Scheduling with general job-dependent learning curves

Several recent papers focused on the effect of learning on the optimal solution of scheduling problems. We extend the setting studied so far to the case of job-dependent learning curves, that is, we allow the learning in the production process of some jobs to be faster than that of others. Our learning curve approach, which assumes learning takes place as a function of repetition of the production process, is otherwise completely general, and is not based upon any particular model of learning acquisition. We show that in the new, possibly more realistic setting, the problems of makespan and total flow-time minimization on a single machine, a due-date assignment problem and total flow-time minimization on unrelated parallel machines remain polynomially solvable.