Cooperative scheduling method for harvester and grain transport vehicle based on IMPA algorithm

To address the issues of low response efficiency, poor timeliness, and high scheduling costs in the collaborative operation of harvesting and transportation, a cooperative scheduling model was constructed in this paper with the objectives of minimizing both scheduling costs and time. The model aimed to solve the challenge of coordinating harvesters and grain transport vehicles of different capacities under time window constraints, thereby completing the harvesting and transportation tasks across multiple fields efficiently. First, to improve the response efficiency and timeliness of harvesters and grain transport vehicles in complex operating environments, a task allocation method based on priority strategy and a response scheme were designed to enhance the efficiency of cooperative operations. Secondly, an Improved Marine Predators Algorithm (IMPA) was proposed by incorporating a radial transformation matrix, a dynamic search mechanism, and an enhanced opposition-based learning strategy to strengthen the overall optimization capability of the scheduling scheme. Additionally, non-dominated sorting and crowding distance calculation were integrated into the algorithm to optimize the solution set, leading to a more optimal scheduling plan. Simulation results demonstrated that, compared to the Non-dominated Sorting Genetic Algorithms-II (NSGA-II) and the Multi-objective Harris Hawks Optimization (MOHHO) presented in the literature, the proposed model significantly reduced scheduling costs and time, with a reduction of 7% and 2.9% in scheduling costs and a reduction of 2.6% and 8.3% in scheduling time, respectively. The results indicated that IMPA not only reduced scheduling costs and time but also enhanced the response capability and operational efficiency of cooperative scheduling, offering significant practical value in solving the collaborative scheduling problem for multi-field harvesting and transportation.