Stochastic multi-attribute acceptability analysis with numerous alternatives

Shiling Song, Feng Yang, Pingxiang Yu and Jianhui Xie

European Journal of Operational Research, 2021, vol. 295, issue 2, 621-633

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.

Keywords: Multiple criteria analysis; Stochastic multi-attribute acceptability analysis; Large-scale computation (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:295:y:2021:i:2:p:621-633

DOI: 10.1016/j.ejor.2021.03.037

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