Due-window assignment scheduling with job-rejection, truncated learning effects and setup times

This paper investigates the single-machine due-window assignment scheduling with truncated learning effects and past-sequence-dependent setup times simultaneously. The scheduler has option of job-rejection, i.e., some jobs are processed other rejected. Under three (i.e., common, slack different) methods, our goal is to minimize sum a general earliness-tardiness cost weighted earliness-tardiness, number early tardy jobs, starting time size due-window) total rejection cost. Some optimal properties problem provided, we demonstrate that polynomially solvable.