Evaluating and selecting agricultural insurance packages through an AHP-based fuzzy TOPSIS Method

Participating in agricultural insurance programs can help farmers reduce risks of financial loss caused by adverse impacts, such as climate change. However, farmers in many developing countries face numerous obstacles to select a suitable agricultural insurance package, which can be attributed to different packages with various criteria including quantitative and qualitative, their different importance, and the aggregation of those weighted criteria. Thus, developing a method for evaluating packages has become a critical issue. To resolve the above problems, this paper proposes an AHP-based fuzzy TOPSIS method, in which ratings of alternative packages versus qualitative criteria are assessed in linguistic values represented by fuzzy numbers. In the proposed method, the criteria weights and the weights of distances of each alternative from positive and negative ideal solutions are generated by AHP to present the objectivity of the weight derivation process. In addition, the mean of removals is used to rank the final fuzzy values to clearly develop the formulas of the ranking procedure to help facilitate the decision-making process. A numerical example of evaluating and selecting agricultural insurance packages is presented to demonstrate the feasibility of the proposed method. Finally, a numerical comparison is conducted to show the robustness of the proposed method.