Scheduling Optimization for Automated Container Terminals Using Discrete Artificial Protozoa Optimization

As container terminals advance toward intelligent automation, collaborative scheduling of quay cranes (QCs), automated guided vehicles (AGVs), and automated stacking cranes (ASCs) becomes increasingly complex due to task release times, resource exclusivity, limited transfer-buffer capacity, and battery charging constraints, which induce waiting and blocking and increase makespan. This paper develops a SimPy-based discrete-event simulation model for the QC–AGV–ASC system and formulates a makespan minimization problem under fixed charging rules. Candidate solutions comprise a task sequence and offline assignments for yard-side and transportation resources, which are decoded into executable plans via simulation. Multiple metaheuristics are implemented on a unified evaluation interface and compared across different task scales and resource configurations.