Optimizing operator allocation in labor-intensive cell production systems: A comparative study of fatigue-aware and proficiency-based models

Effective operation of labor-intensive cell production systems hinge on the identification and accurate training of operators’ skills. Learning models that simulate proficiency play a pivotal role in predicting operators’ skills. However, there are adverse effects due to fatigue accumulation. This study undertakes a comprehensive comparison of several learning-fatigue models with the learning model, meticulously evaluating their features to provide a thorough understanding. Then, we perform computer experiments of task assignment on these models to teach skills and compare the results of each model. The study’s findings underscore the practicality of the learning-fatigue model for a wide range of learning and fatigue patterns, thereby validating its effectiveness.