Selected work

Representative Papers

Rule-Induced Behavior of Fuzzy Scalar Objective Functions for Reliable Multi-Criteria Decision Making

Jul 22, 2026

This study addresses semantic inconsistencies, reference-point tracking failures, and selection biases in multi-criteria decision-making that arise from poorly configured membership functions and rule consequents in fuzzy scalarizing objective functions. It identifies a spurious reference-following phenomenon caused by flat activation plateaus and proposes a design methodology based on three explicit fuzzy rule sets. By integrating a bi-criteria analytical Pareto front with a dominance-reference modeling framework, the approach effectively refines inferior reference solutions. Experimental results demonstrate that the proposed method consistently recovers the true Pareto front, eliminates plateau-induced biases, and uniformly enhances the quality of dominated reference solutions under diagnostic performance metrics.

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Comparing Scalar Objective Functions for Multi-Criteria Engineering Optimization

Jun 26, 2026

This study addresses the challenge of selecting a single solution from the Pareto front in multi-objective engineering optimization through scalarization, noting that different scalarizing functions exhibit significant differences in attainability, preference articulation, and coverage of non-supported solutions. The authors systematically compare four normalized scalarization approaches—weighted sum, achievement scalarizing function, desirability function, and fuzzy logic—on both convex and concave Pareto fronts, evaluating their performance via analytical control experiments in terms of attainable regions, selection density, sensitivity, and parameter interpretability. The findings reveal structural limitations of the weighted sum method on concave fronts, while the other three methods effectively cover non-supported regions. Notably, the desirability function introduces nonlinear preference mapping, and the fuzzy approach enables reference-dependent and non-separable modeling of engineering preferences, offering new insights for scalarization under complex preference structures.

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Recent publications

Latest Papers

Rule-Induced Behavior of Fuzzy Scalar Objective Functions for Reliable Multi-Criteria Decision Making

Jul 22, 2026

This study addresses semantic inconsistencies, reference-point tracking failures, and selection biases in multi-criteria decision-making that arise from poorly configured membership functions and rule consequents in fuzzy scalarizing objective functions. It identifies a spurious reference-following phenomenon caused by flat activation plateaus and proposes a design methodology based on three explicit fuzzy rule sets. By integrating a bi-criteria analytical Pareto front with a dominance-reference modeling framework, the approach effectively refines inferior reference solutions. Experimental results demonstrate that the proposed method consistently recovers the true Pareto front, eliminates plateau-induced biases, and uniformly enhances the quality of dominated reference solutions under diagnostic performance metrics.

0 citationsRead paper

Comparing Scalar Objective Functions for Multi-Criteria Engineering Optimization

Jun 26, 2026

This study addresses the challenge of selecting a single solution from the Pareto front in multi-objective engineering optimization through scalarization, noting that different scalarizing functions exhibit significant differences in attainability, preference articulation, and coverage of non-supported solutions. The authors systematically compare four normalized scalarization approaches—weighted sum, achievement scalarizing function, desirability function, and fuzzy logic—on both convex and concave Pareto fronts, evaluating their performance via analytical control experiments in terms of attainable regions, selection density, sensitivity, and parameter interpretability. The findings reveal structural limitations of the weighted sum method on concave fronts, while the other three methods effectively cover non-supported regions. Notably, the desirability function introduces nonlinear preference mapping, and the fuzzy approach enables reference-dependent and non-separable modeling of engineering preferences, offering new insights for scalarization under complex preference structures.

0 citationsRead paper