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

Published:10 August 2021Publication 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.Google ScholarGoogle Scholar
  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/su11195542Google ScholarGoogle Scholar
  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/10.1080/014461998372349Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle Scholar
  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)Google ScholarGoogle ScholarCross RefCross Ref
  6. Tarek Hegazy. 1999. Optimization of resource allocation and leveling using genetic algorithms. Journal of construction engineering and management, 125(3), 167-175.Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle Scholar
  14. Tarek Hegazy. 2019. Computer-Based Construction Project Management. Grants Regist. 2020, no. January 2002, pp. 1065–1065.Google ScholarGoogle Scholar
  15. Singiresu S. Rao. 2019. Engineering optimization: theory and practice. John Wiley & Sons.Google ScholarGoogle Scholar
  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.Google ScholarGoogle Scholar
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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.Google ScholarGoogle ScholarCross RefCross Ref
  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/su13063172Google ScholarGoogle ScholarCross RefCross Ref

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  • Published in

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    ICIIT '21: Proceedings of the 2021 6th International Conference on Intelligent Information Technology
    February 2021
    106 pages
    ISBN:9781450388948
    DOI:10.1145/3460179

    Copyright © 2021 ACM

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    • Published: 10 August 2021

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