IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v281y2020i1p100-113.html
   My bibliography  Save this article

An alternative efficient representation for the project portfolio selection problem

Author

Listed:
  • Li, Xingmei
  • Huang, Yao-Huei
  • Fang, Shu-Cherng
  • Zhang, Youzhong

Abstract

Project portfolio selection problem (PPSP) is usually formulated as a mixed integer polynomial program with cross-product terms. The problem is hard to solve due to the non-convex cross-product terms involved. To find an exact optimal solution, currently available methods adopt different linearization techniques to handle the cross-product terms and then utilize a branch-and-bound scheme for computations. This study proposes an alternative efficient representation for PPSP using fewer continuous variables than the current methods to achieve global optimum. Numerical experiments are presented to demonstrate the effectiveness and efficiency of the proposed method. In addition, the proposed method is integrated with a general binary cut scheme for identifying all alternative solutions for decision makers to consider better options.

Suggested Citation

  • Li, Xingmei & Huang, Yao-Huei & Fang, Shu-Cherng & Zhang, Youzhong, 2020. "An alternative efficient representation for the project portfolio selection problem," European Journal of Operational Research, Elsevier, vol. 281(1), pages 100-113.
    • Handle: RePEc:eee:ejores:v:281:y:2020:i:1:p:100-113
    DOI: 10.1016/j.ejor.2019.08.022
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221719306757
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2019.08.022?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. Mavrotas, George & Makryvelios, Evangelos, 2021. "Combining multiple criteria analysis, mathematical programming and Monte Carlo simulation to tackle uncertainty in Research and Development project portfolio selection: A case study from Greece," European Journal of Operational Research, Elsevier, vol. 291(2), pages 794-806.
    2. Liesiö, Juuso & Kee, Taeyoung & Malo, Pekka, 2024. "Modeling project interactions in multiattribute portfolio decision analysis: Axiomatic foundations and practical implications," European Journal of Operational Research, Elsevier, vol. 316(3), pages 988-1000.
    3. Tian, Yuanyuan & Bai, Libiao & Wei, Lan & Zheng, Kanyin & Zhou, Xinyu, 2022. "Modeling for project portfolio benefit prediction via a GA-BP neural network," Technological Forecasting and Social Change, Elsevier, vol. 183(C).

    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:281:y:2020:i:1:p:100-113. 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.