Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UK.
Department of Computer Science and Informatics, London South Bank University, London SE1 0AA, UK.
Sensors (Basel). 2020 Nov 6;20(21):6333. doi: 10.3390/s20216333.
Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly.
自主引导车（AGV）是无人驾驶的物料搬运系统，用于运输托盘和线边供应物料，为车间物流提供灵活性和敏捷性。由于其与多种零件类型和替代物料传输路线相关的复杂性质，因此对这些系统中的车间物流进行调度是一项具有挑战性的任务。本文提出了一种决策支持系统，该系统能够通过自动调整柔性制造系统（FMS）中的 AGV 和机器计划来支持制造中断期间的车间决策制定活动。所提出的系统使用离散事件仿真（DES）模型，并通过物联网（IoT）增强数字集成，采用非线性混合整数编程遗传算法（GA）来找到接近最优的生产计划，优先考虑准时（JIT）物料交付性能和物料运输的能源效率。该系统的性能在沃里克大学 WMG 的综合制造和物流（IML）演示器上进行了测试。结果表明，所开发的系统可以在很短的时间内为生产计划找到接近最优的解决方案，从而有效地和快速地支持车间决策制定活动。
