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Disruption management for resource-constrained project scheduling

Author

Listed:
  • G Zhu

    (The University of Texas)

  • J F Bard

    (The University of Texas)

  • G Yu

    (The University of Texas)

Abstract

In this paper, we study the problem of how to react when an ongoing project is disrupted. The focus is on the resource-constrained project scheduling problem with finish–start precedence constraints. We begin by proposing a classification scheme for the different types of disruptions and then define the constraints and objectives that comprise what we call the recovery problem. The goal is to get back on track as soon as possible at minimum cost, where cost is now a function of the deviation from the original schedule. The problem is formulated as an integer linear program and solved with a hybrid mixed-inter programming/constraint programming procedure that exploits a number of special features in the constraints. The new model is significantly different from the original one due to the fact that a different set of feasibility conditions and performance requirements must be considered during the recovery process. The complexity of several special cases is analysed. To test the hybrid procedure, 554 20-activity instances were solved and the results compared with those obtained with CPLEX. Computational experiments were also conducted to determine the effects of different factors related to the recovery process.

Suggested Citation

  • G Zhu & J F Bard & G Yu, 2005. "Disruption management for resource-constrained project scheduling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 56(4), pages 365-381, April.
    • Handle: RePEc:pal:jorsoc:v:56:y:2005:i:4:d:10.1057_palgrave.jors.2601860
    DOI: 10.1057/palgrave.jors.2601860
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    References listed on IDEAS

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    Cited by:

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    2. Hartmann, Sönke & Briskorn, Dirk, 2008. "A survey of variants and extensions of the resource-constrained project scheduling problem," Working Paper Series 02/2008, Hamburg School of Business Administration (HSBA).
    3. Öncü Hazir & Gündüz Ulusoy, 2020. "A classification and review of approaches and methods for modeling uncertainty in projects," Post-Print hal-02898162, HAL.
    4. Q Mu & Z Fu & J Lysgaard & R Eglese, 2011. "Disruption management of the vehicle routing problem with vehicle breakdown," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(4), pages 742-749, April.
    5. Deblaere, Filip & Demeulemeester, Erik & Herroelen, Willy, 2011. "Proactive policies for the stochastic resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 214(2), pages 308-316, October.
    6. Iyer K. Chandrashekhar & Banerjee Partha S., 2016. "Project ambidexterity: case of recovering schedule delay in a brownfield airport project in India," Organization, Technology and Management in Construction, Sciendo, vol. 8(1), pages 1464-1481, December.
    7. Yangyang Liang & Nanfang Cui & Tian Wang & Erik Demeulemeester, 2019. "Robust resource-constrained max-NPV project scheduling with stochastic activity duration," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 41(1), pages 219-254, March.
    8. Xuejun Hu & Jianjiang Wang & Kaijun Leng, 2019. "The Interaction Between Critical Chain Sequencing, Buffer Sizing, and Reactive Actions in a CC/BM Framework," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., vol. 36(03), pages 1-22, June.
    9. Hartmann, Sönke & Briskorn, Dirk, 2010. "A survey of variants and extensions of the resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 207(1), pages 1-14, November.
    10. Xuejun Hu & Erik Demeulemeester & Nanfang Cui & Jianjiang Wang & Wendi Tian, 2017. "Improved critical chain buffer management framework considering resource costs and schedule stability," Flexible Services and Manufacturing Journal, Springer, vol. 29(2), pages 159-183, June.
    11. Narjes Sabeghi & Hamed Reza Tareghian, 2020. "Using the generalized maximum covering location model to control a project’s progress," Computational Management Science, Springer, vol. 17(1), pages 1-21, January.
    12. Szwarc Eryk & Wikarek Jarosław, 2020. "Proactive Planning of Project Team Members’ Competences," Foundations of Management, Sciendo, vol. 12(1), pages 71-84, January.
    13. Hazır, Öncü & Ulusoy, Gündüz, 2020. "A classification and review of approaches and methods for modeling uncertainty in projects," International Journal of Production Economics, Elsevier, vol. 223(C).
    14. Jürgen Kuster & Dietmar Jannach & Gerhard Friedrich, 2010. "Applying Local Rescheduling in response to schedule disruptions," Annals of Operations Research, Springer, vol. 180(1), pages 265-282, November.
    15. Martens, Annelies & Vanhoucke, Mario, 2019. "The impact of applying effort to reduce activity variability on the project time and cost performance," European Journal of Operational Research, Elsevier, vol. 277(2), pages 442-453.
    16. Chen Li & Xiangtong Qi & Chung-Yee Lee, 2015. "Disruption Recovery for a Vessel in Liner Shipping," Transportation Science, INFORMS, vol. 49(4), pages 900-921, November.
    17. Gedik, Ridvan & Medal, Hugh & Rainwater, Chase & Pohl, Ed A. & Mason, Scott J., 2014. "Vulnerability assessment and re-routing of freight trains under disruptions: A coal supply chain network application," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 45-57.
    18. Xingmei Li & Yaxian Wang & Qingyou Yan & Xinchao Zhao, 2019. "Uncertain mean-variance model for dynamic project portfolio selection problem with divisibility," Fuzzy Optimization and Decision Making, Springer, vol. 18(1), pages 37-56, March.

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