Compromise solutions for stochastic multicriteria acceptability analysis with uncertain preferences and nonmonotonic criteria

Abstract

As one of the most popular multiple criteria decision‐making (MCDM) methods, the stochastic multicriteria acceptability analysis (SMAA) is powerful in dealing with the uncertain preference information of decision makers. However, how to find compromise solutions for MCDM problems remains an open question in the SMAA family. A compromise solution refers to an alternative that is the closest to the ideal solution. In this paper, we propose an improved SMAA method, called the SMAA with compromise solutions (SMAA‐CO), to determine compromise solutions for MCDM problems with the uncertain preferences of decision makers and conflicting criteria. The SMAA‐CO uses nonmonotonic additive value functions to depict decision makers’ ideal values for reference points. We apply the proposed method to capture multiple decision makers’ uncertain preference information. An illustrative example concerning supplier selection in the pulp and paper industry is used to show the applicability of the proposed method. Some managerial insights are given.

A multicriteria model to support the selection of inspection service providers based on the delay time model

“…Thus, our future research tends to focus on group decision models to address problems involving DMs with different goals, responsibilities, and interests. Finally, there is also scope for trying to apply our model in conjunction with a method that works with the uncertainties provided by the decision‐makers (Liao et al., 2022).…”

A novel iterative approach to determining compromise rankings

“…Therefore, researchers focus on improving their methods based on compromise solutions. Liao et al presented an improved SMAA-CO method designed to determine compromise solutions [5]. The approach they presented is characterized by its ability to reflect uncertain preferences of decisionmakers with conflicting criteria.…”