The knowledge of future partial information in the form of a lookahead to
design efficient online algorithms is a theoretically-efficient and realistic
approach to solving computational problems. Design and analysis of semi-online
algorithms with extra-piece-of-information (EPI) as a new input parameter has
gained the attention of the theoretical computer science community in the last
couple of decades. Though competitive analysis is a pessimistic worst-case
performance measure to analyze online algorithms, it has immense theoretical
value in developing the foundation and advancing the state-of-the-art
contributions in online and semi-online scheduling. In this paper, we study and
explore the impact of lookahead as an EPI in the context of online scheduling
in identical machine frameworks. We introduce a k-lookahead model and design
improved competitive semi-online algorithms. For a 2-identical machine
setting, we prove a lower bound of 34β and design an optimal
algorithm with a matching upper bound of 34β on the competitive
ratio. For a 3-identical machine setting, we show a lower bound of
1115β and design a 1116β-competitive improved semi-online
algorithm.Comment: 14 pages, 1 figur