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Optimizing non-unit repetitive project resource and scheduling by evolutionary algorithms

Author

Listed:
  • Duc-Hoc Tran

    (Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh, City (VNU-HCM))

  • Jui-Sheng Chou

    (National Taiwan University of Science and Technology)

  • Duc-Long Luong

    (Ho Chi Minh City University of Technology, Vietnam National University Ho Chi Minh, City (VNU-HCM))

Abstract

Repetitive project scheduling is a frequently encountered and challenging task in project planning. Researchers have developed numerous methods for the scheduling and planning of repetitive construction projects. However, almost all current repetitive scheduling methods are based on identical production units or they neglect the priorities of activities. This work presents a new hybrid evolutionary approach, called the fuzzy clustering artificial bee colony approach (FABC), to optimize resource assignment and scheduling for non-unit repetitive projects (NRP). In FABC, the fuzzy c-means clustering technique applies several multi-parent crossover operators to utilize population information efficiently and to improve convergence efficiency. The scheduling subsystem considers the following: (1) the logical relationships among activities throughout the project; (2) the assignment of multiple resources; and (3) the priorities of activities in groups to calculate project duration. Two numerical case studies are analyzed to demonstrate the use of the FABC-NRP model and its ability to optimize the scheduling of non-unit repetitive construction projects. Experimental results indicate that the proposed method yields the shortest project duration on average and deviation of optimal solution among benchmark algorithms considered herein and those considered previously. The outcomes will help project managers to prepare better schedules of repetitive projects.

Suggested Citation

  • Duc-Hoc Tran & Jui-Sheng Chou & Duc-Long Luong, 2022. "Optimizing non-unit repetitive project resource and scheduling by evolutionary algorithms," Operational Research, Springer, vol. 22(1), pages 77-103, March.
    • Handle: RePEc:spr:operea:v:22:y:2022:i:1:d:10.1007_s12351-019-00544-7
    DOI: 10.1007/s12351-019-00544-7
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    References listed on IDEAS

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    1. Wang, Yong-Jun & Zhang, Jiang-She & Zhang, Gai-Ying, 2007. "A dynamic clustering based differential evolution algorithm for global optimization," European Journal of Operational Research, Elsevier, vol. 183(1), pages 56-73, November.
    2. Wenping Zou & Yunlong Zhu & Hanning Chen & Xin Sui, 2010. "A Clustering Approach Using Cooperative Artificial Bee Colony Algorithm," Discrete Dynamics in Nature and Society, Hindawi, vol. 2010, pages 1-16, November.
    3. Sachchida Nand Chaurasia & Shyam Sundar & Alok Singh, 2017. "Hybrid metaheuristic approaches for the single machine total stepwise tardiness problem with release dates," Operational Research, Springer, vol. 17(1), pages 275-295, April.
    4. Khaled El-Rayes & Osama Moselhi, 1998. "Resource-driven scheduling of repetitive activities," Construction Management and Economics, Taylor & Francis Journals, vol. 16(4), pages 433-446.
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