IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v316y2024i2p503-518.html
   My bibliography  Save this article

Analysis of the impact of corrective actions for stochastic project networks

Author

Listed:
  • Vaseghi, Forough
  • Martens, Annelies
  • Vanhoucke, Mario

Abstract

In project management, a project plan is constructed that assigns a planned start time to each project activity. Based on this plan, the total planned project duration and cost can be determined. However, during project execution, deviations from the plan are inevitable due to uncertainty and variability. When these deviations endanger the timely completion of projects, the project manager should take corrective actions to get the project back on track. In this study, corrective actions are modelled as modifications of the original activity duration distributions (i.e., reduced mean and/or standard deviation) to account for the uncertain nature of their impact. Further, an analytical procedure is developed to rank activities according to their expected impact on the project duration distribution when they are controlled by a corrective action. This activity ranking is used to determine the number of actions that should be taken and to select the set of activities that will be controlled. A computational experiment on a large set of project networks with varying network complexity and network structures has been conducted. These experiments have shown that taking actions on a relatively small subset of activities, rather than on the entire set of project activities, proves more efficient, when the subset of activities is carefully selected. More precisely, the efficiency of the corrective actions process depends on both the number of actions and the activity selection criterion (activity ranking).

Suggested Citation

  • Vaseghi, Forough & Martens, Annelies & Vanhoucke, Mario, 2024. "Analysis of the impact of corrective actions for stochastic project networks," European Journal of Operational Research, Elsevier, vol. 316(2), pages 503-518.
  • Handle: RePEc:eee:ejores:v:316:y:2024:i:2:p:503-518
    DOI: 10.1016/j.ejor.2024.02.040
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221724001735
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ejor.2024.02.040?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Elmaghraby, Salah E., 2000. "On criticality and sensitivity in activity networks," European Journal of Operational Research, Elsevier, vol. 127(2), pages 220-238, December.
    2. Bowman, R. Alan, 2006. "Developing activity duration specification limits for effective project control," European Journal of Operational Research, Elsevier, vol. 174(2), pages 1191-1204, October.
    3. 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.
    4. D. G. Malcolm & J. H. Roseboom & C. E. Clark & W. Fazar, 1959. "Application of a Technique for Research and Development Program Evaluation," Operations Research, INFORMS, vol. 7(5), pages 646-669, October.
    5. Brucker, Peter & Drexl, Andreas & Mohring, Rolf & Neumann, Klaus & Pesch, Erwin, 1999. "Resource-constrained project scheduling: Notation, classification, models, and methods," European Journal of Operational Research, Elsevier, vol. 112(1), pages 3-41, January.
    6. Trietsch, Dan & Mazmanyan, Lilit & Gevorgyan, Lilit & Baker, Kenneth R., 2012. "Modeling activity times by the Parkinson distribution with a lognormal core: Theory and validation," European Journal of Operational Research, Elsevier, vol. 216(2), pages 386-396.
    7. Anthony A. Mastor, 1970. "An Experimental Investigation and Comparative Evaluation of Production Line Balancing Techniques," Management Science, INFORMS, vol. 16(11), pages 728-746, July.
    8. Song, Jie & Martens, Annelies & Vanhoucke, Mario, 2020. "The impact of a limited budget on the corrective action taking process," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1070-1086.
    9. Bajis Dodin, 1985. "Bounding the Project Completion Time Distribution in PERT Networks," Operations Research, INFORMS, vol. 33(4), pages 862-881, August.
    10. De Reyck, Bert & Herroelen, Willy, 1996. "On the use of the complexity index as a measure of complexity in activity networks," European Journal of Operational Research, Elsevier, vol. 91(2), pages 347-366, June.
    11. Genaro Gutierrez & Anand Paul, 2000. "Analysis of the Effects of Uncertainty, Risk-Pooling, and Subcontracting Mechanisms on Project Performance," Operations Research, INFORMS, vol. 48(6), pages 927-938, December.
    12. Elmaghraby, S. E. & Fathi, Y. & Taner, M. R., 1999. "On the sensitivity of project variability to activity mean duration," International Journal of Production Economics, Elsevier, vol. 62(3), pages 219-232, September.
    13. George B. Kleindorfer, 1971. "Bounding Distributions for a Stochastic Acyclic Network," Operations Research, INFORMS, vol. 19(7), pages 1586-1601, December.
    14. V. G. Kulkarni & V. G. Adlakha, 1986. "Markov and Markov-Regenerative pert Networks," Operations Research, INFORMS, vol. 34(5), pages 769-781, October.
    15. Schmidt, Craig W. & Grossmann, Ignacio E., 2000. "The exact overall time distribution of a project with uncertain task durations," European Journal of Operational Research, Elsevier, vol. 126(3), pages 614-636, November.
