Job rejection scheduling problems with deterioration and learning effects

In our study, deterioration and the learning effect in the job rejection scheduling environment was considered together. The processing time of the jobs will decrease with the effect of learning, but will also increase with the effect of deterioration. In our study, this situation was examined for four different objectives. These problems are total completion time minimization, makespan minimization and TADC minimization for single machine scheduling problems and parallel machine scheduling problems with makespan minimization. With three different learning rates (0.8, 0.7, 0.6) and two different deterioration rates (0.2, 0.1), 10 jobs, 20 jobs and 30 jobs problems are solved with mathematical models and algorithms and a comparison table is presented.