An Optimal Online Algorithm for Scheduling with Learning Consideration

Abstract

This paper investigates a classic online scheduling problem with learning effect on a single machine. Specifically, a number of independent jobs that arrive online over time will be processed on a single machine and le2arning effect implies that the real processing time of job [Formula: see text] is a non-increasing function of its position [Formula: see text], i.e., [Formula: see text], where [Formula: see text] is the basic processing time of job [Formula: see text] and [Formula: see text] is the learning index. Our goal is to minimize the total completion time of all jobs. For the problem, we develop a deterministic polynomial time online algorithm called Delayed Shortest Basic Processing Time (DSBPT) and state that it is an online algorithm with a competitive ratio of 2, which matches the lower bound of the online scheduling problem we focus on.