IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v270y2018i3p957-967.html
   My bibliography  Save this article

Efficient priority rules for the stochastic resource-constrained project scheduling problem

Author

Listed:
  • Chen, Zhi
  • Demeulemeester, Erik
  • Bai, Sijun
  • Guo, Yuntao

Abstract

In this study we examine the performance of 17 priority rule heuristics and the justification technique on the stochastic resource-constrained project scheduling problem (SRCPSP). Among the 17 priority rules, 12 are selected from the literature that is addressing the deterministic resource-constrained project scheduling problem (RCPSP), and the other 5 are newly designed, based on stochastic information of the SRCPSP. We evaluate the efficiency of the 17 priority rules on the benchmark data set PSPLIB, and analyze the impact of the project characteristics that were used to create this data set. Our computational results on large size instances show that the best priority rule for the RCPSP does not perform best for the SRCPSP. The best priority rule for the SRCPSP performs as well as the best meta-heuristic when the variance of the activity duration is medium, and outperforms all existing algorithms when this variance is high. The validity of justification on the SRCPSP depends on the priority rule and the activity duration variance. The project characteristics network complexity and resource factor do not influence the choice of the best priority rule, but resource strength does. Our research results can aid managers to schedule project activities more efficiently when facing uncertainties.

Suggested Citation

  • Chen, Zhi & Demeulemeester, Erik & Bai, Sijun & Guo, Yuntao, 2018. "Efficient priority rules for the stochastic resource-constrained project scheduling problem," European Journal of Operational Research, Elsevier, vol. 270(3), pages 957-967.
    • Handle: RePEc:eee:ejores:v:270:y:2018:i:3:p:957-967
    DOI: 10.1016/j.ejor.2018.04.025
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037722171830328X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2018.04.025?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.

    Citations

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


    Cited by:

    1. Annear, Luis Mauricio & Akhavan-Tabatabaei, Raha & Schmid, Verena, 2023. "Dynamic assignment of a multi-skilled workforce in job shops: An approximate dynamic programming approach," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1109-1125.
    2. Pejman Peykani & Jafar Gheidar-Kheljani & Sheida Shahabadi & Seyyed Hassan Ghodsypour & Mojtaba Nouri, 2023. "A two-phase resource-constrained project scheduling approach for design and development of complex product systems," Operational Research, Springer, vol. 23(1), pages 1-25, March.
    3. Bosse, Alexander & Ulmer, Marlin W. & Manni, Emanuele & Mattfeld, Dirk C., 2023. "Dynamic priority rules for combining on-demand passenger transportation and transportation of goods," European Journal of Operational Research, Elsevier, vol. 309(1), pages 399-408.
    4. Osman Hürol Türkakın & David Arditi & Ekrem Manisalı, 2021. "Comparison of Heuristic Priority Rules in the Solution of the Resource-Constrained Project Scheduling Problem," Sustainability, MDPI, vol. 13(17), pages 1-17, September.
    5. 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).
    6. Öncü Hazir & Gündüz Ulusoy, 2020. "A classification and review of approaches and methods for modeling uncertainty in projects," Post-Print hal-02898162, HAL.
    7. 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.
    8. Zarghami, Seyed Ashkan & Dumrak, Jantanee, 2021. "Aleatory uncertainty quantification of project resources and its application to project scheduling," Reliability Engineering and System Safety, Elsevier, vol. 211(C).
    9. Masoud Arjmand & Amir Abbas Najafi & Majid Ebrahimzadeh, 2020. "Evolutionary algorithms for multi-objective stochastic resource availability cost problem," OPSEARCH, Springer;Operational Research Society of India, vol. 57(3), pages 935-985, September.

    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:270:y:2018:i:3:p:957-967. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.