Energy-Efficient Collaborative Scheduling of Dual-Trolley Quay Cranes and Automated Guided Vehicles in Automated Container Terminals

This paper investigates the energy-efficient collaborative scheduling of dual-trolley quay cranes (DTQCs) and automated guided vehicles (AGVs) in automated container terminals (ACTs). Considering operational constraints such as mixed bidirectional flows, limited buffers, precedence constraints, and deadlocks, this complex logistical system is formally characterized as a blocking hybrid flow shop scheduling problem (BHFSSP-BFLB). To systematically minimize the total energy consumption, a mathematical framework grounded in a mixed-integer programming model is developed. To solve the model efficiently, an improved genetic algorithm (IGA) is proposed featuring a two-layer encoding approach to respect precedence and mitigate deadlocks. Furthermore, an active scheduling strategy based on machine idle time insertion is incorporated during decoding to shorten the makespan without increasing energy consumption. Numerical experiments demonstrate that the IGA can significantly decrease the makespan while reducing total energy consumption: compared with a standard genetic algorithm (GA) without active scheduling, the proposed IGA reduces the makespan by 32.35% on average. In addition, the makespan under energy minimization is within 1.5% of that under makespan minimization, indicating that energy optimization yields an almost minimal makespan. Sensitivity analysis further evaluates the effects of DTQC-AGV configurations and buffer capacities, offering practical insights for decision-makers.