Skip to main content
padlock icon - secure page this page is secure

Approximately optimal algorithms for scheduling a single machine with general convex resource functions

Buy Article:

$61.00 + tax (Refund Policy)

This paper studies batch-scheduling problems with different resource-allocation polices and general convex (g-conv) resource functions to minimise the total completion time. To reflect the focused batch-scheduling problems more appropriately in real-life production systems, two different resource-allocation policies were introduced and the g-conv resource function, which has been proven to be more implementable than previous resource functions in the literature, was employed. The authors proposed a novel concept of resource sensitivity according to the unique characteristic of the problems, in order to illustrate the differences in the time-compression effects among various jobs. Through the analysis of the properties for optimal solutions, the authors deduced the construction of the equivalent relations among these effects and consequently developed two approximately optimal algorithms, which have reasonably great accuracies. For performance comparison, other state-of-the-art algorithms were introduced, which are extensively applied for solving continuous optimisation problems. The results showed that the proposed algorithms outperform the compared approaches with respect to both solution quality and stability.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics

Keywords: approximately optimal algorithms; batch scheduling; general convex function; resource sensitivity

Document Type: Research Article

Affiliations: School of Computer Science and Technology, University of Science and Technology of China, Hefei, 230026, P.R. China

Publication date: September 2, 2015

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more