Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Single machine scheduling with flow time and earliness penalties

Single machine scheduling with flow time and earliness penalties This paper considers the problem of schedulingn jobs on a single machine to minimize the total cost incurred by their respective flow time and earliness penalties. It is assumed that each job has a due date that must be met, and that preemptions are not allowed. The problem is formulated as a dynamic program (DP) and solved with a reaching algorithm that exploits a series of dominance properties and efficiently generated bounds. A major factor underlying the effectiveness of the approach is the use of a greedy randomized adaptive search procedure (GRASP) to construct high quality feasible solutions. These solutions serve as upper bounds on the optimum, and permit a predominant portion of the state space to be fathomed during the DP recursion. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Global Optimization Springer Journals

Single machine scheduling with flow time and earliness penalties

Loading next page...
 
/lp/springer-journals/single-machine-scheduling-with-flow-time-and-earliness-penalties-0Rl06tBGdx

References (30)

Publisher
Springer Journals
Copyright
Copyright
Subject
Mathematics; Optimization; Operations Research/Decision Theory; Real Functions; Computer Science, general
ISSN
0925-5001
eISSN
1573-2916
DOI
10.1007/BF01096772
Publisher site
See Article on Publisher Site

Abstract

This paper considers the problem of schedulingn jobs on a single machine to minimize the total cost incurred by their respective flow time and earliness penalties. It is assumed that each job has a due date that must be met, and that preemptions are not allowed. The problem is formulated as a dynamic program (DP) and solved with a reaching algorithm that exploits a series of dominance properties and efficiently generated bounds. A major factor underlying the effectiveness of the approach is the use of a greedy randomized adaptive search procedure (GRASP) to construct high quality feasible solutions. These solutions serve as upper bounds on the optimum, and permit a predominant portion of the state space to be fathomed during the DP recursion.

Journal

Journal of Global OptimizationSpringer Journals

Published: Jan 19, 2005

There are no references for this article.