Semi-online scheduling problems on two identical machines with inexact partial information

Authors:
Zhiyi Tan;Yong He
Affiliations:
Department of Mathematics, State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310027, PR China;Department of Mathematics, State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310027, PR China
Venue:
Theoretical Computer Science
Year:
2007

Citing 9
Cited 1

On the performance of on-line algorithms for partition problems

Acta Cybernetica
Semi on-line scheduling on two identical machines

Computing
On-line bin-stretching

Theoretical Computer Science
The On-Line Multiprocessor Scheduling Problem with Known Sum of the Tasks

Journal of Scheduling
Semi-on-line multiprocessor scheduling with given total processing time

Theoretical Computer Science
Semi-online algorithms for parallel machine scheduling problems

Computing
Semi on-line algorithms for the partition problem

Operations Research Letters
Semi-online scheduling with decreasing job sizes

Operations Research Letters
Semi-on-line problems on two identical machines with combined partial information

Operations Research Letters

Semi-online scheduling with known partial information about job sizes on two identical machines

Theoretical Computer Science

Quantified Score

Hi-index	5.23

Visualization

Abstract

In semi-online scheduling problems, we always assume that some partial additional information is exactly known in advance. This may not be true in some applications. This paper considers semi-online scheduling problems on two identical machines with inexact partial information. Three versions are considered, where we know in advance that the total size of all jobs, the optimal value, and the largest job size are in given intervals, respectively, while their exact values are unknown. We give both lower bounds of the problems and competitive ratios of algorithms as functions of a so-called disturbance parameter r@?[1,~). We establish for which r the inexact partial information is useful to improve the performance of a semi-online algorithm with respect to its pure online problem. Optimal or near optimal algorithms are then obtained.