Optimizing Multi-Quay Combined Berth and Quay Crane Allocation Using Computational Intelligence

The significant increase in international seaborne trade volumes over the last several years is pushing port operators to improve the efficiency of terminal processes and reduce vessel turnaround time. Toward this direction, this study investigates and solves the combined berth allocation problem (BAP) and quay crane allocation problem (QCAP) in a multi-quay (MQ) setting using computational intelligence (CI) approaches. First, the study develops a mathematical model representing a real port environment and then adapts the cuckoo search algorithm (CSA) for the first time in this setup. The CSA is inspired by nature by following the basic rules of breeding parasitism of some cuckoo species that lay eggs in other birds' nests. For comparison purposes, we implement two baseline approaches, first come first serve and exact MILP, and two CI approaches, particle swarm optimization (PSO) and genetic algorithm (GA), that are typically used to solve such complex or NP-hard problems. Performance assessment is carried out via a comprehensive series of experiments using real-world data. Experimental findings show that the MILP method can address the problems only when a small dataset is employed. In contrast, the newly adapted CSA can solve larger instances of MQ BAP and QCAP within significantly reduced computation times.