基于改进GA算法的AGV运输车路径优化及自动物流仓储系统闭环管控研究

Research on AGV transporter path optimization and closed-loop control of automatic logistics and warehousing system based on improved GA algorithm

信息技术和自动化控制技术的不断发展,使得自动仓储物流技术得到了新的利用和优化。为了使自动导引车在物流运输中能够利用最小化成本实现最大化收益,此次研究结合遗传算法设计了自动导引运输车路径优化方案,并且设计了物流仓储系统自动化闭环管控方案。研究结果表明,相较于狼群算法和传统的遗传算法来说,改进后的遗传算法能够更好地对自动导引车的运输路径进行优化。当分别以成本最小化和路径最小化为优化目标时,改进遗传算法分别迭代187次和92次就能完成收敛,对于最优运输路径的规划时间分别为2.08 min和1.96 min。综上,此次研究所采用的路径优化方案具有一定的有效性,将其与自动仓储系统结合能够实现高效的闭环管控。

The continuous development of information technology and automation control technology has led to new utilization and optimization of automatic storage and logistics technology.In order to make the automated guided vehicle in logistics transportation can use the minimization of cost to achieve maximum benefits,this study combined with the genetic algorithm to design the automated guided vehicle path optimization scheme,and designed the logistics warehousing system automated closed-loop control scheme.The results show that compared with the wolf pack algorithm and the traditional genetic algorithm,the improved genetic algorithm can better optimize the transportation path of the automated guided vehicle.When cost minimization and path minimization are the optimization objectives,the improved genetic algorithm converges in 187 and 92 iterations respectively,and the planning time for the optimal transportation path is 2.08 min and 1.96 min respectively.In conclusion,the path optimization scheme adopted in this study is effective,and the combination of this scheme with the automatic warehousing system can realize the efficient closed-loop control.