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Stochastic multi-attribute acceptability analysis-based heuristic algorithms for multi-attribute project portfolio selection and scheduling problem

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

Abstract

Selecting an appropriate project portfolio and starting work at the right time are two key decision-making problems in project and engineering management. Project portfolio selection (PPS) and scheduling become complicated when random attribute values and unknown attribute weights are simultaneously considered. To manage this complex decision-making problem, a stochastic multi-attribute acceptability analysis (SMAA)-based approach is proposed to formulate multi-attribute PPS and scheduling problem with random attribute values and unknown attribute weights. Four heuristic algorithms, namely, SMAA-based particle swarm optimisation, SMAA-based genetic algorithm, SMAA-based simulated annealing algorithm, and SMAA-based teaching–learning-based optimisation, have been developed to solve this problem. The performance of the four algorithms is also evaluated on four data sets.

Suggested Citation

  • Shiling Song & Tingting Wei & Feng Yang & Qiong Xia, 2021. "Stochastic multi-attribute acceptability analysis-based heuristic algorithms for multi-attribute project portfolio selection and scheduling problem," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(6), pages 1373-1389, June.
    • Handle: RePEc:taf:tjorxx:v:72:y:2021:i:6:p:1373-1389
    DOI: 10.1080/01605682.2020.1718018
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    Cited by:

    1. 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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