Multi-objective optimization (MOO) is a fundamental approach in decision-making that addresses the complexities of balancing multiple conflicting objectives. While MOO aims to identify solutions that balance trade-offs among competing objectives, decision-makers often require a singular optimal solution for effective implementation. This paper highlights the limitations of existing methodologies, particularly their reliance on high-level information and the weighted sum approach, which is inadequate for non-convex problems. To overcome these challenges, a novel framework based on the Shapley value and Tchebycheff function is proposed to ensure fairness in assigning weights to objective functions without relying on external inputs. This approach utilizes Tchebycheff functions to identify points on the Pareto front that correspond to these weights, thereby enhancing decision-making in MOO by providing an equitable post- Pareto mechanism for selecting fair-optimal solutions. The paper ends with a discussion of results and their implications for upcoming MOO research.

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