Production scheduling with autonomous and induced learning

The vast majority of scheduling research involving the learning effect only considers autonomous learning, i.e. learning by doing. Proactive investment in learning promotion, i.e. induced learning, is rarely considered. Nevertheless, induced learning is important for total production cost reduction and helping managers control the production systems, which can be interpreted as management or investment seeking to improve employees’ working efficiency. We consider in this paper scheduling models with both autonomous and induced learning. The objective is to find the optimal sequence and level of induced learning that optimise a scheduling criterion plus the investment cost. We propose polynomial-time algorithms to solve all the single-machine scheduling problems considered and the parallel-machine problem to minimise the total completion time plus the investment cost. We also propose an approximate algorithm for the parallel-machine problem to minimise the makespan plus the investment cost.