Two-level Crane Scheduling with Hybrid Metaheuristics in Realistic Port Operations

Abstract

Sustainable and efficient container unloading is a critical operation in maritime logistics, as it directly affects port throughput and ship turnaround time. Since quay cranes are the primary equipment responsible for this task, their effective scheduling is essential to minimize delays and ensure operational stability. This paper presents a two-level optimization approach for quay crane scheduling that balances operational efficiency with ship stability. At the bay level, we compare a flexible crane assignment strategy with the port’s current strategy, which ensures ship stability but results in a long total unloading time. Using the LINGO solver and a Genetic Algorithm (GA), we achieve up to a 15% improvement. To leverage the port’s strategy while overcoming its inefficiency, we redefine the unloading unit at the tier level and structure the process into two sequential phases. A hybrid metaheuristic combining the Firefly Algorithm (FA) and Cuckoo Search (CS) is proposed and compared with the GA. The hybrid algorithm shows higher consistency and statistically comparable solution quality to the GA.