首页|Researchers from Beijing University of Posts and Telecommunications Report Recen t Findings in Computational Intelligence (Flowshop Scheduling Problem With Batc h Processing Machines Via Deep Reinforcement Learning for Industrial Internet of ...)
Researchers from Beijing University of Posts and Telecommunications Report Recen t Findings in Computational Intelligence (Flowshop Scheduling Problem With Batc h Processing Machines Via Deep Reinforcement Learning for Industrial Internet of ...)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Current study results on Machine Learn ing - Computational Intelligence have been published. According to news reportin g originating from Beijing, People's Republic of China, by NewsRx correspondents , research stated, "The rapidly evolving Industrial Internet of Things (IIoT) is driving the transition from conventional manufacturing to intelligent manufactu ring. Intelligent shop scheduling, as one of the essential components of intelli gent manufacturing in IIoT, is desired to allocate jobs on different machines to achieve specific production targets." Financial support for this research came from National Natural Science Foundatio n of China (NSFC). Our news editors obtained a quote from the research from the Beijing University of Posts and Telecommunications, "The flow-shop scheduling problem with batch pr ocessing machines (FSSP-BPM), which widely exists in real-world manufacturing, r equires two distinct but interdependent decisions: batch formation and job sched uling. Existing approaches rely on fixed search paradigms that utilize expert kn owledge to find satisfactory solutions. However, these methods struggle to ensur e solution quality under real-time constraints due to the varying data distribut ion and the complexity of large-scale practical problems. To address this challe nge, we propose a deep reinforcement learning (DRL) based method. First, we form ulate the FSSP-BPM decision process as a Markov Decision Process (MDP) and desig n the corresponding state, action, and reward. Second, we propose a basic schedu ling framework based on an encoder-decoder model with the attention mechanism. F inally, we design a batch formation module and a scheduling module trained on un labeled multi-dimensional data."
BeijingPeople's Republic of ChinaAsi aComputational IntelligenceEmerging TechnologiesMachine LearningReinforc ement LearningBeijing University of Posts and Telecommunications