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An alternative efficient representation for the project portfolio selection problem

Author

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  • 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
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    Cited by:

    1. 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).
    2. 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.
    3. 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.

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