Optimization of load reduction and berth shifting operations in green ports: a collaborative scheduling model for berths, unloaders, and tugboats

With the increasing trend of large-scale vessels, the shortage of deep-water berth resources in ports has become increasingly prominent, severely restricting vessel turnover rates and cargo unloading efficiency. To address this bottleneck, this study proposes an optimization approach for load reduction and berth shifting of large dry bulk carriers, coordinating the allocation of core resources such as berths, unloaders, and tugboats to enhance the overall operational efficiency of ports. A multi-objective, two-stage joint scheduling optimization model for berth, unloader, and tugboat operations is developed. The model decouples the problem into two stages: berth-unloader joint allocation and tugboat scheduling. It comprehensively considers operation time and costs to achieve full-process optimization of load reduction and berth shifting. Furthermore, a two-layer solution framework integrating the Starfish Optimization Algorithm (SFOA) with the Non-dominated Sorting Genetic Algorithm (NSGA-II) is proposed. The framework employs tabu search to enhance Pareto front exploration in berth-unloader allocation and utilizes an elite-based chromosome generation mechanism in tugboat scheduling to improve solution quality. Additionally, a multi-energy hybrid tugboat fleet comprising diesel and clean energy-powered vessels is designed, along with a vessel-tugboat matching strategy that factors in emission reduction requirements based on varying tugboat demands of different bulk carrier sizes. Finally, a case study based on a real port in northern China demonstrates the effectiveness of the proposed optimization scheme in alleviating deep-water berth shortages, reducing tugboat emissions, and promoting the intelligent and green transformation of port operations.