Manufacturing Nudging Personalization Through Optimization of Nudge Configuration Using 2D Genetic Algorithm

Human-automation symbiosis (HAS) is a key aspect of Industry 5.0, marking the collaborative relationship between humans and automation systems. Nudging, a behavioral economics concept that indirectly encourages individuals to act in a certain way through subtle interventions, can be applied in the manufacturing context to improve the collaboration efficiency between human and automation agents. While manufacturing nudges facilitates the symbiotic relationship, they may also impact certain aspects of manufacturing system performance. Therefore, the objective of nudging personalization is to achieve HAS while maintaining system performance for a group of people, which suggests a multi-objective optimization problem. In this paper, a behavioral economics model for nudge evaluation using the conjoint prospect theory is proposed, and the formulation of manufacturing nudging personalization optimization is proposed, which is defined as the prospect value of the selected nudges relative to their engineering cost. A 2D genetic algorithm (GA) is employed to solve the optimization problem. Its feasibility and effectiveness are validated through a jet engine assembly case study.