Robust multilinear target-based decision analysis considering high-dimensional interactions

• Published in: European journal of operational research Vol. 322; no. 3; pp. 920 - 936
• Main Authors: Feng, Qiong, Tong, Shurong, Corrente, Salvatore, Zhang, Xinwei
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.05.2025
• Subjects: k-interactive fuzzy measure; Multiple criteria analysis; Nonmodularity index; Stochastic multicriteria acceptability analysis; Target-based preference functions; Nonmodularity index; k-interactive fuzzy measure; Stochastic multicriteria acceptability analysis; Target-based preference functions; Multiple criteria analysis
• ISSN: 0377-2217
• DOI: 10.1016/j.ejor.2024.10.036

•High-dimensional interactions are rarely considered in decision-making methods.•k-interactive Nonmodularity index is derived for high-dimensional interactions.•Target-based preference function with 2-interactive Nonmodularity index is offered.•Robustness analysis is conducted in the light of uncertain targets and parameters. The Multilinear Target-based Preference Functions (MTPFs) support multi-attribute decision problems characterized by attribute interactions and targets. However, existing research falls short in flexibly modeling high-dimensional interactions and lacks robustness in decision-making recommendations when faced with uncertain parameters and targets. The paper proposes a robust multilinear target-based decision analysis framework considering high-dimensional interactions, along with uncertainties in parameters and targets. First, the necessity of high-dimensional interactions and the limitations of available MTPFs in modeling high-dimensional interactions are demonstrated. Second, the MTPFs based on the 2-interactive fuzzy measure and the Nonmodularity index are proposed to model the high-dimensional interactions and simultaneously reduce the computational challenges of parameter identification. Third, new descriptive measures are proposed based on the Stochastic Multicriteria Acceptability Analysis to evaluate the robustness of decision recommendations subject to uncertain targets and parameters. The validation and advantages of the framework are illustrated with simulation studies and an application in customer competitive evaluation of smart thermometer patches.