Single-machine scheduling with multiple performance measures: Minimizing job-dependent earliness and tardiness subject to the number of tardy jobs

This research considers a single-machine scheduling problem with the objective of minimizing the summation of the weighted earliness and tardiness, subject to the number of tardy jobs. There are n jobs with a given common due date and each job has different weights for earliness and tardiness. We propose a Pareto-optimal solution algorithm, by which we can efficiently generate Pareto-optimal solutions for any possible number of tardy jobs. We also work with the benchmark problems discussed by Biskup and Feldmann to show the superior accuracy and run time of our algorithm. The computational analysis also shows that approximately 47.15% of the nodes in the branching tree can be eliminated.