Multiple criteria decision making method based on normal interval-valued intuitionistic fuzzy generalized aggregation operator

Complexity ◽  
2015 ◽  
Vol 21 (5) ◽  
pp. 277-290 ◽  
Author(s):  
Peide Liu ◽  
Fei Teng
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Aggregation Operator ◽  
Multiple Criteria ◽  
Intuitionistic Fuzzy ◽  
Interval Valued

Interval-Valued Intuitionistic Fuzzy Sets based Method for Multiple Criteria Decision-Making

2016 ◽  
Vol 5 (4) ◽  
pp. 192-210 ◽  
Author(s):  
Bhagawati Prasad Joshi
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Real Life ◽  
Multiple Criteria Decision Making ◽  
Intuitionistic Fuzzy Sets ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Intuitionistic Fuzzy ◽  
Life Problems ◽  
Interval Valued

Due to the huge applications of fuzzy set theory, many generalizations were available in literature. Atanassov (1983) and Atanassov and Gargov (1989) introduced the notions of intuitionistic fuzzy sets (IFSs) and interval-valued intuitionistic fuzzy sets (IVIFSs) respectively. It is observed that IFSs and IVIFSs are more suitable tools for dealing with imprecise information and very powerful in modeling real life problems. However, many researchers made efforts to rank IVIFSs due to its importance in fusion of information. In this paper, a new ranking method is introduced and studied for IVIFSs. The proposed method is compared and illustrated with other existing methods by numerical examples. Then, it is utilized to identify the best alternative in multiple criteria decision-making problems in which criterion values for alternatives are IVIFSs. On the basis of the developed approach, it would provide a powerful way to the decision-makers to make his or her decision under IVIFSs. The validity and applicability of the proposed method are illustrated with practical examples.

scholarly journals AN IVIF-ELECTRE OUTRANKING METHOD FOR MULTIPLE CRITERIA DECISION-MAKING WITH INTERVAL-VALUED INTUITIONISTIC FUZZY SETS

2015 ◽  
Vol 22 (3) ◽  
pp. 416-452 ◽  
Author(s):  
Ting-Yu CHEN
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Score Function ◽  
Multiple Criteria ◽  
Outranking Method ◽  
Intuitionistic Fuzzy ◽  
Ranking Procedures ◽  
Complete Ranking ◽  
Electre Method ◽  
Interval Valued

The method of ELimination Et Choix Traduisant la REalité (ELimination and Choice Expressing Reality, ELECTRE) is a well-known and widely used outranking method for handling decision-making problems. The purpose of this paper is to develop an interval-valued intuitionistic fuzzy ELECTRE (IVIF-ELECTRE) method and apply it to multiple criteria decision analysis (MCDA) involving the multiple criteria evaluation/selection of alternatives. Using interval-valued intuitionistic fuzzy (IVIF) sets with an inclusion comparison approach, concordance and discordance sets are identified for each pair of alternatives. Next, concordance and discordance indices are determined using an aggregate importance weight score function and a generalised distance measurement between weighted evaluative ratings, respectively. Based on the concordance and discordance dominance matrices, two IVIF-ELECTRE ranking procedures are developed for the partial and complete ranking of the alternatives. The feasibility and applicability of the proposed methods are illustrated with a multiple criteria decision-making problem of watershed site selection. A comparative analysis of other MCDA methods is conducted to demonstrate the advantages of the proposed IVIF-ELECTRE methods. Finally, an empirical study of job choices is implemented to validate the effectiveness of the current methods in the real world.

Comparative analysis of evolutionary algorithms for multiple criteria decision making with interval-valued belief distributions

2020 ◽  
Vol 14 (3) ◽  
pp. 373-391
Author(s):  
Guangyan Lu ◽  
Wenjun Chang
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Comparative Analysis ◽  
Evolutionary Algorithms ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Swarm Optimization ◽  
Interval Valued

In multiple criteria decision making (MCDM) with interval-valued belief distributions (IVBDs), individual IVBDs on multiple criteria are combined explicitly or implicitly to generate the expected utilities of alternatives, which can be used to make decisions with the aid of decision rules. To analyze an MCDM problem with a large number of criteria and grades used to profile IVBDs, effective algorithms are required to find the solutions to the optimization models within a large feasible region. An important issue is to identify an algorithm suitable for finding accurate solutions within a limited or acceptable time. To address this issue, four representative evolutionary algorithms, including genetic algorithm, differential evolution algorithm, particle swarm optimization algorithm, and gravitational search algorithm, are selected to combine individual IVBDs of alternatives and generate the minimum and maximum expected utilities of alternatives. By performing experiments with different numbers of criteria and grades, a comparative analysis of the four algorithms is provided with the aid of two indicators: accuracy and efficiency. Experimental results indicate that particle swarm optimization algorithm is the best among the four algorithms for combining individual IVBDs and generating the minimum and maximum expected utilities of alternatives.

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