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Critical chain project buffer sizing based on resource constraints

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  • Junguang Zhang
  • Xiwei Song
  • Estrella Díaz

Abstract

Project scheduling is a complex process involving many types of resources and activities that require optimisation. The resource-constrained project scheduling problem is one of the well-known problematic issues when project activities have to be scheduled to minimise the project duration. Consequently, several methods have been proposed for adjusting the buffer size but none of these traditional methods consider buffer sizing accuracy based on resource constraints. The purpose of this paper is to develop a buffer sizing method based on a fuzzy resource-constrained project scheduling problem in order to obtain an appropriate proportionality between the activity duration and the buffer size. Specifically, a comprehensive resource-constrained method that considers both the general average resource constraints (GARC) and the highest peak of resource constraints (HPRC) is proposed in order to obtain a new buffer sizing method. This paper contributes to the research by considering several different aspects. First, this paper adopts a fuzzy method to calculate and obtain the threshold amount. Second, this paper discusses the resource levelling problem and proposes the HPRC method. Third, the proposed method uses a fuzzy quantitative model to calculate the resource requirement. The findings indicate that the project achieved higher efficiency, providing effective protection and an appropriate buffer size.

Suggested Citation

  • Junguang Zhang & Xiwei Song & Estrella Díaz, 2017. "Critical chain project buffer sizing based on resource constraints," International Journal of Production Research, Taylor & Francis Journals, vol. 55(3), pages 671-683, February.
    • Handle: RePEc:taf:tprsxx:v:55:y:2017:i:3:p:671-683
    DOI: 10.1080/00207543.2016.1200151
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    References listed on IDEAS

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    1. Junguang Zhang & Xiwei Song & Hongyu Chen & Ruixia (Sandy) Shi, 2015. "Optimisation of critical chain sequencing based on activities’ information flow interactions," International Journal of Production Research, Taylor & Francis Journals, vol. 53(20), pages 6231-6241, October.
    2. Jovanović, Predrag & Kecman, Pavle & Bojović, Nebojša & Mandić, Dragomir, 2017. "Optimal allocation of buffer times to increase train schedule robustness," European Journal of Operational Research, Elsevier, vol. 256(1), pages 44-54.
    3. Cui, Nanfang & Demeulemeester, Erik & Bie, Li, 2016. "Incorporation of activity sensitivity measures into buffer management to manage project schedule riskAuthor-Name: Hu, Xuejun," European Journal of Operational Research, Elsevier, vol. 249(2), pages 717-727.
    4. Tukel, Oya I. & Rom, Walter O. & Eksioglu, Sandra Duni, 2006. "An investigation of buffer sizing techniques in critical chain scheduling," European Journal of Operational Research, Elsevier, vol. 172(2), pages 401-416, July.
    5. Kolb, Oliver & Göttlich, Simone, 2015. "A continuous buffer allocation model using stochastic processes," European Journal of Operational Research, Elsevier, vol. 242(3), pages 865-874.
    6. Zhang, Junguang & Song, Xiwei & Díaz, Estrella, 2016. "Project buffer sizing of a critical chain based on comprehensive resource tightness," European Journal of Operational Research, Elsevier, vol. 248(1), pages 174-182.
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    Cited by:

    1. She, Bingling & Chen, Bo & Hall, Nicholas G., 2021. "Buffer sizing in critical chain project management by network decomposition," Omega, Elsevier, vol. 102(C).
    2. Yan Zhao & Nanfang Cui & Wendi Tian, 2020. "A two-stage approach for the critical chain project rescheduling," Annals of Operations Research, Springer, vol. 285(1), pages 67-95, February.
    3. Guofeng Ma & Jianyao Jia & Tiancheng Zhu & Shan Jiang, 2019. "A Critical Design Structure Method for Project Schedule Development under Rework Risks," Sustainability, MDPI, vol. 11(24), pages 1-20, December.

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