The Asymptotic Performance Ratio of an On-Line Algorithm for Uniform Parallel Machine Scheduling with Release Dates

Authors:
Cheng-Feng Mabel Chou;Maurice Queyranne;David Simchi-Levi
Affiliations:
-;-;-
Venue:
Proceedings of the 8th International IPCO Conference on Integer Programming and Combinatorial Optimization
Year:
2001

Citing 3
Cited 1

Improved approximation algorthims for scheduling with release dates

SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
Approximation techniques for average completion time scheduling

SODA '97 Proceedings of the eighth annual ACM-SIAM symposium on Discrete algorithms
On-line Scheduling

Developments from a June 1996 seminar on Online algorithms: the state of the art

LP-based online scheduling: from single to parallel machines

IPCO'05 Proceedings of the 11th international conference on Integer Programming and Combinatorial Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

Jobs arriving over time must be non-preemptively processed on one of m parallel machines, each of which running at its own speed, so as to minimize a weighted sum of the job completion times. In this on-line environment, the processing requirement and weight of a job are not known before the job arrives. The Weighted Shortest Processing Requirement (WSPR) on-line heuristic is a simple extension of the well known WSPT heuristic, which is optimal for the single machine problem without release dates. We prove that the WSPR heuristic is asymptotically optimal for all instances with bounded job processing requirements and weights. This implies that the WSPR algorithm generates a solution whose relative error approaches zero as the number of jobs increases. Our proof does not require any probabilistic assumption on the job parameters and relies extensively on properties of optimal solutions to a single machine relaxation of the problem.