关键词: 农机, 时间窗优先级策略, 多目标优化, 路径规划

Abstract: Due to the lack of scientific and reasonable optimization and scheduling strategies, multi‑machine cross‑regional operations during the agricultural harvest season often result in high operational costs, low operation efficiency and the inability to complete field tasks within the optimal time frame. In order to address this issue, a multi‑machine and multi‑objective cross‑regional collaborative operation scheduling model was constructed, targeting the shortest transfer distance for agricultural machinery and the lowest total scheduling cost under the constraint of operation time windows. A multi‑objective adaptive priority‑based optimization scheduling algorithm (APRMOGA) was designed. The genes were encoded by  dual‑layer encoding method, the combine harvesters were assigned successively for operation according to time window priority rules to generate the initial population. A time window priority sequence crossover method based on dual‑layer encoding was designed to preferentially retain genes with earlier start times. This is combined with adaptive mutation probability and elitism strategies to select and transform individuals, and obtain the globally optimal Pareto solution set. An experiment was conducted by using 24 fields in a certain region of Hebei Province for verification. The results indicated that the operational efficiency of the APRMOGA algorithm was higher than that of the NSGA-II algorithm. The transfer distance and total scheduling cost of combine harvesters calculated by the APRMOGA algorithm were reduced by 23.60% and 13.72%, respectively, compared to the NSGA-II algorithm.

Key words: agricultural machinery, time window prioritization strategy, multi?objective optimization, path planning