Minimizing the maximum lateness in a scheduling problem with a time-dependent learning effect: A non-linear programming model

Abstract

In traditional scheduling problems, most literature assumes that the processing time of a job is fixed. However, there are many situations where the processing time of a job depends on the starting time or the position of the job in a sequence. In such situations, the actual processing time of a job may be more or less than its normal processing time if it is scheduled later. This phenomenon is known as the "learning effect''. In this study, we introduce a time-dependent learning effect into a single-machine scheduling problem. The objective function of the problem is minimization of the maximum lateness. This study shows that EDD rule, which guarantees the best solution in classical situation (without learning effect), can not guarantee the best results in the situation with learning effect. In addition, a non-linear programming model is proposed for this problem, and solutions are found for problems which have up to 14 jobs.