调度(生产过程)
动态优先级调度
人工智能
流水车间调度
深度学习
单调速率调度
作业调度程序
作者
Bruno Cunha,Ana Madureira,Benjamim Fonseca,Duarte Coelho
出处
期刊:Advances in intelligent systems and computing
日期:2019-03-21
卷期号:: 350-359
被引量:24
标识
DOI:10.1007/978-3-030-14347-3_34
摘要
Complex optimization scheduling problems frequently arise in the manufacturing and transport industries, where the goal is to find a schedule that minimizes the total amount of time (or cost) required to complete all the tasks. Since it is a critical factor in many industries, it has been, historically, a target of the scientific community. Mathematically, these problems are modelled with Job Shop scheduling approaches. Benchmark results to solve them are achieved with evolutionary algorithms. However, they still present some limitations, mostly related to execution times and the difficulty to generalize to other problems. Deep Reinforcement Learning is poised to revolutionise the field of artificial intelligence. Chosen as one of the MIT breakthrough technologies, recent developments suggest that it is a technology of unlimited potential which shall play a crucial role in achieving artificial general intelligence. This paper puts forward a state-of-the-art review on Job Shop Scheduling, Evolutionary Algorithms and Deep Reinforcement Learning. It also proposes a novel architecture capable of solving Job Shop Scheduling optimization problems using Deep Reinforcement Learning.
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