A hybrid metaheuristic algorithm for collaborative scheduling optimization of port coal transportation

Port coal transportation scheduling faces significant challenges with slow response times and low efficiency, especially under dynamic disruptions. To address these issues, this research proposes a novel hybrid meta-heuristic algorithm that deeply integrates the improved Ant Colony Optimization (IACO) and the Greedy Randomized Adaptive Search Procedure (GRASP). The proposed IACO-GRASP algorithm is designed to enhance collaborative scheduling by effectively coordinating global exploration and local optimization. The major findings from a case study at Qinhuangdao Port demonstrate that the algorithm increases ship turnover efficiency by 36.25% and reduces the empty running rate by 25.18%, leading to an estimated annual cost saving of 18.95 million yuan. Comparative experiments also show that the algorithm’s convergence speed is significantly improved over other hybrid methods. These findings highlight the potential of IACO-GRASP as a robust and efficient decision-support tool for building responsive, resource-optimized, and intelligent port operations.