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Sustainable Multi-Objective Portfolio Optimization with Mean, Variance and Return Entropy: An Iterative Intuitionistic Fuzzy Linear Programming Approach

Ghaemizadeh, Nazanin | 2025

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 58677 (01)
  4. University: Sharif University of Technology
  5. Department: Industrial Engineering
  6. Advisor(s): Habibi, Moslem; Seifbarghy, Mehdi
  7. Abstract:
  8. This study proposes a sustainable multi-objective portfolio optimization model that simultaneously integrates financial performance, sustainability, and market uncertainty through the framework of intuitionistic trapezoidal fuzzy sets. By allowing continuous degrees of membership and non-membership, the framework offers greater flexibility for financial problems and thus better captures uncertainty, investor preferences, and asset performance under ambiguous, incomplete and linguistic information. The proposed “Sustainability–Mean–Variance–Entropy” model jointly optimizes expected return, risk, and uncertainty, linking entropy-based risk assessment with stock sustainability within a single platform. Sustainability performance across social, environmental, and economic dimensions is evaluated via multi-criteria group decision-making process and a novel Shannon-entropy weighting method in an intuitionistic trapezoidal fuzzy environment, which objectively derives criterion weights from the dispersion of expert judgments. The optimization problem incorporates realistic investment constraints, including a budget limit, cardinality, no short-selling, and adaptive bounds tied to sustainability performance. An iterative intuitionistic fuzzy linear-programming algorithm is designed to refine solutions by maximizing membership degrees and minimizing non-membership degrees, thereby achieving improved trade-offs. Empirical validation on Tehran Stock Exchange data under four scenarios—sustainable, bearable, viable, equitable—demonstrates the robustness of the approach. In the most sustainable setting, the model attains a sustainability score of 83.12%, a return of 50.77%, a risk of 8.64%, and an uncertainty of 12.40%, with a mean runtime of 82.34 seconds. A comparative analysis against a uniform buy-and-hold strategy further confirms that the model consistently delivers superior outcomes. The proposed framework provides a transparent and adaptable decision-support tool, “POROPTO,” for investors, fund managers, and policymakers seeking to align capital allocation with financial efficiency and sustainability requirements
  9. Keywords:
  10. Portfolio Optimization ; Multi Objective Programming ; Multicriteria Decision Making ; Intuitionistic Fuzzy Sets ; Sustainability Assessment ; Uncertainty

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