Common due date scheduling with autonomous and induced learning

Up to now the few existing models that consider learning effects in scheduling, concentrate on learning-by-doing (autonomous learning). But recent contributions to the literature on learning in manufacturing organizations emphasize the important impact of proactive investments in technological knowledge on the learning rate (induced learning). In the present paper, we focus on a scheduling problem where the processing times decrease according to a learning rate, which can be influenced by an initial cost-inducing investment. Thus we have integrated into our model both aspects of learning – autonomous and induced – thereby highlighting the management's responsibility to invest in technological knowledge enhancement. We have been able to derive some structural properties of the problem and present a polynomially bound solution procedure which optimally solves the problem by using these properties. The optimal solution to the scheduling problem contains – of course – information on the optimal level of proactive investments in learning.