    16. Vanhoucke, Mario, 2010. "Using activity sensitivity and network topology information to monitor project time performance," Omega, Elsevier, vol. 38(5), pages 359-370, October.
    17. Viswanathan Krishnan & Steven D. Eppinger & Daniel E. Whitney, 1997. "A Model-Based Framework to Overlap Product Development Activities," Management Science, INFORMS, vol. 43(4), pages 437-451, April.
    18. Vanhoucke, Mario, 2011. "On the dynamic use of project performance and schedule risk information during projecttracking," Omega, Elsevier, vol. 39(4), pages 416-426, August.
    19. Kolisch, Rainer & Hartmann, Sonke, 2006. "Experimental investigation of heuristics for resource-constrained project scheduling: An update," European Journal of Operational Research, Elsevier, vol. 174(1), pages 23-37, October.
    20. Madadi, M. & Iranmanesh, H., 2012. "A management oriented approach to reduce a project duration and its risk (variability)," European Journal of Operational Research, Elsevier, vol. 219(3), pages 751-761.
    21. She, Bingling & Chen, Bo & Hall, Nicholas G., 2021. "Buffer sizing in critical chain project management by network decomposition," Omega, Elsevier, vol. 102(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Song, Jie & Martens, Annelies & Vanhoucke, Mario, 2021. "Using Schedule Risk Analysis with resource constraints for project control," European Journal of Operational Research, Elsevier, vol. 288(3), pages 736-752.
    2. 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.
    3. Colin, Jeroen & Vanhoucke, Mario, 2014. "Setting tolerance limits for statistical project control using earned value management," Omega, Elsevier, vol. 49(C), pages 107-122.
    4. Song, Jie & Martens, Annelies & Vanhoucke, Mario, 2022. "Using Earned Value Management and Schedule Risk Analysis with resource constraints for project control," European Journal of Operational Research, Elsevier, vol. 297(2), pages 451-466.
    5. Martens, Annelies & Vanhoucke, Mario, 2017. "A buffer control method for top-down project control," European Journal of Operational Research, Elsevier, vol. 262(1), pages 274-286.
    6. Fernando Acebes & Javier Pajares & José M. González-Varona & Adolfo López-Paredes, 2021. "Project risk management from the bottom-up: Activity Risk Index," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(4), pages 1375-1396, December.
    7. Fernando Acebes & Javier Pajares & Jose M Gonzalez-Varona & Adolfo Lopez-Paredes, 2024. "Project Risk Management from the bottom-up: Activity Risk Index," Papers 2406.00078, arXiv.org.
    8. Messelis, Tommy & De Causmaecker, Patrick, 2014. "An automatic algorithm selection approach for the multi-mode resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 233(3), pages 511-528.
    9. 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.
    10. Xiong, Jian & Leus, Roel & Yang, Zhenyu & Abbass, Hussein A., 2016. "Evolutionary multi-objective resource allocation and scheduling in the Chinese navigation satellite system project," European Journal of Operational Research, Elsevier, vol. 251(2), pages 662-675.
    11. Vanhoucke, Mario, 2010. "Using activity sensitivity and network topology information to monitor project time performance," Omega, Elsevier, vol. 38(5), pages 359-370, October.
    12. Song, Jie & Martens, Annelies & Vanhoucke, Mario, 2020. "The impact of a limited budget on the corrective action taking process," European Journal of Operational Research, Elsevier, vol. 286(3), pages 1070-1086.
    13. Vanhoucke, Mario, 2011. "On the dynamic use of project performance and schedule risk information during projecttracking," Omega, Elsevier, vol. 39(4), pages 416-426, August.
    14. Madadi, M. & Iranmanesh, H., 2012. "A management oriented approach to reduce a project duration and its risk (variability)," European Journal of Operational Research, Elsevier, vol. 219(3), pages 751-761.
    15. Davaadorjin Monhor, 2011. "A new probabilistic approach to the path criticality in stochastic PERT," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 19(4), pages 615-633, December.
    16. Azaron, Amir & Fatemi Ghomi, S.M.T., 2008. "Lower bound for the mean project completion time in dynamic PERT networks," European Journal of Operational Research, Elsevier, vol. 186(1), pages 120-127, April.
    17. Junguang Zhang & Dan Wan, 2021. "Determination of early warning time window for bottleneck resource buffer," Annals of Operations Research, Springer, vol. 300(1), pages 289-305, May.
    18. Yang-Kuei Lin & Chin Soon Chong, 2017. "Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1189-1201, June.
    19. V. Van Peteghem & M. Vanhoucke, 2009. "Using Resource Scarceness Characteristics to Solve the Multi-Mode Resource-Constrained Project Scheduling Problem," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 09/595, Ghent University, Faculty of Economics and Business Administration.
    20. Alessio Angius & András Horváth & Marcello Urgo, 2021. "A Kronecker Algebra Formulation for Markov Activity Networks with Phase-Type Distributions," Mathematics, MDPI, vol. 9(12), pages 1-22, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:316:y:2024:i:2:p:503-518. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.