A comparative analysis of score functions for multiple criteria decision making in intuitionistic fuzzy settings

2011 ◽  
Vol 181 (17) ◽  
pp. 3652-3676 ◽  
Author(s):  
Ting-Yu Chen
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Score Functions ◽  
Intuitionistic Fuzzy

scholarly journals Lexicographic Orders of Intuitionistic Fuzzy Values and Their Relationships

Mathematics ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 166 ◽  
Author(s):  
Feng Feng ◽  
Meiqi Liang ◽  
Hamido Fujita ◽  
Ronald Yager ◽  
Xiaoyan Liu
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Scalar Product ◽  
Multiple Attribute Decision Making ◽  
Benchmark Problem ◽  
Score Functions ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute ◽  
Risk Investment ◽  
Membership Score

Intuitionistic fuzzy multiple attribute decision making deals with the issue of ranking alternatives based on the decision information quantified in terms of intuitionistic fuzzy values. Lexicographic orders can serve as efficient and indispensable tools for comparing intuitionistic fuzzy values. This paper introduces a number of lexicographic orders by means of several measures such as the membership, non-membership, score, accuracy and expectation score functions. Some equivalent characterizations and illustrative examples are provided, from which the relationships among these lexicographic orders are ascertained. We also propose three different compatible properties of preorders with respect to the algebraic sum and scalar product operations of intuitionistic fuzzy values, and apply them to the investigation of compatible properties of various lexicographic orders. In addition, a benchmark problem regarding risk investment is further explored to give a comparative analysis of different lexicographic orders and highlight the practical value of the obtained results for solving real-world decision-making problems.

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.

scholarly journals Comparative Analysis of the Simple WISP and Some Prominent MCDM Methods: A Python Approach

Axioms ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 347
Author(s):  
Dragiša Stanujkić ◽  
Darjan Karabašević ◽  
Gabrijela Popović ◽  
Edmundas Kazimieras Zavadskas ◽  
Muzafer Saračević ◽  
...  
Keyword(s):  
Decision Making ◽  
Comparative Analysis ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Weighted Sum ◽  
Python Programming Language ◽  
Decision Making Problem ◽  
Product Method ◽  
Comparison Of The Results ◽  
Python Programming

This article presents a comparison of the results obtained using the newly proposed Simple Weighted Sum Product method and some prominent multiple criteria decision-making methods. For comparison, several analyses were performed using the Python programming language and its NumPy library. The comparison was also made on a real decision-making problem taken from the literature. The obtained results confirm the high correlation of the results obtained using the Simple Weighted Sum Product method and selected multiple criteria decision-making methods such as TOPSIS, SAW, ARAS, WASPAS, and CoCoSo, which confirms the usability of the Simple Weighted Sum Product method for solving multiple criteria decision-making problems.

New Method for Solving a General Multiple Criteria Decision-Making Problem Under Risk in Fuzzy Environment

2016 ◽  
Vol 15 (05) ◽  
pp. 1157-1179 ◽  
Author(s):  
N. Thillaigovindan ◽  
S. Anita Shanthi ◽  
J. Vadivel Naidu
Keyword(s):  
Decision Making ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Fuzzy Soft Set ◽  
Score Functions ◽  
Fuzzy Environment ◽  
Criterion Weight ◽  
Decision Making Problem ◽  
Criteria Weights ◽  
Interval Valued

This paper considers a multiple criteria decision-making (MCDM) problem under risk in fuzzy environment in its general form. There are m alternatives which need to be ranked on the basis of a set of n criteria. The alternatives and the criteria are evaluated based on a set of l characteristics. The entire data is presented in the form of interval valued intuitionistic fuzzy soft set of root type. In addition each criterion is assigned a subjective criterion weight based on expert’s evaluation and each characteristic is assigned a probability weight on the basis of decision maker’s knowlege and understanding of the importance of the characteristic. This problem may be called as a MCDM problem under risk in fuzzy environment in its general form. A method for ranking the alternatives using the new score functions, prospect theory and method of determining the optimum criteria weights is explained. An algorithm is developed for this purpose and its working illustrated with a suitable example.

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