Balancing of Manual Reconfigurable Assembly Systems with Learning and Forgetting Effects

Within the paradigm of Industry 4.0, digital reconfigurable manufacturing and assembly systems can rapidly adapt to dynamic market demand, modifying their capacity and functionality. In manual or hybrid reconfigurable assembly systems, the rapid and frequent variations in the performed tasks subject workers to a significant cognitive load, making relevant the learning-forgetting phenomenon. In fact, the operators carry out the assigned activities for a short time before a reconfiguration of the system takes place, assigning them tasks often different from those just performed. This paper aims at investigating how the tasks' execution time varies for operators working along a reconfigurable assembly line, depending on the learning forgetting effect. We applied a Kottas-Lau algorithm, considering the expected execution times updated according to a learning-forgetting curve. A numerical example, considering with five successive reconfigurations, allows to analyse the expected execution time trend for each operator-task pair and the variation in costs obtained as the operators learning rate and the variability of the operations change.