Abstract
TOPSIS (Technique for Order Performance by Similarity to Ideal Solution) is a very practical decision support method used in several areas of life. This method already exists in the literature in the context of a single decision maker. In order to adapt this method to group decision making, which can be easily applied in various situations, this work extended the TOPSIS method to group decision making using the quadratic mean and the geometric mean. In this work, numerical applications have been made and interesting results have been obtained.
Highlights
Decision support is generally requested by industrialists and researchers when they are faced with complex decision-making problems [12]
In all domains, from everyday life to the world of work, a large number of decisions are made, either individually or collectively. They lead to happy or unhappy outcomes, they are the object of regret or contentment, they give rise to progress or regression [2] .According to the idea of synergy, decisions made collectively tend to be more effective than decisions made individually, so according to [10], Multiple Attribute Group Decision Making (MAGDM) plays an important role in the real world
In this paper we have provided a new method for solving group decision problems by extending the TOPSIS method (Technique for Order Performance by Similarity to Ideal Solution)
Summary
Decision support is generally requested by industrialists and researchers when they are faced with complex decision-making problems [12]. For [3] it is necessary to establish a consensus-building process in order to arrive at a decision that best reconciles local preferences with the choice made by the group. For this purpose, several aggregation methods related to group decision already exist in the scientific literature such as TOPSIS (Technique for Order Performance by Similarity to Ideal Solution). In this paper we have provided a new method for solving group decision problems by extending the TOPSIS method (Technique for Order Performance by Similarity to Ideal Solution). After the State of the Art we will write our new method, we will make a numerical application, comparisons and we will finish with a conclusion
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