithm and heuristic strategies solves the problem effectively. • Our analysis assesses time partitioning, buffer settings, and train operational parameters ' effect on RMGC profile. The railway operation area at the sea-rail intermodal port provides the necessary infrastructure for the seamless connection of railway and water transportation, thereby attracting increased railway container flows to the port. Rail-mounted gantry cranes (RMGCs), as the primary handling equipment in this area, play a pivotal role in facilitating swift transshipment of sea-rail intermodal containers. To address the fluctuating workload caused by train arrivals and departures, as well as the time constraints associated with loading and unloading operations, we develop an integrated optimization model for the dynamic deployment and scheduling of RMGCs. This model generates an optimal RMGC profile by effectively allocating crane capacity across both temporal and spatial dimensions. Decision-making regarding dynamic space partitioning, task assignment, and task sequencing is achieved hierarchically through a logic-based Benders decomposition framework, incorporating A* and heuristic strategies for single crane scheduling. Through case analysis, we discuss the performance of various algorithmic variants and the impact of time partitioning on algorithm efficiency. Additionally, we analyze the advantages of employing a flexible number of RMGCs and dynamic task partitioning strategies. Finally, we examine the effects of buffer time settings and train operational characteristics on the RMGC profile. The results indicate that the proposed dynamic deployment and scheduling approach for RMGCs can adapt to variations in workload and different train characteristics, leading to an optimized RMGC profile that minimizes unnecessary equipment mobilization while ensuring timely task completion.