Enabling Knowledge Discovery in Multi-Objective Optimizations of Worker Well-Being and Productivity

Usually, optimizing productivity and worker well-being are separate tasks performed by engineers with different roles goals using tools. This results in a silo effect which can lead to slow development process suboptimal solutions, one of the objectives, either or well-being, being given precedence. Moreover, studies often focus on finding best solutions for particular use case, once have been identified has implemented, move analyzing next case. However, knowledge obtained from previous cases could be used find rules thumb similar without needing perform new optimizations. In this study, we employed data mining methods obtain real-world optimization dataset multi-objective optimizations aim identify actionable insights current future cases. Using analysis database revealed rules, as well relative importance design variables workstation. The generated measures improve welding gun workstation design.