Research on emergency allocation of agricultural machinery for cross-regions operation in large-scale farmland

In the busy farming season, the most urgent work is to rush to harvest grain. Due to the lack of scientific and reasonable emergency operation scheduling plan of agricultural machinery, the agricultural machinery management department cannot guide the agricultural machinery to complete the emergency operation task of large-scale farmland. For the grain rush harvest of large-scale farmland, due to the restriction of the rush harvest constraint time, it may not be able to complete all the grain harvest tasks within the specified time, and then the grain loss value will appear. In this paper, a trans-regional emergency allocation model of agricultural machinery with the goal of minimizing the grain loss value is constructed, and an agricultural machinery emergency allocation algorithm based on two stages（TSEA） is proposed. Firstly, the large-scale farmland is divided according to the distance-based division strategy, and then the improved genetic algorithm is used to carry out the emergency operation allocation of agricultural machinery for each farmland division. In order to verify the effectiveness of the algorithm in this paper, the TSEA algorithm is compared with GA and SA algorithms in terms of the grain loss value of the emergency task and the running time of the algorithm. The experimental results show that the emergency scheduling scheme obtained by the TSEA algorithm is superior to the other two algorithms. In order to verify the effectiveness of the zoning strategy in this paper, the TSEA algorithm and the non-zoning emergency allocation algorithm are used to compare the grain loss value, algorithm running time and other aspects to verify the effectiveness of the zoning strategy. The results of multiple groups of experiments show that the strategies and algorithms proposed in this paper are more effective for solving the problem of emergency allocation of agricultural machinery across-regions in large-scale farmland, and can provide solutions for agricultural machinery management departments.