In light of Industry 4.0, rapid analysis and optimization of manufacturing processes are emerging as a vital demand of smart manufacturing factories. Ergonomics is an essential aspect of the ongoing screening of working conditions and a fundamental variable in Industry 4.0, as it calls for a flexible manufacturing system to strengthen the competitiveness of factories in the global market. This paper proposes a new augmented reality-based ergonomic evaluation platform: Augmented Reality-based Ergonomic Platform (ARE Platform). Introducing the Augmented Reality technology by superimposing virtual planning objects into the physically existing production environment. Utilizing the motion capture system collects data for a set of ergonomic indexes (RULA, OWAS, and NIOSH), accessibility and visibility verification. ARE platform could be used in the verification phase of smart manufacturing systems to evaluate the level of risk to workers’ bodies during operations in real-time. The platform reduces the time and economic cost of verification and satisfies the rapid response of ergonomic evaluation and feedback in the context of smart manufacturing. Finally, the developed ARE platform is validated in two rigorous automotive assembly cases in the laboratory. Meanwhile, the ergonomic assessment results are analysed and reported for a people-oriented manufacturing system.

With the extensive application of automated guided vehicles, real-time production scheduling considering logistics services in cloud manufacturing (CM) becomes an urgent problem. Thus, this study focuses on the distributed real-time scheduling (DRTS) of multiple services to respond to dynamic and customized orders. First, a DRTS framework with cloud–edge collaboration is proposed to improve performance and satisfy responsiveness, where distributed actors and one centralized learner are deployed in the edge and cloud layer, respectively. And, the DRTS problem is modeled as a semi-Markov decision process, where the processing services sequencing and logistics services assignment are considered simultaneously. Then, we developed a distributed dueling deep Q network (D3QN) with cloud–edge collaboration to optimize the weighted tardiness of jobs. The experimental results show that the proposed D3QN obtains lower weighted tardiness and shorter flow-time than other state-of-the-art algorithms. It indicates the proposed DRTS method has significant potential to provide efficient real-time decision-making in CM.