Flow-shop path planning for multi-automated guided vehicles in intelligent textile spinning cyber-physical production systems dynamic environment

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 singlepath distances of each AGVs exerted. By using the improved GA, simulation results show that the total path distance was shortened.