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Genetic algorithm-based multi-criteria project portfolio selection

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  • Lean Yu
  • Shouyang Wang
  • Fenghua Wen
  • Kin Lai

Abstract

Project portfolio selection is one of the most important decision-making problems for most organizations in project management and engineering management. Usually project portfolio decisions are very complicated when project interactions in terms of multiple selection criteria and preference information of decision makers (DMs) in terms of the criteria importance are taken into consideration simultaneously. In order to solve this complex decision-making problem, a multi-criteria project portfolio selection problem considering project interactions in terms of multiple selection criteria and DMs’ preferences is first formulated. Then a genetic algorithm (GA)-based nonlinear integer programming (NIP) approach is used to solve the multi-criteria project portfolio selection problem. Finally, two illustrative examples are presented for demonstration and verification purposes. Experimental results obtained indicate that the GA-based NIP approach can be used as a feasible and effective solution to multi-criteria project portfolio selection problems. Copyright Springer Science+Business Media, LLC 2012

Suggested Citation

  • Lean Yu & Shouyang Wang & Fenghua Wen & Kin Lai, 2012. "Genetic algorithm-based multi-criteria project portfolio selection," Annals of Operations Research, Springer, vol. 197(1), pages 71-86, August.
    • Handle: RePEc:spr:annopr:v:197:y:2012:i:1:p:71-86:10.1007/s10479-010-0819-6
    DOI: 10.1007/s10479-010-0819-6
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    4. Maciej Nowak & Tadeusz Trzaskalik, 2022. "A trade-off multiobjective dynamic programming procedure and its application to project portfolio selection," Annals of Operations Research, Springer, vol. 311(2), pages 1155-1181, April.
    5. Hassanzadeh, Farhad & Nemati, Hamid & Sun, Minghe, 2014. "Robust optimization for interactive multiobjective programming with imprecise information applied to R&D project portfolio selection," European Journal of Operational Research, Elsevier, vol. 238(1), pages 41-53.
    6. Xiang Li & Hui Jiang & Sini Guo & Wai-ki Ching & Lean Yu, 2020. "On product of positive L-R fuzzy numbers and its application to multi-period portfolio selection problems," Fuzzy Optimization and Decision Making, Springer, vol. 19(1), pages 53-79, March.
    7. Vijaya Dixit & Manoj Kumar Tiwari, 2020. "Project portfolio selection and scheduling optimization based on risk measure: a conditional value at risk approach," Annals of Operations Research, Springer, vol. 285(1), pages 9-33, February.
    8. Lev Solomonovich Mazelis* & Konstantin Sergeevich Solodukhin & Aleksandr Dmitrievich Tarantaev, 2018. "Fuzzy Optimization Models for Project Portfolio Rolling Planning Taking into Account Risk and Stakeholder Interests," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 201-210:3.
    9. Wu, Yunna & Xu, Chuanbo & Ke, Yiming & Li, Xinying & Li, Lingwenying, 2019. "Portfolio selection of distributed energy generation projects considering uncertainty and project interaction under different enterprise strategic scenarios," Applied Energy, Elsevier, vol. 236(C), pages 444-464.
    10. Aidin Delgoshaei & Timon Rabczuk & Ahad Ali & Mohd Khairol Anuar Ariffin, 2017. "An applicable method for modifying over-allocated multi-mode resource constraint schedules in the presence of preemptive resources," Annals of Operations Research, Springer, vol. 259(1), pages 85-117, December.
    11. Farhad Hassanzadeh & Hamid Nemati & Minghe Sun, 2013. "Robust Optimization for Interactive Multiobjective Programming with Imprecise Information Applied to R&D Project Portfolio Selection," Working Papers 0194mss, College of Business, University of Texas at San Antonio.
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    13. Suchada Rianmora & Kulayuk Poulpanich, 2022. "Concept Development in a Walking Assistive Device: Offset Handle With a Small Base Area," International Journal of Knowledge and Systems Science (IJKSS), IGI Global, vol. 13(1), pages 1-39, January.

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