Combining Fuzzy Logic and Genetic Algorithms to Optimize Cost, Time and Quality in Modern Agriculture

Aylin Erdoğdu, Faruk Dayi (), Ferah Yildiz, Ahmet Yanik and Farshad Ganji
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Aylin Erdoğdu: Department of Finance and Banking, Faculty of Economics and Administrative Sciences, Istanbul Arel University, Istanbul 34295, Türkiye
Faruk Dayi: Department of Business Administration, Faculty of Economics and Administrative Sciences, Kastamonu University, Kastamonu 37160, Türkiye
Ferah Yildiz: Department of Business Administration, Faculty of Management, Kocaeli University, Kocaeli 41350, Türkiye
Ahmet Yanik: Department of Business Administration, Faculty of Economics and Administrative Sciences, Recep Tayyip Erdoğan University, Rize 53100, Türkiye
Farshad Ganji: Department of Finance and Banking, Faculty of Economics and Administrative Sciences, Istanbul Arel University, Istanbul 34295, Türkiye

Sustainability, 2025, vol. 17, issue 7, 1-41

Abstract: This study presents a novel approach to managing the cost–time–quality trade-off in modern agriculture by integrating fuzzy logic with a genetic algorithm. Agriculture faces significant challenges due to climate variability, economic constraints, and the increasing demand for sustainable practices. These challenges are compounded by uncertainties and risks inherent in agricultural processes, such as fluctuating yields, unpredictable costs, and inconsistent quality. The proposed model uses a fuzzy multi-objective optimization framework to address these uncertainties, incorporating expert opinions through the alpha-cut technique. By adjusting the level of uncertainty (represented by alpha values ranging from 0 to 1), the model can shift from pessimistic to optimistic scenarios, enabling strategic decision making. The genetic algorithm improves computational efficiency, making the model scalable for large agricultural projects. A case study was conducted to optimize resource allocation for rice cultivation in Asia, barley in Europe, wheat globally, and corn in the Americas, using data from 2003 to 2025. Key datasets, including the USDA Feed Grains Database and the Global Yield Gap Atlas, provided comprehensive insights into costs, yields, and quality across regions. The results demonstrate that the model effectively balances competing objectives while accounting for risks and opportunities. Under high uncertainty (α = 0\alpha = 0α = 0), the model focuses on risk mitigation, reflecting the impact of adverse climate conditions and market volatility. On the other hand, under more stable conditions and lower market volatility conditions (α = 1\alpha = 1α = 1), the solutions prioritize efficiency and sustainability. The genetic algorithm’s rapid convergence ensures that complex problems can be solved in minutes. This research highlights the potential of combining fuzzy logic and genetic algorithms to transform modern agriculture. By addressing uncertainties and optimizing key parameters, this approach paves the way for sustainable, resilient, and productive agricultural systems, contributing to global food security.

Keywords: fuzzy logic; genetic algorithm; cost–time–quality trade-off; modern agriculture; optimization techniques; hybrid optimization methods; agricultural productivity (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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