Agricultural machinery operations, as extensive and highly technical agricultural production activities, are characterized by heavy tasks, complex targets, poor environments, and strong time sensitivity. However, current agricultural machinery operations face problems such as poor scheduling, unreasonable resource allocation, and low operational efficiency. To improve the efficiency and service level of agricultural machinery operations, this study establishes a scheduling model of agricultural machinery under multivariable constraints. The model incorporates factor such as transportation time, optimal path, total costs, waiting time penalties, and tardiness penalties, according to the practical scheduling characteristics of agricultural machinery operations. By introducing these various constraints, the effectiveness and feasibility of the model and algorithms are fully verified through relevant case studies. The result show that task sequence optimization based on a genetic algorithm effectively reduces scheduling costs, optimizes the path, and improves the operational efficiency and the quality of agricultural machinery social services. In addition, the running time of the algorithm is less than 1 s, which ensures that the agricultural machinery scheduling reaches the optimal state under various complex situations and meets the real-time and time-sensitive operational requirements.

农机作业作为一种涉及广泛、技术性强的农业生产活动，具有任务重、对象复杂、环境差和时效性强等特点。然而，当前农机作业面临调度水平差、资源配置不合理以及作业效率低等问题，为提高农机作业的效率和服务水平，在综合考虑运输时间、最优路径、综合成本、等待时间惩罚以及迟到时间惩罚的基础上，结合农机作业实际调度特点，建立多变量因子约束下的农机调度模型。通过引入运输时间、最优路径、综合成本、等待时间惩罚、迟到时间惩罚等多个约束条件，经相关实际算例验证该模型及算法的有效性和可行性。结果表明，基于遗传算法进行任务序列优化，可以有效降低调度成本，优化最优路径，提高农机作业效率和农机社会化服务水平。同时，算法的运行时间小于1 s，确保在各种复杂情况下，农机调度能够达到最优状态，满足农机作业的实时性和时效性要求。