Multi-Objective Optimization Method for Matching Between Bulk Cargo Order and Ship Based on Improved NSGA-II Algorithm

To increase the loading rate of bulk carriers and reduce the cost of loading and transportation, it is necessary to rationally match bulk orders with vessel resources. Decision-making on order-ship matching is difficult due to the need to consider issues such as order splitting, bulk cargo variety switching, and liner routes. This paper aims to optimise the ship loading rate, loading variety switching cost, and transportation cost by constructing a three-objective order-ship matching optimisation model. Addressing the problems of poor solution set diversity and search ability in the traditional NSGA-II algorithm for large-scale problems, this paper proposes using the First Fit algorithm as an initialisation method to reduce the solution space. Additionally, an adaptive greedy evolution operator is designed to improve the searchability of the NSGA-II algorithm. Finally, an aggregate producer is used as an example to verify the feasibility of the matching algorithm. Experimental results show that the algorithm achieves an average ship loading rate of over 93% for the matching scheme and reduces costs in solving the ship waybill scheme when the problem size is large.