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SMAA-PO: project portfolio optimization problems based on stochastic multicriteria acceptability analysis

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  • Feng Yang
  • Shiling Song
  • Wei Huang
  • Qiong Xia

Abstract

In a typical project portfolio optimization problem, the decision makers would choose the optimal project portfolio from the candidates under the constraints of certain resources. However, in lots of real world examples, traditional approaches cannot perfectly solve the project portfolio optimization problems with unknown information about the importance of attributes. An innovative feature of this paper is to propose a stochastic multiattribute acceptability analysis (SMAA) to solve the multiattribute project portfolio optimization problems. SMAA includes a series of methods, which provide full ranks for a set of alternatives by exploring the weight space to make each alternative the most preferred one. Our proposed method provides feasible procedures to deal with the project portfolio optimization problems and, as an extension of traditional SMAA methods, expands the SMAA applications from the comparison of single alternatives to the comparison of sets of alternatives. Copyright Springer Science+Business Media New York 2015

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  • Feng Yang & Shiling Song & Wei Huang & Qiong Xia, 2015. "SMAA-PO: project portfolio optimization problems based on stochastic multicriteria acceptability analysis," Annals of Operations Research, Springer, vol. 233(1), pages 535-547, October.
    • Handle: RePEc:spr:annopr:v:233:y:2015:i:1:p:535-547:10.1007/s10479-014-1583-9
    DOI: 10.1007/s10479-014-1583-9
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    Cited by:

    1. Song, Shiling & Yang, Feng & Yu, Pingxiang & Xie, Jianhui, 2021. "Stochastic multi-attribute acceptability analysis with numerous alternatives," European Journal of Operational Research, Elsevier, vol. 295(2), pages 621-633.
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    4. Zhang, Xinwei & Yan, Yong & Wang, Lilin & Wang, Yang, 2024. "A ranking approach for robust portfolio decision analysis based on multilinear portfolio utility functions and incomplete preference information," Omega, Elsevier, vol. 122(C).

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