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Stochastic multi-attribute acceptability analysis with numerous alternatives

Author

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  • Song, Shiling
  • Yang, Feng
  • Yu, Pingxiang
  • Xie, Jianhui

Abstract

Stochastic multi-attribute acceptability analysis (SMAA) is a method for assisting multi-attribute decision-making with unknown preference information and inaccurate or uncertain attribute values. The traditional Monte Carlo simulation-based SMAA can calculate the rank acceptability of each alternative for small data sets. However, computation time exhibits a geometric growth as the number of alternatives increases. Thus, decision makers are facing a problem of efficiently running SMAA procedure on large data sets. In this paper, we propose a novel algorithm for solving this problem. In particular, we divide large alternative set into small groups on the basis of studying of the relationships of alternatives’ k-best rank acceptability and holistic acceptability between whole alternative sets and their subsets. Lastly, the proposed method is applied to simulated data sets and real-world data sets in the express industry.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:ejores:v:295:y:2021:i:2:p:621-633
    DOI: 10.1016/j.ejor.2021.03.037
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    References listed on IDEAS

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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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