Akademik Veri Yönetim Sistemi
Tezin Türü: Yüksek Lisans
Tezin Yürütüldüğü Kurum: İstanbul Kültür Üniversitesi, Lisansüstü Eğitim Enstitüsü, Endüstri Mühendisliği Anabilim Dalı, Türkiye
Tezin Onay Tarihi: 2026
Tezin Dili: İngilizce
Öğrenci: HAJAR RAHALI
Danışman: Duygun Fatih Demirel
Özet:
Making decisions regarding agricultural investments has grown more intricate due to diverging objectives, market volatility, and sustainability demands impacting the worldwide food system. Conventional assessment techniques are insufficient for tackling the complex dimensions of agricultural investments, resulting in project failure rates of 40-60% and more than 70% of agricultural investment firms depending on unstructured decision-making approaches. This research tackles the essential requirement for organized decision-support models that can successfully align financial performance, ecological sustainability, regulatory adherence, resource accessibility, partner trustworthiness, and cost-effectiveness in choosing agricultural investment sectors. The study creates and applies an extensive Fuzzy Analytical Hierarchy Process (AHP) and Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) framework to assess five agricultural investment areas in Westanbul company's portfolio. Data was gathered via structured interviews with three domain experts, employing linguistic scales transformed into triangular fuzzy numbers to reflect intrinsic uncertainty and subjectivity. The approach includes criterion weight assessment using Fuzzy AHP, alternative performance evaluation via Fuzzy TOPSIS, and robustness verification through sensitivity analysis across five distinct investor scenarios.
Results indicate that Feasibility & ROI lead the criterion hierarchy with a decision weight of 49%, while Agri-Industry Inputs stands out as the best investment option with a close coefficient of 0.507. The sensitivity analysis verifies the stability of the framework among various stakeholder preferences. Subsequent studies need to emphasize the integration of real-time market data, utilize machine learning algorithms for automated preference collection, and broaden stakeholder involvement to encompass wider community viewpoints for improved decision-making accuracy.