基于蚁群算法的物流车辆监控及调度系统设计  

Design of Logistics Vehicle Monitoring and Scheduling System Based on Ant Colony Algorithm

作　　者：李琳[1] 江晋[2] LI Lin;JIANG Jin(Xi’an University of Posts&Telecommunications,Xi’an 710121,China;Xi’an Polytechnic University,Xi’an 710048,China)

机构地区：[1]西安邮电大学,西安710121 [2]西安工程大学,西安710048

出　　处：《自动化与仪器仪表》2023年第10期85-89,共5页Automation & Instrumentation

基　　金：陕西省十三五教育规划项目《基于Wiki的跨文化交际复合型涉外商科人才培养模式研究》(SGH18H089)。

摘　　要：物流车辆的监控以及调度关系着企业的成本与利润,因此,需要对企业的物流配送方法进行合理规划。研究中采用了蚁群算法进行路径的规划,实验中对信息素的增量函数进行了优化,同时利用爬山操作来优化蚁群算法。结果显示当信息素挥发因子ρ=1时,改进后的爬山蚁群算法在迭代至16次时开始大幅收敛;当迭代次数为45时,接近最优解;该算法比基础蚁群算法的精度提升了14.23%。在B公司的案例分析中,当客户数量为100时,爬山蚁群算法的使用车辆数为22,成本为5798.6元,客户满意度为96.8%。实验证明改进的蚁群算法能够有效用于物流车辆的监控及调度中。The monitoring and scheduling of logistics vehicles is related to the cost and profit of an enterprise, so it is necessary to reasonably plan the logistics distribution methods of the enterprise. In this study, an ant colony algorithm was used for path planning. In the experiment, the incremental function of pheromones was optimized, and mountain climbing operations were used to optimize the ant colony algorithm. The results show that when the pheromone volatility factor is A, the improved mountain climbing ant colony algorithm starts to converge significantly when it iterates to 16 times. When the number of iterations is 45, it is close to the optimal solution. The accuracy of this algorithm is 14.23% higher than that of the basic ant colony algorithm. In the case study of Company B, when the number of customers is 100, the number of vehicles used by the climbing ant colony algorithm is 22, the cost is 5 798.6 yuan, and customer satisfaction is 96.8%. Experiments show that the improved ant colony algorithm can be effectively used in the monitoring and scheduling of logistics vehicles.

关 键 词：蚁群算法 物流 车辆监控及调度 系统设计 爬山算法 

分 类 号：TP392[自动化与计算机技术—计算机应用技术]