Can collaborative robotics help in large parts picking for assembly? An ergonomics/economics model

Abstract

Manual assembly workstations still devote a relevant share of their cycle time to non-value-added picking-and-placing activities, which may also expose operators to awkward postures when bulky parts are stored in rear locations. This paper studies whether collaborative robots (cobots) can support large-part picking in Human–Robot Collaboration (HRC) assembly systems while jointly improving productivity, ergonomics, and economic feasibility. A structured offline decision-support framework is proposed. The method combines storage-location- and part-attribute-dependent estimation of Picking-and-Placing Time (PPT), a Mixed-Integer Linear Programming (MILP) task allocation model for two operators and one cobot, a time-weighted Rapid Entire Body Assessment (REBA) indicator, and an iso-payback economic analysis. The framework is applied to a 26-task assembly case study. Compared with the human-only baseline, the makespan-oriented solution reduces the cycle time from 553 s to 493 s (about 11%), while the selected bi-objective solution achieves a cycle time of 507 s and reduces the time-weighted REBA values from 2.45 to 1.91 for Operator 1 and from 2.05 to 0.73 for Operator 2. The economic analysis shows that productivity gains mainly drive feasibility in low-margin contexts, whereas monetized ergonomic improvements extend the acceptable payback region. The results indicate that cobot adoption should be evaluated as an integrated design problem involving task characteristics, storage configuration, ergonomic exposure, and investment conditions.