IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v259y2017i3p1097-1111.html
   My bibliography  Save this article

A Nearest Neighbour extension to project duration forecasting with Artificial Intelligence

Author

Listed:
  • Wauters, Mathieu
  • Vanhoucke, Mario

Abstract

In this paper, we provide a Nearest Neighbour based extension for project control forecasting with Earned Value Management. The k-Nearest Neighbour method is employed as a predictor and to reduce the size of a training set containing more similar observations. An Artificial Intelligence (AI) method then makes use of the reduced training set to predict the real duration of a project. Additionally, we report on the forecasting stability of the various AI methods and their hybrid Nearest Neighbour counterparts. A large computer experiment is set up to assess the forecasting accuracy and stability of the existing and newly proposed methods. The experiments indicate that the Nearest Neighbour technique yields the best stability results and is able to improve the AI methods when the training set is similar or not equal to the test set. Sensitivity checks vary the amount of historical data and number of neighbours, leading to the conclusion that having more historical data, from which the a relevant subset can be selected by means of the proposed Nearest Neighbour technique, is preferential.

Suggested Citation

  • Wauters, Mathieu & Vanhoucke, Mario, 2017. "A Nearest Neighbour extension to project duration forecasting with Artificial Intelligence," European Journal of Operational Research, Elsevier, vol. 259(3), pages 1097-1111.
    • Handle: RePEc:eee:ejores:v:259:y:2017:i:3:p:1097-1111
    DOI: 10.1016/j.ejor.2016.11.018
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221716309432
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2016.11.018?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. 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.
    2. Colin, Jeroen & Vanhoucke, Mario, 2014. "Setting tolerance limits for statistical project control using earned value management," Omega, Elsevier, vol. 49(C), pages 107-122.
    3. Vanhoucke, Mario, 2010. "Using activity sensitivity and network topology information to monitor project time performance," Omega, Elsevier, vol. 38(5), pages 359-370, October.
    4. M Vanhoucke & S Vandevoorde, 2007. "A simulation and evaluation of earned value metrics to forecast the project duration," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(10), pages 1361-1374, October.
    5. Ravi Kumar, P. & Ravi, V., 2007. "Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review," European Journal of Operational Research, Elsevier, vol. 180(1), pages 1-28, July.
    6. Vanhoucke, Mario & Coelho, Jose & Debels, Dieter & Maenhout, Broos & Tavares, Luis V., 2008. "An evaluation of the adequacy of project network generators with systematically sampled networks," European Journal of Operational Research, Elsevier, vol. 187(2), pages 511-524, June.
    7. Nikolopoulos, K. & Goodwin, P. & Patelis, A. & Assimakopoulos, V., 2007. "Forecasting with cue information: A comparison of multiple regression with alternative forecasting approaches," European Journal of Operational Research, Elsevier, vol. 180(1), pages 354-368, July.
    8. Raz, Tzvi & Erel, Erdal, 2000. "Optimal timing of project control points," European Journal of Operational Research, Elsevier, vol. 127(2), pages 252-261, December.
    9. 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.
    10. James E. Kelley, 1961. "Critical-Path Planning and Scheduling: Mathematical Basis," Operations Research, INFORMS, vol. 9(3), pages 296-320, June.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ariyaluran Habeeb, Riyaz Ahamed & Nasaruddin, Fariza & Gani, Abdullah & Targio Hashem, Ibrahim Abaker & Ahmed, Ejaz & Imran, Muhammad, 2019. "Real-time big data processing for anomaly detection: A Survey," International Journal of Information Management, Elsevier, vol. 45(C), pages 289-307.
    2. Mohammad Hossein Haghighi & Maryam Ashrafi, 2024. "A novel framework for risk management of software projects by integrating a new COPRAS method under cloud model and machine learning algorithms," Annals of Operations Research, Springer, vol. 338(1), pages 675-708, July.
    3. Ünsal-Altuncan, Izel & Vanhoucke, Mario, 2024. "A hybrid forecasting model to predict the duration and cost performance of projects with Bayesian Networks," European Journal of Operational Research, Elsevier, vol. 315(2), pages 511-527.
    4. Schäfers, A. & Bougioukos, V. & Karamatzanis, G. & Nikolopoulos, K., 2024. "Prediction-led prescription: Optimal Decision-Making in times of turbulence and business performance improvement," Journal of Business Research, Elsevier, vol. 182(C).
    5. Servranckx, Tom & Vanhoucke, Mario & Aouam, Tarik, 2021. "Practical application of reference class forecasting for cost and time estimations: Identifying the properties of similarity," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1161-1179.
    6. Thordur Vikingur Fridgeirsson & Helgi Thor Ingason & Haukur Ingi Jonasson & Hildur Jonsdottir, 2021. "An Authoritative Study on the Near Future Effect of Artificial Intelligence on Project Management Knowledge Areas," Sustainability, MDPI, vol. 13(4), pages 1-20, February.

    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, 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.
    2. 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.
    3. 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.
    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. Colin, Jeroen & Vanhoucke, Mario, 2014. "Setting tolerance limits for statistical project control using earned value management," Omega, Elsevier, vol. 49(C), pages 107-122.
    6. 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.
    7. Ünsal-Altuncan, Izel & Vanhoucke, Mario, 2024. "A hybrid forecasting model to predict the duration and cost performance of projects with Bayesian Networks," European Journal of Operational Research, Elsevier, vol. 315(2), pages 511-527.
    8. Ö Hazır & A Shtub, 2011. "Effects of the information presentation format on project control," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(12), pages 2157-2161, December.
    9. Plaza, Malgorzata, 2016. "Balancing the costs of human resources on an ERP project," Omega, Elsevier, vol. 59(PB), pages 171-183.
    10. 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.
    11. She, Bingling & Chen, Bo & Hall, Nicholas G., 2021. "Buffer sizing in critical chain project management by network decomposition," Omega, Elsevier, vol. 102(C).
    12. Servranckx, Tom & Vanhoucke, Mario & Aouam, Tarik, 2021. "Practical application of reference class forecasting for cost and time estimations: Identifying the properties of similarity," European Journal of Operational Research, Elsevier, vol. 295(3), pages 1161-1179.
    13. 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.
    14. 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.
    15. Richard E. Wendell & Timothy J. Lowe & Mike M. Gordon, 2023. "Dangers in using earned duration and other earned value metrics to measure a project’s schedule performance," 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. 31(2), pages 665-680, June.
    16. Litsiou, Konstantia & Polychronakis, Yiannis & Karami, Azhdar & Nikolopoulos, Konstantinos, 2022. "Relative performance of judgmental methods for forecasting the success of megaprojects," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1185-1196.
    17. Vanhoucke, Mario & Colin, Jeroen, 2016. "On the use of multivariate regression methods for longest path calculations from earned value management observations," Omega, Elsevier, vol. 61(C), pages 127-140.
    18. Womer, K. & Li, H. & Camm, J. & Osterman, C. & Radhakrishnan, R., 2017. "Learning and Bayesian updating in long cycle made-to-order (MTO) production," Omega, Elsevier, vol. 69(C), pages 29-42.
    19. 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.
    20. 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.

    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:259:y:2017:i:3:p:1097-1111. 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.