Data envelopment analysis (DEA) estimates the relative performance of decision making units (DMUs). This paper uses the idea of the Analytical hierarchy process (AHP) method and fuzzy set theory to modify the model of DEA which can be used to evaluate the performance of business units. In this paper, a new method has been proposed for estimating the performance of DMUs with interval data and weights of data. The models proposed in previous studies have interval data or interval weights of data, so the proposed model has more flexibility than previous studies . Thus, innovation has been done theoretically and the experimental part is for testing the theory. Finally, a method is introduced for ranking the DMUs by computed performance. In order to prove the applicability of the proposed method, a case study for ranking of some Iranian automotive companies products is given. The model results indicate that the proposed model will be useful for practical problems, especially when the number of choices is limited.

Abstract

 Forecasting of crude oil price plays a crucial role in optimization of production, marketing and market strategies. Furthermore, it plays a significant role in government’s policies, because the government sets and implements its policies not only according to the current situation but also according to short run and long run predictions of important economic variables like oil price. The main purpose of this study is modeling and forecasting spot oil price of Iran by using GARCH model and A Gravitational Search Algorithm. Performed forecasts of this study are based in static and out-of-sample forecasting and each subseries data is divided in to two parts: data for estimation and data for forecasting. The forecast horizon is next leading period and its length is one month. In this study the selected models for forecasting spot oil of Iran are GARCH(2,1) and a Cobb Douglas function which is functional of prices of 5 days ago. Finally, the performances of these models are compared. For comparison of these models MSE, RMSE, MAE, and MAPE criteria are used and the results indicate that except in MAPE criterion, the mentioned criteria are smaller for GARCH model in comparison to GSA algorithm.