Stochastic assembly line balancing with learning effects

Human learning is nowadays taken into account in several research fields, including the assembly line balancing problem. Despite the plethora of contributions and different approaches to solving the problem, the autonomous learning phenomenon, that is to say, the time-dependent or position-dependent reduction of assembly task times due to repetition, should also be explored using stochastic models which, to the best of our knowledge, have been disregarded. In this paper, a well-established cost-based stochastic balancing heuristic has been coupled with a time-dependent learning curve in order to investigate the role of learning in the rebalancing of assembly lines with repetitive tasks. Finally, a real case study has been conducted with the aim of demonstrating the applicability of our proposal.