Machine scheduling with DeJong's learning effect

We consider a relatively new learning model in scheduling based on DeJong's learning effect. Compared with the classical learning model for scheduling, this model is more general and realistic. The objectives are to minimize makespan and total completion time. For the single-machine case, we show that both of the objectives are polynomially solvable. For the parallel-machine case, we show that minimizing total completion time is still polynomially solvable, while minimizing makespan is NP-hard, for which we provide a fully polynomial-time approximation scheme (FPTAS).