SMAA methods and their applications: a literature review and future research directions

R. Pelissari (), M. C. Oliveira, S. Ben Amor, A. Kandakoglu and A. L. Helleno
Additional contact information
R. Pelissari: UNIMEP
M. C. Oliveira: UNIMEP
S. Ben Amor: University of Ottawa
A. Kandakoglu: University of Ottawa
A. L. Helleno: UNIMEP

Annals of Operations Research, 2020, vol. 293, issue 2, No 3, 433-493

Abstract: Abstract Stochastic multicriteria acceptability analysis (SMAA) is a family of multiple criteria decision making (MCDM) methods dealing with incomplete, imprecise, and uncertain information on the evaluations and preference model parameters. As it provides a general framework that has extensions to deal with various specificities in MCDM problems, the development of SMAA methods and their applications in real-life decision-making problems have been increased over the recent years. This paper provides an up-to-date literature review of different SMAA methods and their applications in various areas. First, we selected, from different on-line data base, 118 articles published between 1998 and 2017. We categorized the selected papers into theoretical and applied. While the theoretical papers were analyzed based on the method’s aggregation procedure, type of problem, type of method’s outputs and inputs, the applied papers were separated and analyzed by application areas. Then, we provide some descriptive statistics, analyzing the papers regarding to publication year and journals of publication. Finally, we provide some guidelines to assist decision-makers in the choice of a SMAA method on a specific decision-making context and some future research directions.

Keywords: Uncertainty; Imprecision; Multiple criteria decision making (MCDM); Stochastic multicriteria acceptability analysis (SMAA); Simulation (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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DOI: 10.1007/s10479-019-03151-z

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