A Novel Hybrid PSO-GWO Algorithm for Optimizing the Unloading Berth Allocation with Berth Shifting for Dry Bulk Ports

Berth shifting for vessel unloading is widely adopted in large dry bulk ports, yet relevant berth scheduling optimization remains under-researched. This paper proposes a Hybrid Particle Swarm Optimization and Grey Wolf Optimization (HPSGWO) algorithm for an Unloading Berth Allocation Problem with Berth Shifting (UBAPBS) with the objective of minimizing the total service cost. To mitigate premature convergence and update stagnation during the iterative process, we develop HPSGWO by fusing the local exploitation capability of Particle Swarm Optimization (PSO) with the global exploration strength of Grey Wolf Optimization (GWO). We further design a Dynamic adaptive scheduling Insertion (DI) strategy and a Stagnation monitoring and diversity Restart (SR) mechanism to improve the stability and solution quality of the proposed algorithm, forming the HPSGWO with DI and SR (HPSGWO-DISR). Numerical experiments based on real-world data from a major dry bulk port in Northern China first show the effect of key hyper parameters on the performance of the proposed algorithm. They also demonstrate that HPSGWO significantly outperforms well-known metaheuristic algorithms such as PSO and GWO in terms of solution quality and algorithm stability and preserves comparable computational time cost. Moreover, the proposed DI and SR mechanisms can collectively further improve the performance of HPSGWO.