Scheduling problems under learning effects: classification and cartography

Traditionally, the processing times of jobs are assumed to be fixed and known throughout the entire process. However, recent empirical research in several industries has demonstrated that processing times decline as workers improve their skills and gain experience after doing the same task for a long time. This phenomenon is known as learning effects. Recently, several researchers have devoted a lot of effort on scheduling problems under learning effects. Although there is increase in the number of research in this topic, there are few review papers. The most recent one considers solely studies on scheduling problems with learning effects models prior to early 2007. For that, this paper focuses on reviewing the most recent advances in this field. First, we attempt to present a concise overview of some important learning models. Second, a new classification scheme for the different model of scheduling under learning effects is proposed and discussed. Next, a cartography showing the relation between some well-known models is proposed. Finally, our viewpoints and several areas for future research are provided.