Flow-shop predictive modeling for multi-automated guided vehicles scheduling in smart spinning cyber–physical production systems

Abstract

Pointed at a problem that leads to the high complexity of the production management tasks in the multi-stage spinning industry, mixed flow batch production is often the case in response to a customer’s personalized demands. Manual handling cans have a large number of tasks, and there is a long turnover period in their semi-finished products. A novel heuristic research was conducted that considered mixed-flow shop scheduling problems with automated guided vehicle (AGV) distribution and path planning to prevent conflict and deadlock by optimizing distribution efficiency and improving the automation degree of can distribution in a draw-out workshop. In this paper, a cross-region shared resource pool and an inter-regional independent resource pool, two AGV predictive scheduling strategies are established for the ring-spinning combing process. Besides completion time, AGV utilization rate and unit AGV time also analyzed with the bottleneck process of the production line. The results of the optimal computational experiment prove that a draw frame equipped with multi-AGV and coordinated scheduling optimization will significantly improve the efficiency of can distribution. Flow-shop predictive modeling for multi-AGV resources is scarce in the literature, even though this modeling also produces, for each AGV, a control mode and, if essential, a preventive maintenance plan.