One of the techniques, which are used for Multiple Criteria Decision Making (MCDM) is the permutation. In the classical form of permutation, it is assumed that weights and decision matrix components are crisp. However, when group decision making is under consideration and decision makers could not agree on a crisp value for weights and decision matrix components, fuzzy numbers should be used. In this article, the fuzzy permutation technique for MCDM problems has been explained. The main deficiency of permutation is its big computational time, so a Tabu Search (TS) based algorithm has been proposed to reduce the computational time. A numerical example has illustrated the proposed approach clearly. Then, some benchmark instances extracted from literature are solved by proposed TS. The analyses of the results show the proper performance of the proposed method.

In decision making when multiple criteria are determined, the best choice depends on having complete information and proper decision-making technique. The permutation method is one of the popular techniques used in the context of multiple criteria decision making (MCDM). In this paper, a method is presented where there is more than one vector of weights for the criteria and there are uncertainties associated with criteria weights or there are multiple decision makers. We first take different weight vectors to create a multi-objective problem and then we solve them simultaneously to achieve appropriate Pareto solutions of the permutation method. Therefore, MOPSO and NSGA-II algorithms are utilized to find non-dominated solutions. Some examples in different sizes are considered to compare the efficiency of the proposed methods. Results show that by increasing the number of options and considering the computational time, the proposed methods perform better compared with the exact method. Moreover, NSGA-II is more efficient than MOPSO for the considered problem.

The paper discusses about the effect of outliers and trends on the response surface design fitted to the experiments results. The common way to analyze the response surface is to fit the polynomial regression to the response variable by ordinary least square method and to find the significant controllable variables by ANOVA. In this case, the outliers can have confusing effect on the regression model, which derives the experiment results and lead to wrong interpretation of the data. The proposed moving average iterative method (MAIW) of this paper is a robust approach to decrease the effect of these faulty points by considering the previous data to detect the outliers or detect the probable trends in residuals. The iterative weighting method is used to estimate the coefficients of the regression model and a numerical example illustrates the proposed approach.