Resource leveling using normalized entropy and relative entropy

Resource leveling using normalized entropy and relative entropy The resource leveling problem (RLP) involves the development of a project schedule that specifies the starting time of each activity constrained by the project deadline and precedence, aimed at the minimizing the variation of the resource utilization. To shorten project duration and to find more effective and flexible object functions, normalized entropy and relative entropy are proposed. The former considers shortening the project duration, improved based on the entropy model; the latter measures the similarity of two distributions, that is the actual versus the theoretical resource allocation based on the resource assignments per activity, and which covers not only entropy itself, but also cross entropy. Finally, discrete particle swarm optimization (DPSO) is encoded to investigate the performance of the proposed normalized entropy and relative entropy with other objective functions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Automation in Construction Elsevier

Resource leveling using normalized entropy and relative entropy

Loading next page...
 
/lp/elsevier/resource-leveling-using-normalized-entropy-and-relative-entropy-5UG0g7aWCq
Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier B.V.
ISSN
0926-5805
D.O.I.
10.1016/j.autcon.2017.12.022
Publisher site
See Article on Publisher Site

Abstract

The resource leveling problem (RLP) involves the development of a project schedule that specifies the starting time of each activity constrained by the project deadline and precedence, aimed at the minimizing the variation of the resource utilization. To shorten project duration and to find more effective and flexible object functions, normalized entropy and relative entropy are proposed. The former considers shortening the project duration, improved based on the entropy model; the latter measures the similarity of two distributions, that is the actual versus the theoretical resource allocation based on the resource assignments per activity, and which covers not only entropy itself, but also cross entropy. Finally, discrete particle swarm optimization (DPSO) is encoded to investigate the performance of the proposed normalized entropy and relative entropy with other objective functions.

Journal

Automation in ConstructionElsevier

Published: Mar 1, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial