Stochastic multi-attribute acceptability analysis with numerous alternatives

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.