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A Robust Human–Machine Framework for Project Portfolio Selection

Author

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  • Hang Chen

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Nannan Zhang

    (Finance and Economics Pearl River College, Tianjin University, Tianjin 300345, China)

  • Yajie Dou

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

  • Yulong Dai

    (College of Systems Engineering, National University of Defense Technology, Changsha 410073, China)

Abstract

Based on the project portfolio selection and scheduling problem (PPSS), the development of a systematic and scientific project scheduling plan necessitates comprehensive consideration of individual preferences and multiple realistic constraints, rendering it an NP-hard problem. Simultaneously, accurately and swiftly evaluating the value of projects as a complex entity poses a challenging issue that requires urgent attention. This paper introduces a novel qualitative evaluation-based project value assessment process that significantly reduces the cost and complexity of project value assessment, upon which a preference-based deep reinforcement learning method is presented for computing and solving project subsets and time scheduling plans. This paper first determines the key parameter values of the algorithm through specific examples. Then, using the method of controlling variables, it explores the sensitivity of the algorithm to changes in problem size and dimensionality. Finally, the proposed algorithm is compared with two classical algorithms and two heuristic algorithms across different instances. The experimental results demonstrate that the proposed algorithm exhibits higher effectiveness and accuracy.

Suggested Citation

  • Hang Chen & Nannan Zhang & Yajie Dou & Yulong Dai, 2024. "A Robust Human–Machine Framework for Project Portfolio Selection," Mathematics, MDPI, vol. 12(19), pages 1-18, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:19:p:3025-:d:1487749
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    References listed on IDEAS

    as
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