Wind farms are designed to supply power to the consumers at a minimal price. The cost of wind power production directly or indirectly depends on proper selection of vendors. The present paper highlights a model for selection and ranking of vendors for a wind farm based on fuzzy set theory to determine criteria weights and an additive ratio assessment (ARAS) method to analysis criteria values. The objective of the paper is to establish the ARAS method as an effective method for Vendor selection. A case study is shown to ascertain the proposed method especially when the criteria are interdependent and conflicting in nature. The result is validated with another popular MCDM technique, COPRAS, which shows that the models are effective and applicable, and provide decision makers with better solutions for decision making.

Today global warming is on the rise and the natural resources are getting consumed at a faster rate. Power consumption has increased many folds to cater the human need. Thus renewable energy resources are the only option available at this juncture. Wind energy is one of the renewable energy. Location selection for wind farm takes an important role on power generation. However, the location selection is a complex multicriteria problem due to the criteria factors which are conflicting in nature as well as uncertain. The process becomes more complex when a group of decision makers are involved in decision making. In the present study, a COPRAS (COmplex PRoportional ASsessment) based multi-criteria decision-making (MCDM) methodology is done under fuzzy environment with the help of multiple decision makers. More specifically, this study is aimed to focus the applicability of COPRAS-F as a strategic decision making tools to handle the group decision-making problems.

In this paper, we examine advanced optimization approach for portfolio problem introduced by Black and Litterman to consider the shortcomings of Markowitz standard Mean-Variance optimization. Black and Litterman propose a new approach to estimate asset return. They present a way to incorporate the investor’s views into asset pricing process. Since the investor’s view about future asset return is always subjective and imprecise, we can represent it by using fuzzy numbers and the resulting model is multi-objective linear programming. Therefore, the proposed model is analyzed through fuzzy compromise programming approach using appropriate membership function. For this purpose, we introduce the fuzzy ideal solution concept based on investor preference and indifference relationships using canonical representation of proposed fuzzy numbers by means of their corresponding?-cuts. A real world numerical example is presented in which MSCI (Morgan Stanley Capital International Index) is chosen as the target index. The results are reported for a portfolio consisting of the six national indices. The performance of the proposed models is compared using several financial criteria.