On the performance of on-line algorithms for partition problems
Acta Cybernetica
New algorithms for an ancient scheduling problem
Journal of Computer and System Sciences - Special issue on selected papers presented at the 24th annual ACM symposium on the theory of computing (STOC '92)
A better algorithm for an ancient scheduling problem
Journal of Algorithms
Better Bounds for Online Scheduling
SIAM Journal on Computing
Theoretical Computer Science
On randomized online scheduling
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Optimal Non-preemptive Semi-online Scheduling on Two Related Machines
MFCS '02 Proceedings of the 27th International Symposium on Mathematical Foundations of Computer Science
Developments from a June 1996 seminar on Online algorithms: the state of the art
Semi on-line algorithms for the partition problem
Operations Research Letters
Ordinal algorithms for parallel machine scheduling
Operations Research Letters
Semi-online scheduling with decreasing job sizes
Operations Research Letters
Semi-on-line scheduling with ordinal data on two uniform machines
Operations Research Letters
Semi-on-line problems on two identical machines with combined partial information
Operations Research Letters
Information Processing Letters
Semi-online problems on identical machines with inexact partial information
COCOON'05 Proceedings of the 11th annual international conference on Computing and Combinatorics
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This paper investigates the semi-online version of scheduling problem P||C"m"a"x on a three-machine system. We assume that all jobs have their processing times between p and rp (p0,r=1). We give a comprehensive competitive ratio of LS algorithm which is a piecewise function on r=1. It shows that LS is an optimal semi-online algorithm for every r@?[1,1.5], [3,2] and [6,+~). We further present an optimal algorithm for every r@?[2,2.5], and an almost optimal algorithm for every r@?(2.5,3] where the largest gap between its competitive ratio and the lower bound of the problem is at most 0.01417. We also present an improved algorithm with smaller competitive ratio than that of LS for every r@?(3,6).