Multi-Objective Human-Robot Collaborative Stochastic Two-Sided Assembly Line Balancing Problem Considering Ergonomic Risk

Abstract

Traditional approaches to two-sided assembly line balancing often ignore the complexities introduced by humanrobot collaboration, particularly stochastic operation times and the associated ergonomic risks. This study addresses this problem by developing a novel multi-objective optimization model that integrates these critical, real-world factors. The model explicitly aims to minimize three key objectives: cycle time, total ergonomic fatigue, and the smoothness index. To handle the inherent uncertainty in operation times, we employ chance-constrained programming, while ergonomic risk is dynamically assessed through muscle fatigue-recovery modeling. For optimization, a decomposition-based multi-objective evolutionary algorithm (MOEA/D) with Tchebycheff scalarization is adopted. Through a detailed case study on keyboard assembly and a subsequent sensitivity analysis on operation time variance, the results reveal distinct trade-offs between production efficiency and ergonomic risk. This work provides a robust methodological framework and valuable insights for designing assembly systems that are not only efficient and robust but also human-centric.