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Flow-shop path planning for multi-automated guided vehicles in intelligent textile spinning cyber-physical production systems dynamic environment
Journal of Manufacturing Systems ( IF 12.2 ) Pub Date : 2021-02-09 , DOI: 10.1016/j.jmsy.2021.01.009
Basit Farooq , Jinsong Bao , Hanan Raza , Yicheng Sun , Qingwen Ma

Aiming at the path planning and decision-making problem, multi-automated guided vehicles (AGVs) have played an increasingly important role in the multi-stage industries, e.g., textile spinning. We recast a framework to investigate the improved genetic algorithm (GA) on multi-AGV path optimization within spinning drawing frames to solve the complex multi-AGV maneuvering scheduling decision and path planning problem. The study reported in this paper simplifies the scheduling model to meet the drawing workshop's real-time application requirements. According to the characteristics of decision variables, the model divides into two decision variables: time-independent variables and time-dependent variables. The first step is to use a GA to solve the AGV resource allocation problem based on the AGV resource pool strategy and specify the sliver can's transportation task. The second step is to determine the AGV transportation scheduling problem based on the sliver can-AGV matching information obtained in the first step. One significant advantage of the presented approach is that the fitness function is calculated based on the machine selection strategy, AGV resource pool strategy, and the process constraints, determining the scheduling sequence of the AGVs to deliver can. Moreover, it discovered that double-path decision-making constraints minimize the total path distance of all AGVs, and minimizing single-path distances of each AGVs exerted. By using the improved GA, simulation results show that the total path distance was shortened.



中文翻译:

智能纺织纺网络物理生产系统动态环境中多导引导车的流水车间路径规划

针对路径规划和决策问题,多自动导引车(AGV)在纺织纺纱等多阶段行业中发挥了越来越重要的作用。我们重铸了一个框架,以研究在旋转并条机内的多AGV路径优化的改进遗传算法(GA),以解决复杂的多AGV机动调度决策和路径规划问题。本文报道的研究简化了调度模型,以满足制图车间的实时应用需求。根据决策变量的特征,该模型分为两个决策变量:与时间无关的变量和与时间有关的变量。第一步是使用遗传算法基于AGV资源池策略解决AGV资源分配问题,并指定条子罐的运输任务。第二步是根据第一步获得的条子罐-AGV匹配信息确定AGV运输调度问题。提出的方法的一个重要优点是,适应度函数是根据机器选择策略,AGV资源池策略和过程约束条件来计算的,从而确定了要交付罐的AGV的调度顺序。此外,它发现双路径决策约束最小化了所有AGV的总路径距离,并最小化了所施加的每个AGV的单路径距离。通过使用改进的遗传算法,仿真结果表明总路径距离缩短了。的运输任务。第二步是根据第一步获得的条子罐-AGV匹配信息确定AGV运输调度问题。提出的方法的一个重要优点是,适应度函数是根据机器选择策略,AGV资源池策略和过程约束条件来计算的,从而确定了要交付罐的AGV的调度顺序。此外,它发现双路径决策约束最小化了所有AGV的总路径距离,并最小化了所施加的每个AGV的单路径距离。通过使用改进的遗传算法,仿真结果表明总路径距离缩短了。的运输任务。第二步是根据第一步获得的条子罐-AGV匹配信息确定AGV运输调度问题。提出的方法的一个重要优点是,适应度函数是根据机器选择策略,AGV资源池策略和过程约束条件来计算的,从而确定了要交付罐的AGV的调度顺序。此外,它发现双路径决策约束最小化了所有AGV的总路径距离,并最小化了所施加的每个AGV的单路径距离。通过使用改进的遗传算法,仿真结果表明总路径距离缩短了。提出的方法的一个重要优点是,适应度函数是根据机器选择策略,AGV资源池策略和过程约束条件来计算的,从而确定了要交付罐的AGV的调度顺序。此外,它发现双路径决策约束使所有AGV的总路径距离最小化,并使所施加的每个AGV的单路径距离最小化。通过使用改进的遗传算法,仿真结果表明总路径距离缩短了。提出的方法的一个重要优点是,适应度函数是根据机器选择策略,AGV资源池策略和过程约束条件来计算的,从而确定了要交付罐的AGV的调度顺序。此外,它发现双路径决策约束最小化了所有AGV的总路径距离,并最小化了所施加的每个AGV的单路径距离。通过使用改进的遗传算法,仿真结果表明总路径距离缩短了。研究发现,双路径决策约束使所有AGV的总路径距离最小化,并使所施加的每个AGV的单路径距离最小化。通过使用改进的遗传算法,仿真结果表明总路径距离缩短了。研究发现,双路径决策约束使所有AGV的总路径距离最小化,并使所施加的每个AGV的单路径距离最小化。通过使用改进的遗传算法,仿真结果表明总路径距离缩短了。

更新日期:2021-02-10
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