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Preference disaggregation analysis (PDA) is a widely used approach inmulticriteria decision analysis that aims to extract preferential informationfrom holistic judgments provided by decision makers. This paper presents anoriginal methodological framework for PDA that addresses two significantchallenges in this field. Firstly, it considers the multidimensional structureof data to capture decision makers' preferences based on descriptive measuresof the criteria time series, such as trend and average. This novel approachenables an understanding of decision makers' preferences in decision-makingscenarios involving time series analysis, which is common in medium- tolong-term impact decisions. Secondly, the paper addresses the robustness issuecommonly encountered in PDA methods by proposing a multi-objective and MonteCarlo simulation approach. This approach enables the consideration of multiplepreference models and provides a mechanism to converge towards the most likelypreference model. The proposed method is evaluated using real data,demonstrating its effectiveness in capturing preferences based on criteria andtime series descriptive measures. The multi-objective analysis highlights thegeneration of multiple solutions, and, under specific conditions, reveals thepossibility of achieving convergence towards a single solution that representsthe decision maker's preferences.

Improving preference disaggregation in multicriteria decision making: incorporating time series analysis and a multi-objective approach