A Stochastic-Based Model to Assess the Variability of Task Completion Times of Differently Aged and Experienced Workers Subject to Fatigue

In current industrial scenarios, the new paradigm of Industry 5.0 (I5.0) is gaining interest: considering the Industry 4.0 paradigm as a base, I5.0 is aimed at reaching a more sustainable, human-centric, and resilient industry. Under the I5.0 human-centric perspective, behavioural issues assume high criticality, thus requiring a more reliable prediction of operators’ performances. In manual assembly lines, operators’ performances are characterized by a stochastic behaviour over time. System’s and operator’s features cause the variability of task completion time: the former is related to properties of the work environment (e.g., ergonomics, cycle time), the latter is related to the intrinsic stochastic behaviour of operators. Furthermore, workers’ features and their different attitudes to becoming fatigued, influence performance variability. In this context, the authors propose a new stochastic model that expresses the variability of execution times of operators involved in manual assembly lines by considering their differences in age, experience, and fatigue state. The novelty of the proposed model relies on considering the stochastic behaviour of workers influenced by age, experience as well as fatigue. The effectiveness of the proposed model is tested through numerical experiments of a job rotation scheduling problem to maximize productivity with proper worker-workstation assignments.