Akademik Veri Yönetim Sistemi
International Journal of Industrial Engineering : Theory Applications and Practice, cilt.32, sa.4, ss.1117-1142, 2025 (SCI-Expanded)
Agriculture has continuously evolved in terms of technology and economics throughout human history. From ancient times to the present, it has consistently embraced technological innovations to produce more efficient and higher-quality products. Over the past two decades, the rise of electric vehicles has emphasized the importance of electric tractors in agriculture. The widespread adoption of Agricultural Electric Tractors can lead to more sustainable and ecologically friendly farming practices. Although agricultural electric tractors are more environmentally friendly compared to traditional internal combustion tractors, the limited availability of solar-powered charging stations poses a significant barrier to their widespread use. This study aims to develop a mixed-integer mathematical model to determine the optimal locations for solar-powered charging stations in open-field agricultural areas for Agricultural Electric Tractors. The Chance-Constrained optimization model is compared with a deterministic model to evaluate the performance of the proposed mathematical model. Given that solving the deterministic model is an NP-hard problem, a Binary Genetic Algorithm was employed as a solution approach. This comparison focuses on assessing the effectiveness of the Chance-Constrained model in handling uncertainty, highlighting differences in solution quality, computational efficiency, and robustness. Additionally, the study identifies the locations for off-grid (solar-powered) charging stations for electric vehicles in agricultural settings. The results obtained from the mathematical model, where only one solar-powered charging station provides service, have been tested using the Arena simulation program and subsequently interpreted.