文章基本信息

标题：Improved Control Scheduling Based on Learning to Prediction Mechanism for Efficient Machine Maintenance in Smart Factory
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作者：Sehrish Malik ; DoHyeun Kim
期刊名称：Actuators
电子版ISSN：2076-0825
出版年度：2021
卷号：10
期号：2
页码：27
DOI：10.3390/act10020027
出版社：MDPI Publishing
摘要：The prediction mechanism is very crucial in a smart factory as they widely help in improving the product quality and customer’s experience based on learnings from past trends. The implementation of analytics tools to predict the production and consumer patterns plays a vital rule. In this paper, we put our efforts to find integrated solutions for smart factory concerns by proposing an efficient task management mechanism based on learning to scheduling in a smart factory. The learning to prediction mechanism aims to predict the machine utilization for machines involved in the smart factory, in order to efficiently use the machine resources. The prediction algorithm used is artificial neural network (ANN) and the learning to prediction algorithm used is particle swarm optimization (PSO). The proposed task management mechanism is evaluated based on multiple scenario simulations and performance analysis. The comparisons analysis shows that proposed task management system significantly improves the machine utilization rate and drastically drops the tasks instances missing rate and tasks starvation rate.
关键词：real-time tasks; task scheduling; smart factory; periodic tasks; event-driven tasks real-time tasks ; task scheduling ; smart factory ; periodic tasks ; event-driven tasks