An integrated solution for collaborative scheduling of heterogeneous agricultural machines of different types in harvesting-transportation scenarios

Abstract

Efficient coordination of machinery fleets in regional farmland operations remains a significant challenge due to the lack of scientifically grounded scheduling management strategies, high modeling complexity, and elevated operational costs. This study proposed an integrated solution for collaborative scheduling of heterogeneous agricultural machines of different types, aiming to address the collaborative scheduling of harvesters and grain trucks in harvest-transport scenarios. Firstly, an electronic farm map was constructed to facilitate path planning and generate unloading points within plots. The study then developed a collaborative scheduling model involving multiple machines, which incorporated heterogeneous parameters such as harvester harvesting speeds and grain truck hopper capacities. The model aims to minimize the total operational time of the machinery fleet. The scheduling problem was addressed by introducing a hybrid greedy heuristic-based improved genetic algorithm. Simulation and experimental validation were conducted using the electronic map of the Shanghai Qingpu unmanned farm. The results demonstrated that the proposed algorithm outperforms three algorithms in optimizing total operational time. For example, when the number of tasks is 20, the average total operational time is reduced by 32.4 min, an improvement of approximately 11.45% compared to the standard genetic algorithm. Additionally, parameter comparison experiments validate the algorithm’s compatibility with heterogeneous parameter settings, thereby substantiating its efficacy in addressing task allocation problems for heterogeneous machinery. The effectiveness of the proposed method in facilitating efficient collaboration among heterogeneous agricultural machines of different types is demonstrated through a case study on collaborative scheduling in harvest-transport scenarios. The findings validate the feasibility and applicability of the proposed approach in effectively addressing real-world agricultural scheduling challenges.