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Multi-Criteria Decision-Making for Tractor Selection in Agricultural Mechanization
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DOI：10.18016/ksutarimdoga.vi.1727037.png)
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Abstract
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This study aims to determine the most suitable tractor brand for farmers operating in the Aegean Region of T & uuml;rkiye. In light of rising cost pressures and the need for enhanced agricultural productivity, the study emphasizes the necessity of objective and scientific approaches in tractor selection processes. To address uncertainties in decision-making, fuzzy multi-criteria decision-making techniques were employed. Specifically, the Fuzzy Simplified Best-Worst Method was used to calculate the weights of the evaluation criteria, while the Fuzzy Combined Compromise Solution method was applied to rank 14 tractor brands based on 18 defined criteria. Expert evaluations were obtained from a panel of five experienced decision-makers. The analysis revealed that the most critical main criterion was economic factors (0.366), whereas brand and image (0.122) were considered the least important. Among the sub-criteria, purchase cost (0.1352), operating cost (0.1296), and maneuverability (0.1293) were the most influential. New Holland was identified as the most preferred tractor brand with the highest score. The consistency of these findings was confirmed through sensitivity analysis. The findings of the study indicate that economic factors are the most influential main criterion in tractor selection, with purchase and operating costs emerging as the most critical sub-criteria. Based on the evaluation conducted using the F-CoCoSo method, the New Holland brand received the highest score and was identified as the most appropriate alternative by the decision-makers. Sensitivity analyses revealed that New Holland consistently ranked first across all weighting scenarios, thereby confirming the robustness and reliability of the model. These results demonstrate that the integrated use of the F-SBWM and F-CoCoSo methods offers a systematic and consistent evaluation framework for addressing complex multi-criteria decision-making problems such as tractor selection. The integration of F-SBWM and F-CoCoSo methods offers a systematic and reliable framework for tractor selection. The results indicate that scientific decision-making tools in agricultural machinery selection significantly improve resource efficiency and agricultural productivity.
Keywords:
Agricultural Mechanization F-CoCoSo
Fuzzy Multi-Criteria Decision-Making
F-SBWM
Tractor Selection
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