Liu Huanyu, Zhang Lihan, Zhao Baidong, Tang Jiacheng, Luo Jiahao, Wang Shuang
Institute of Modern Agricultural Equipment, Xihua University, Chengdu, Sichuan, China.
School of Mechanical Engineering and Automation, Dalian Polytechnic University, Dalian, Liaoning, China.
Front Plant Sci. 2024 Jun 21;15:1413595. doi: 10.3389/fpls.2024.1413595. eCollection 2024.
In response to the issue of harvesting machine failures affecting crop harvesting timing, this study develops an emergency scheduling model and proposes a hybrid optimization algorithm that combines a genetic algorithm and an ant colony algorithm. By enhancing the genetic algorithm's crossover and mutation methods and incorporating the ant colony algorithm, the proposed algorithm can prevent local optima, thus minimizing disruptions to the overall scheduling plan. Field data from Deyang, Sichuan Province, were utilized, and simulations on various harvesting machines experiencing random faults were conducted. Results indicated that the improved genetic algorithm reduced the optimal comprehensive scheduling cost during random fault occurrences by 47.49%, 19.60%, and 32.45% compared to the basic genetic algorithm and by 34.70%, 14.80%, and 24.40% compared to the ant colony algorithm. The improved algorithm showcases robust global optimization capabilities, high stability, and rapid convergence, offering effective emergency scheduling solutions in case of harvesting machine failures. Furthermore, a visual management system for agricultural machinery scheduling was developed to provide software support for optimizing agricultural machinery scheduling.
针对收割机故障影响作物收获时机的问题，本研究建立了应急调度模型，并提出了一种将遗传算法和蚁群算法相结合的混合优化算法。通过改进遗传算法的交叉和变异方法并融入蚁群算法，该算法能够防止局部最优，从而将对整体调度计划的干扰降至最低。利用了四川省德阳市的田间数据，并对各种出现随机故障的收割机进行了模拟。结果表明，与基本遗传算法相比，改进后的遗传算法在随机故障发生时将最优综合调度成本降低了47.49%、19.60%和32.45%，与蚁群算法相比降低了34.70%、14.80%和24.40%。改进后的算法展现出强大的全局优化能力、高稳定性和快速收敛性，在收割机出现故障时提供了有效的应急调度解决方案。此外，还开发了农机调度可视化管理系统，为优化农机调度提供软件支持。
