skip to main content
10.1145/3460179.3460183acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiciitConference Proceedingsconference-collections
research-article

Resource Utilization Optimization using Genetic Algorithm based on Variation of Resource Fluctuation Moment for Extra-Large Building Renovation

Published: 10 August 2021 Publication History

Abstract

This paper compared optimizing resource utilization using Genetic Algorithm (GA) based on variation of resource fluctuation moment (Mx) for extra-large building renovation. The Mx variable, as determined by resource demand on day squared, has a large effect on the result of multi-objective optimization. In this research, an Mx-based optimization model targeting five variables for resource utilization was proposed. In addition, the proposed method is flexible in that the construction planners could specify predecessors or preferences for optional construction sequences so that a more efficient optimal scheduling may be obtained. Three activation functions for Mx were considered, namely Mx, and Mx /1000. In this work, the models in consideration were applied to real data from the university main library building renovation projects which consisted of 251 activities. The contractor's work plan was used as the initial scheduling for the optimization process. When comparing the experimental results from all 3 models, it can be seen that the form and Mx/1000 are more suitable in optimizing resource utilization through GA method in extra-large building renovation.

References

[1]
Sergio Kemmer. 2018. Development of a Method for Construction Management in Refurbishment Projects, Technol. Forecast. Soc. Chang., vol. 104, no. April, pp. 1–15.
[2]
Fang-Jye Shiue,Meng-Cong Zheng, Hsin-Yun Lee, Akhmad Khitam and Pei-Ying Li. 2019. Renovation Construction Process Scheduling for Long-Term Performance of Buildings: An Application Case of University Campus. Sustainability, 11(19), 5542. https://doi.org/10.3390/su11195542
[3]
Charles O. Egbu, Barbara A. Young & Victor B. Torrance (1998) Planning and control processes and techniques for refurbishment management, Construction Management and Economics, 16:3, 315-325, https://doi.org/10.1080/014461998372349
[4]
Ismail Rahmat, Victor B. Torrance and Barbara A. Young. 1998. The planning and control process of refurbishment projects. In: Hughes, W (Ed.), Proceedings 14th Annual ARCOM Conference, 9-11 September 1998, Reading, UK. Association of Researchers in Construction Management, Vol. 1, 137–45.
[5]
Weng-Tat Chan, David K. H. Chua, and Govindan Kannan. 1996. Construction resource scheduling with genetic algorithms, Journal of Construction Engineering and Management, vol. 122, no. 2, pp. 125–132. https://doi.org/10.1061/(ASCE)0733-9364(1996)122:2(125)
[6]
Tarek Hegazy. 1999. Optimization of resource allocation and leveling using genetic algorithms. Journal of construction engineering and management, 125(3), 167-175.
[7]
Yan Liu, Sheng-li Zhao, Xi-kai Du, and Shu-quan Li. 2005. Optimization of resource allocation in construction using genetic algorithms. In 2005 International Conference on Machine Learning and Cybernetics (Vol. 6, pp. 3428-3432). IEEE.
[8]
Khaled El-Rayes and Dho Heon Jun. 2009. Optimizing resource leveling in construction projects. Journal of Construction Engineering and Management, 135(11), 1172-1180.
[9]
Dho Heon Jun, and Khaled El-Rayes. 2011. Multiobjective optimization of resource leveling and allocation during construction scheduling. Journal of construction engineering and management, 137(12), 1080-1088.
[10]
Aekanan Intarasap and Vacharaphoom Benjaoran. 2013. Resource Leveling Model by Reviewing Relationship Options Case Study of Orthopaedic Building Renovation Project, Ramathibodi Hospital,” UBU Eng. J., vol. 2, no. July, pp. 35–45.
[11]
Parviz Ghoddousi, Ehsan Eshtehardian, Shirin Jooybanpour, and Ashtad Javanmardi. (2013). Multi-mode resource-constrained discrete time–cost-resource optimization in project scheduling using non-dominated sorting genetic algorithm. Automation in construction, 30, 216-227.
[12]
Christos Kyriklidis and Georgios Dounias. 2014. Application of evolutionary algorithms in project management. In IFIP International Conference on Artificial Intelligence Applications and Innovations (pp. 335-343). Springer, Berlin, Heidelberg.
[13]
Eknarin Sriprasert and Nashwan Dawood. 2003. Genetic algorithms for multi-constraint scheduling: An application for the construction industry. In 20th International Conference on Construction IT: Construction IT Bridging the Distance (pp. 341-353). International Council for Research and Innovation in Building and Construction.
[14]
Tarek Hegazy. 2019. Computer-Based Construction Project Management. Grants Regist. 2020, no. January 2002, pp. 1065–1065.
[15]
Singiresu S. Rao. 2019. Engineering optimization: theory and practice. John Wiley & Sons.
[16]
Jian-wen Huang, Xing-xia Wang and Rui Chen. 2010. Genetic Algorithms for Optimization of Resource Allocation in Large Scale Construction Project Management. JCP, 5(12), 1916-1924.
[17]
Hyounseok Moon, Hyeonseung Kim, Vineet R. Kamat, and Leenseok Kang. 2015. BIM-based construction scheduling method using optimization theory for reducing activity overlaps. Journal of Computing in Civil Engineering, 29(3), 04014048.
[18]
Chung-Wei Feng, Liang Liu, and Scott A. Burns. 2000. Stochastic construction time-cost trade-off analysis. Journal of Computing in Civil Engineering, 14(2), 117-126.
[19]
Jin-Lee Kim and Ralph D. Ellis Jr. 2008. Permutation-based elitist genetic algorithm for optimization of large-sized resource-constrained project scheduling. Journal of construction engineering and management, 134(11), 904-913.
[20]
Suchat Tachaudomdach, Auttawit Upayokin, Nopadon Kronprasert, Kriangkrai Arunotayanun. 2021. Quantifying Road-Network Robustness toward Flood-Resilient Transportation Systems. Sustainability. 13(6):3172. https://doi.org/10.3390/su13063172

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
ICIIT '21: Proceedings of the 2021 6th International Conference on Intelligent Information Technology
February 2021
106 pages
ISBN:9781450388948
DOI:10.1145/3460179
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 10 August 2021

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. building information modeling
  2. construction renovation
  3. construction scheduling
  4. genetic algorithm

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

ICIIT '21

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 50
    Total Downloads
  • Downloads (Last 12 months)8
  • Downloads (Last 6 weeks)0
Reflects downloads up to 10 Feb 2025

Other Metrics

Citations

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

HTML Format

View this article in HTML Format.

HTML Format

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media