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

Abstract

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

Topics & Concepts