Research on the optimization of the scheduling of grain carrier fleets for dry bulk companies considering dynamic demand

This study proposes an optimization model for fleet scheduling to address the dynamic demand issues in grain transportation for dry bulk companies. It comprehensively considers multiple constraints such as fuel consumption, transportation time, and loading and unloading time windows, with the goal of maximizing company revenue. It is solved using a genetic algorithm. The results show that the optimal scheduling plan can achieve a revenue of 10.4 million US dollars under static demand, and the revenue increases to 13.95 million US dollars under dynamic demand. Sensitivity analysis shows that fuel prices, freight rate fluctuations and changes in cargo demand have a significant impact on the company's revenue. A 20% increase in fuel prices will lead to a decrease in revenue of about 30%, while a 20% increase in freight rates can increase revenue by about 23%, and an increase in demand by 20% will bring about a 15% increase in revenue. Therefore, companies should flexibly adjust sailing speed, shipping frequency and cargo loading strategies to respond to market changes and maximize revenue. This study provides theoretical basis and practical guidance for dry bulk companies to optimize fleet scheduling in a complex market environment.