摘要
针对移动机器人履行系统(RMFS)中大规模订单任务的动态性和复杂性,改进了任务效用的计算方法,提出了一种基于模糊逻辑的任务分配改进拍卖算法。首先考虑AGV运行的影响因素,引入模糊逻辑构建任务效用计算函数,以总效用最大为目标构建分配模型;然后改进传统的拍卖算法,以减少模型求解所需的计算量。最后通过离散事件仿真实验,验证了建立的RMFS模型的有效性,且证明了提出的改进拍卖算法在一定程度上减少了任务分配的总完成时间与资源消耗,提高了RFMS订单任务分配的作业效率。
In this paper, in view of the dynamics and complexity of the large-scale order tasks in RMFS(Robotic Mobile Fulfillment System), we improved the calculation method of task utility, and proposed an improved auction algorithm for task allocation based on fuzzy logic. Firstly, considering the influencing factors of AGV operation, we introduced the fuzzy logic to construct the task utility calculation function and built the allocation model with maximized total utility as the objective. Then, we modified the traditional auction algorithm to reduce the amount of calculation needed to solve the model. Finally, through a discrete event simulation experiment, we demonstrated the effectiveness of the RMFS model established, and proved that the proposed improved auction algorithm could reduce the total completion time and resource consumption required in task allocation by a certain extent, and improve the operation efficiency of the RFMS-based order task allocation process.
作者
陶芬
李文锋
TAO Fen;LI Wenfeng(School of Transportation&Logistics Engineering,Wuhan University of Technology,Wuhan 430063,China)
出处
《物流技术》
2022年第12期63-68,共6页
Logistics Technology
