基于遗传变邻域搜索算法的农机跨区调度优化研究  

Agricultural Machinery Cross-region Scheduling Optimization Based on Genetic Algorithm Variable Neighborhood Search

作　　者：曹光乔 马斌 陈聪 任保鑫 胡朝中 CAO Guangqiao;MA Bin;CHEN Cong;REN Baoxin;HU Chaozhong(Nanjing Institute of Agricultural Mechanization,Ministry of Agriculture and Rural Affairs,Nanjing 210014,China;Graduate School of Chinese Academy of Agricultural Sciences,Beijing 100081,China)

机构地区：[1]农业农村部南京农业机械化研究所,南京210014 [2]中国农业科学院研究生院,北京100081

出　　处：《农业机械学报》2023年第10期114-123,共10页Transactions of the Chinese Society for Agricultural Machinery

基　　金：中国农业科学院科技创新工程项目(农科院办(2014)216号);中国农业科学院基本科研业务费专项(S202215)。

摘　　要：智慧农业的快速发展促使多区域互联农机的调度追求更高的实时性,为更合理配置农机资源,农机跨区作业已成为完成“三夏”机收任务的主要服务模式。基于小麦收获机跨区作业真实场景,研究了带时间窗的多库、多机型的农机跨区调度问题,同时考虑经济成本和环境成本,建立以最小调度成本为目标的跨区调度模型。根据问题特征,设计遗传变邻域搜索算法(Genetic algorithm variable neighborhood search,GAVNS),该方法通过交叉、随机扰动、自适应邻域选择等操作,使解的搜索更加高效和灵活。对我国黄淮海平原72个小麦生产区县的作业需求进行计算与分析:不同算法相比,本文设计的算法得到最优解的迭代次数更低、收敛速度更快,求得的目标函数值较遗传算法、变邻域搜索算法分别降低16.41%、11.15%;对比不同调度模式,开放路径模式更有利于提升跨区调度服务效率,较闭合路径模式,调度成本降低17.76%。In recent years,the rapid advancement of smart agriculture has spurred the pursuit of higher real-time scheduling for inter-connected agricultural machinery across multiple regions. This approach aims to achieve more reasonable allocation of agricultural machinery resources. Cross-regional agricultural machinery operations have emerged as the principal service mode for completing the tasks of the “three summer” harvest. Drawing from real-world scenarios of cross-regional wheat harvesting machinery operations,the cross-regional scheduling problem involving multiple depots and machinery types was investigated,incorporating time windows. Economic and environmental costs were simultaneously considered,leading to the establishment of a cross-regional scheduling model with the objective of minimizing scheduling costs. Tailored to the characteristics of the problem,a genetic algorithm variable neighborhood search (GAVNS) was designed. This algorithm enhanced efficiency and flexibility in solution search through operations like crossover,random perturbations,and adaptive neighborhood selection. The operational demands of 72 wheat-producing counties in the Huang-Huai-Hai Plain in China were computed and analyzed. Comparative analysis revealed that the proposed algorithm outperformed alternative algorithms in terms of reduced iteration count to reach the optimal solution and faster convergence speed,with 16.41% decrease compared with the genetic algorithm and 11.15% decrease compared with the variable neighborhood search algorithm in terms of the objective function value. Furthermore,different scheduling modes were compared,highlighting the open path mode as more conducive to enhancing cross regional scheduling service efficiency,leading to 17.76% reduction in scheduling costs compared with the closed path mode.

关 键 词：农业机械 时间窗 跨区调度 遗传变邻域搜索算法 

分 类 号：S115[农业科学—农业基础科学] S231