Workers in modular construction suffer frequent exposure to ergonomic risks that lead to work-related musculoskeletal disorders (WMSDs). Addressing ergonomic risk factors is thus critical to enhance the productivity of production lines and reduce social expenses for workers' recovery. Towards this goal, an ergonomic-driven workplace design approach is essential to not only prevent risks through design changes proactively but also accommodate medical restrictions for workers getting back on the job during the health recovery period. However, a lack of methods to identify root causes of ergonomic risks among various workplace design parameters (WDPs) and design optimal workplace settings for complex and multiple tasks leads to difficulties in adopting this twofold design approach. To address this limitation, this thesis proposes a parameterized workplace design optimization framework that involves four procedures: (i) performing design initiation to identify WDPs and accordingly create design alternatives using the definitive screening design (DSD) method; (ii) building interactive worker-workplace simulation models to acquire workers' body posture data and assess ergonomic risks among the different design alternatives; (iii) developing predictive surrogate models of the tasks using DSD statistical analysis; and (iv) optimizing workplace settings using the genetic algorithm to minimize ergonomic risk scores. The proposed framework is demonstrated through a case study to design a drywall preparation workplace in a real modular construction plant.

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