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.
Highlights
China’s agriculture has a small average household operation scale, high agricultural multiple cropping index, tight farming time, and other problems. e operation capacity of agricultural machinery cannot meet production needs during busy periods. e regional shortage of agricultural machinery can be solved by cross-regional operation of agricultural machinery in regular years. e cross-regional operation area of agricultural machinery for three major food crops in China reached 20,478 thousand hectares, accounting for 21% of the sown area in 2019 [1].Due to the outbreak of COVID-19, global production and life in 2020 paused; companies suspended production and businesses shut down for a while
With seizing the farming season and stabilizing production as the goal and the scheduling of tractors within the scope of town as the research object, influencing factors such as farmland area, agricultural machinery, farmland location information, and operating time window time were fully considered based on in-depth analysis of various agricultural machinery operation costs. e model solving method was designed through the improved simulated annealing algorithm (SA) algorithm. e tractor production scheme and scheduling path within the town are obtained to meet the requirements of the farmland operating time window, achieve the objective of total cost minimization, and accomplish the task of ensuring spring ploughing with high efficiency and cost-effectiveness
To further illustrate the superiority of the algorithm designed in this paper, the genetic algorithm (GA) algorithm commonly used in the research of similar agricultural machinery scheduling issues is applied to comparison experiments
Summary
China’s agriculture has a small average household operation scale, high agricultural multiple cropping index, tight farming time, and other problems. e operation capacity of agricultural machinery cannot meet production needs during busy periods. e regional shortage of agricultural machinery can be solved by cross-regional operation of agricultural machinery in regular years. e cross-regional operation area of agricultural machinery for three major food crops in China reached 20,478 thousand hectares, accounting for 21% of the sown area in 2019 [1]. In large-scale emergencies, the problems and challenges that arise in the management of emergency resource supply chains are significantly different from those in ordinary commercial applications [2,3,4,5] To respond to such disasters effectively, governments and management organizations must consider multiple and unique aspects of emergency operations, such as resource scarcity and disaster uncertainty [6,7,8]. Given that farmers could not organize agricultural production effectively at home in areas with COVID-19 outbreaks, an agricultural machinery operation scheduling model with the minimization of total scheduling cost as the optimization objective was established in this paper. With seizing the farming season and stabilizing production as the goal and the scheduling of tractors within the scope of town as the research object, influencing factors such as farmland area, agricultural machinery, farmland location information, and operating time window time were fully considered based on in-depth analysis of various agricultural machinery operation costs. e model solving method was designed through the improved SA algorithm. e tractor production scheme and scheduling path within the town are obtained to meet the requirements of the farmland operating time window, achieve the objective of total cost minimization, and accomplish the task of ensuring spring ploughing with high efficiency and cost-effectiveness
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