Simulated annealing approach for the single-machine total late work scheduling problem with a position-based learning

This paper considers a single-machine scheduling problem with a position-based learning effect where the aim is to find an optimal sequence to minimize the total late work. The late work for a job means the amount of processing of this job that is performed after its due date. Because the problem under consideration is NP-hard, this paper then proposes several simulated annealing algorithms for the near-optimal solution. Finally, the computational results of proposed algorithms are also reported.