Temporal Multi-Objective Optimization for Sustainable Agricultural Finance: Evidence from Evolutionary Algorithms
Yazarlar (5)
Doç. Dr. Aylin Erdoğdu İstanbul Arel Üniversitesi, Türkiye
Prof. Dr. Faruk DAYI Kastamonu Üniversitesi, Türkiye
Prof. Dr. Ferah Yıldız Kocaeli Üniversitesi, Türkiye
Farshad Ganjı
İstanbul Aydın Üniversitesi, Türkiye
İstanbul Aydın Üniversitesi, Türkiye
Dr. Öğr. Üyesi Ahmet İçöz Kocaeli Üniversitesi, Türkiye
| Makale Türü |
Özgün Makale
(SSCI, AHCI, SCI, SCI-Exp dergilerinde yayınlanan tam makale)
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| Dergi Adı | Sustainability Switzerland (Q2) | ||
| Dergi ISSN | 2071-1050 Wos Dergi Scopus Dergi | ||
| Dergi Tarandığı Indeksler | SSCI | ||
| Makale Dili | İngilizce | Basım Tarihi | 04-2026 |
| Cilt / Sayı / Sayfa | 18 / 8 / 1–27 | DOI | 10.3390/su18083839 |
| Makale Linki | https://doi.org/10.3390/su18083839 | ||
| UAK Araştırma Alanları |
İşletme Finansı
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| Özet |
| This study presents a modeling framework for multi-objective optimization in agricultural finance, emphasizing profitability, risk management, and sustainability. The proposed Advanced Financial Framework for Temporal Synergistic Optimization (AFFTSO) does not introduce a new algorithm; rather, it structures existing optimization workflows to explicitly integrate temporal dynamics, evolving objectives, feedback loops, and sustainability-oriented considerations. AFFTSO is designed to support long-term planning under fluctuating economic and environmental conditions. To demonstrate its applicability, AFFTSO is applied to a 25-year Turkish agricultural dataset (2000–2025), encompassing production, financial, market, and climate indicators. Two widely used evolutionary algorithms—Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)—are benchmarked within this framework, optimizing profit, financial risk, and resource-use efficiency simultaneously. Results show that NSGA-II consistently outperforms MOPSO, yielding a 12.4% increase in cumulative net profit, a 20.3% reduction in financial risk, and a 15.7% improvement in resource-use efficiency. These outcomes confirm that embedding temporal structures, adaptive objectives, and sustainability considerations into multi-objective optimization models enhances the robustness and resilience of financial planning. Overall, AFFTSO offers a practical approach for guiding resource allocation, investment planning, and risk-aware decision-making in agriculture. By bridging computational optimization with sustainability-oriented financial … |
| Anahtar Kelimeler |
| agricultural finance | agricultural value chains | Multi-Objective Nonlinear Programming (MONLP) | risk management | sustainable agriculture |
| Atıf Sayıları | |
| Web of Science | 1 |
| Scopus | 1 |