On Scheduling Problems with an Intelligent Use of the Learning Effect

This paper is devoted to scheduling problems with the learning effect, which is understood as a process of acquiring experience that increases the efficiency of a processor. To bring closer the considered phenomenon, a short survey on results concerning scheduling problems with the learning effect is provided. In particular, the existing models of the experience are presented along with a discussion on different shapes of the learning curve. Some complexity results of scheduling problems with the learning effect are also presented. We also show that scheduling problems with the learning effect model such problems as a minimization of a total transmission cost of packets in a computer network that uses a reinforcement learning routing algorithm. We also derive properties that allow us to construct scheduling algorithms, which can be applied in the computer network to increase its effectiveness by the utilization of its learning ability.