DUE-WINDOW ASSIGNMENT SCHEDULING WITH LEARNING AND DETERIORATION EFFECTS

Abstract

This paper considers single machine due-window assignment scheduling problems with position-dependent weights. Under the learning and deterioration effects of jobs processing times, our goal is to minimize the weighted sum of earliness-tardiness, starting time of due-window, and due-window size, where the weights only depends on their position in a sequence (i.e., positiondependent weights). Under common due-window (CONW), slack due-window (SLKW) and different due-window (DIFW) assignments, we show that these problems remain polynomial-time solvable.