Research on the Scheduling of Tractors in the Major Epidemic to Ensure Spring Ploughing

Abstract

When the outbreak of COVID-19 began, people could not go out. It was not allowed to provide agricultural machinery services in different places across regions to reduce the flow and gathering of people. Improvement of utilization efficiency of agricultural machinery resources is required through scientific scheduling of agricultural machinery. With seizing the farming season and stabilizing production as the goal, this paper studied the scientific scheduling of tractors within the scope of town and established agricultural machinery operation scheduling model with the minimization of total scheduling cost as the optimization objective. Factors such as farmland area, agricultural machinery, and farmland location information and operating time window are considered in this model to improve the accuracy of the agricultural machinery operation scheduling model. The characteristics of multiple scheduling algorithms are analyzed comprehensively. The scheduling requirements of agricultural machinery operation to ensure spring ploughing are combined to design the agricultural machinery scheduling algorithm based on the SA algorithm. With Hushu Street, Jiangning District, Nanjing City, as an example, a comparative experiment is conducted on the simulated annealing algorithm (SA) designed in this paper and the empirical algorithm and genetic algorithm (GA). The results suggest that the total cost of the scheduling scheme generated by the SA algorithm is 19,042.07 yuan lower than that by the empirical scheduling algorithm and 779.19 yuan lower than that by the genetic algorithm on average. Compared with the GA algorithm, the transfer distance, waiting cost, and delay cost of the SA algorithm are reduced by 11.6%, 100%, and 98.1% on average, indicating that the transfer distance of agricultural machinery in the scheduling scheme generated by the SA algorithm is shorter, so is the waiting and delay time. Meanwhile, it can effectively obtain the near-optimal solution that meets the time window constraint, with good convergence, stability, and adaptability.

Task Allocation of Multi-Machine Collaborative Operation for Agricultural Machinery Based on the Improved Fireworks Algorithm

“…Currently, multi-machine collaboration of agricultural machinery is one of the main research topics in the field of agricultural machinery and, thus, has attracted much attention internationally. Reasonably arranging the cooperative operation of multiple agricultural machinery or multiple types of agricultural machinery, that is, realizing a multi-machine cooperative operation of agricultural machinery, can be an important way to reduce the cost and increase the efficiency of agricultural machinery [1][2][3].…”

A Cooperative Scheduling Based on Deep Reinforcement Learning for Multi-Agricultural Machines in Emergencies

“…With the agricultural machinery scheduling, researchers usually model it as a combinatorial optimization problem (COP) [1][2][3][4][5][6][7][8][9][10][11][12]. In particular, Huang et al [1] modeled an agricultural machinery scheduling problem as a multi-depot vehicle routing problem with time windows (MDVRPTW) problem and proposed a hybrid particle swarm optimization (PSO) algorithm for solving it.…”