Construction and application of an ergonomic simulation optimization method driven by a posture load regulatory network

The optimization of man–machine systems is a critical component in the research and development of products but it has been a struggle to improve the optimization accuracy. This study presents an ergonomic optimization method driven by a posture load regulatory network (PLRN). Considering that the differences in work-related musculoskeletal disorders are caused by different occupations, human body part data collection is completed using the deconstructions of man–machine task sequences, which applies in partial theory of the complex network to build the PLRN model. Then the approach connects the human body part data with the regulatory network to calculate the cumulative load tendency and the performance of the load group; results of the analysis provide support for the ranking of human body part loads. In addition, we derive the mapping relationship between the man–machine workload and product engineering modules based on the quality function deployment theory, which can reflect the man–machine system problems of products and assist designers in optimizing and making decisions in man–machine systems. To this end, this paper provides a case study research for evaluating the feasibility of the PLRN simulation optimization method. Results show that our method is capable of explaining the change and the predicting the tendency of human load during the man–machine operation. Compared with the traditional subjective analytic hierarchy process, the PLRN simulation optimization method provides more accurate and objective evaluation on product ergonomics, and new research opportunities on ergonomic optimization.