Collaborative Optimization of Multi-Equipment Scheduling and Intersection Point Allocation for U-Shaped Automated Sea-Rail Intermodal Container Terminals

Guided by green and low-carbon policies, the construction of intelligent container terminals is imperative. Based on the emerging U-shaped automated sea-rail intermodal container terminal (SRICT), this paper focuses on the collaborative optimization of multi-equipment scheduling and intersection point allocation considering direct and indirect transshipment modes. With the objectives of maximizing the balanced workload of bilateral-cantilever automated yard cranes (AYCs) and minimizing the invalid operation cost of the yard, a bi-objective programming model is formulated to determine the handling area for bilateral-cantilever AYCs, intersection points for direct transshipment containers, and scheduling schemes for bilateral-cantilever AYCs, automated guided vehicles (AGVs), and internal trucks (ITs). We employ Q-learning (QL) and cumulative prospect theory (CPT) based on multi-objective particle swarm optimization (MOPSO) to solve the model by guiding the search and adaptively adjusting parameters. By comparing evaluation indicators, we have verified that our algorithm is superior to the benchmark algorithms. We conducted comparative experiments on various repair operators and optimization strategies. Results indicate that the proposed model and algorithm can balance the workload of AYCs, reduce energy consumption, and improve handling efficiency. Note to Practitioners —This paper addresses the transshipment connection between ships, the yard, and trains. The U-shaped automated SRICT adopted a side-operation layout with more interaction points. Furthermore, due to constraints related to laying cables and the environment, automation technology has not fully covered all functional areas at the SRICT. Therefore, the new layout and semi-automation present a challenge for transferring sea-rail intermodal containers. This paper proposes a tailored model and algorithm for emerging automated SRICTs with bilateral-cantilever AYCs. The collaborative optimization of multi-equipment scheduling (AYCs, AGVs, and ITs) and intersection point allocation is studied for the first time.