Because of the complexity of decision-making environment, the uncertainty of fuzziness and the uncertainty of grey maybe coexist in the problems of multi-attribute group decision making. In this paper, we study the problems of multi-attribute group decision making with hybrid grey attribute data (the precise values, interval numbers and linguistic fuzzy variables coexist, and each attribute val...

the development of multiple criteria decision making techniques is to provide the possibility of using multiple criteria in decision making problems. multi-criteria decision making problems primarily relate to choosing the best option among several available options and according to some indicators. one of the major issues in this regard is to determine the importance weights of decision criter...

The sensitivity analysis for multi-attribute decision making (MADM) problems is important for two reasons: First, the decision matrix as the source of the results of a decision problem is inaccurate because it sorts the alternatives in each criterion inaccurately. Second, the decision maker may change his opinions in a time period because of changes in the importance of the criteria and in the ...

vendor selection decisions are complicated by the fact that various conflicting multi-objective factors must be considered in the decision making process. the problem of vendor selection becomes still more compli-cated with the inclusion of incremental discount pricing schedule. such hard combinatorial problems when solved using meta heuristics produce near optimal solutions. this paper propose...

This paper proposes a compromise model, based on the technique for order preference through similarity ideal solution (TOPSIS) methodology, to solve the multi-objective large-scale linear programming (MOLSLP) problems with block angular structure involving fuzzy parameters. The problem involves fuzzy parameters in the objective functions and constraints. This compromise programming method is ba...

Decision Neural Network is a new approach for solving multi-objective decision-making problems based on artificial neural networks. Using inaccurate evaluation data, network training has improved and the number of educational data sets has decreased. The available training method is based on the gradient decent method (BP). One of its limitations is related to its convergence speed. Therefore,...