scholarly journals Fuzzy Integral Multiple Criteria Decision Making Method Based on Fuzzy Preference Relation on Alternatives

2016 ◽  
Vol 4 (3) ◽  
pp. 280-290 ◽  
Author(s):  
Qiaojiao Zhao ◽  
Ling Zeng ◽  
Jinjin Liu
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Programming Model ◽  
Preference Relation ◽  
Multiple Criteria Decision Making ◽  
Fuzzy Relation ◽  
Multiple Criteria ◽  
Fuzzy Integral ◽  
Fuzzy Preference Relation ◽  
Preference Information

AbstractA new method is proposed to solve the multiple criteria decision making with interacting criteria, where the preference information on alternatives in a fuzzy relation given by the decision maker. On the basis of the decision maker’s preference information, two types of models — the least squares model, the linear programming model — are constructed to determine the capacities and then to select the most desirable alternative. Finally, a numerical example is used to illustrate the validity and practicality of the proposed method.

scholarly journals COMMENTS ON “MULTIPLE CRITERIA DECISION MAKING (MCDM) METHODS IN ECONOMICS: AN OVERVIEW”

2012 ◽  
Vol 18 (4) ◽  
pp. 672-695 ◽  
Author(s):  
James J. H Liou ◽  
Gwo-Hshiung Tzeng
Keyword(s):  
Decision Making ◽  
Performance Improvement ◽  
Information Fusion ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Fuzzy Integral ◽  
Multi Objective Programming ◽  
New Concepts ◽  
Aspiration Levels ◽  
Published Paper

This paper offers comments on a previously published paper, titled “Multiple criteria decision making (MCDM) methods in economics: an overview,” by Zavadskas and Turskis (2011). The paper's authors made great efforts to summarize MCDM methods but may have failed to consider several important new concepts and trends in the MCDM field for solving actual problems. First, the traditional model assumes the criteria are independently and hierarchically structured; however, in reality, problems are often characterized by interdependent criteria and dimensions and may even exhibit feedback-like effects. Second, relatively good solutions from the existing alternatives are replaced by aspiration levels to fit today's competitive markets. Third, the emphasis in the field has shifted from ranking and selection when determining the most preferable approaches to performance improvement of existing methods. Fourth, information fusion techniques, including the fuzzy integral method, have been developed to aggregate the performances. Finally, the original fixed resources in multi-objective programming are divided such that both decision and objective spaces are changeable. In this paper, we add new concepts and provide comments that could be thought of as an attempt to complete the original paper.

scholarly journals A  linear programming technique to solve fuzzy multiple criteria decision making problems with an application

2020 ◽  
Author(s):  
S.A. Sadabadi ◽  
A. Hadi-Vencheh ◽  
A. Jamshidi ◽  
M. Jalali
Keyword(s):  
Decision Making ◽  
Linear Programming ◽  
Linear Models ◽  
Multiple Criteria Decision Making ◽  
Multiple Criteria ◽  
Radius Of Gyration ◽  
Programming Technique ◽  
Weighting Method ◽  
Practical Applications ◽  
Simple Additive Weighting

Generally, in real world multiple criteria decision making (MCDM) problems, we concern with inaccurate data. This paper transforms a fuzzy multiple criteria decision making (FMCDM) problem into three linear programming models based on simple additive weighting method (SAW). The new linear models calculate fuzzy performance scores for each alternative. To rank the alternatives, the numerical value of the area between the Radius of Gyration (ROG) and original points of the given fuzzy numbers is used. Finally, we illustrate the practical applications of the proposed method in selection an industrial zone for construct dairy products factory.

Decision-Making Models Based on Satisfaction Degree with Incomplete Hesitant Fuzzy Preference Relation

2021 ◽  
Author(s):  
Jian Li ◽  
Li-li Niu ◽  
Qiongxia Chen ◽  
Zhong-xing Wang
Keyword(s):  
Decision Making ◽  
Programming Model ◽  
Preference Relation ◽  
Weight Vector ◽  
Decision Makers ◽  
Fuzzy Preference Relation ◽  
Satisfaction Degree ◽  
Hesitant Fuzzy Preference Relation ◽  
Decision Making Models ◽  
Fuzzy Preference

Abstract To address the situation where the incomplete hesitant fuzzy preference relation (IHFPR) is necessary, this paper develops decision-making models based on decision makers’ satisfaction degree with IHFPR. First, the consistency measures from the perspectives of additive and multiplicative consistent IHFPR are defined based on the relationships between the IHPFRs and their corresponding priority weight vector, respectively. Second, two decision-making models are developed in view of the proposed additive and multiplicative consistency measures. The main characteristic of the constructed model sarethey taking into account the decision makers’ satisfaction degree. The objective functions of the models are developed by maximizing the parameter of satisfaction degree. Third, a square programming model is developed to obtain the decision makers’ weights byutilizing the optimal priority weight vectors information, the solution of the model is obtained by solving the partial derivatives ofLagrange function.Finally, a procedure for multi-criteria decision-making (MCDM) problems with IHFPRs is given, and an illustrative example in conjunction with comparative analysis is used to demonstrate the proposed models are feasible and efficiency for practical MCDM problems.

scholarly journals DYNAMIC FUZZY MULTIPLE CRITERIA DECISION MAKING FOR PERFORMANCE EVALUATION

2015 ◽  
Vol 21 (5) ◽  
pp. 705-719 ◽  
Author(s):  
Guangxu LI ◽  
Gang Gang KOU ◽  
Yi PENG
Keyword(s):  
Decision Making ◽  
Distance Measure ◽  
Programming Model ◽  
Multiple Criteria Decision Making ◽  
Unit Interval ◽  
Multiple Criteria ◽  
Decision Makers ◽  
Basic Unit ◽  
Mathematical Programming Model ◽  
Order Preference

The paper proposes a dynamic fuzzy multiple criteria decision making (DFMCDM) method. The method considers the integrated weight of the decision makers with the subjective and objective preference and the effect of time weight. In the proposed method, a mathematical programming model is used to determine the integrated weight, and a basic unit-interval monotonic (BUM) function based approach is used to calculate the time weight. In addition, a distance measure of membership function is introduced to effectively measure the degree of difference between the alternatives in the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS). Finally, a numerical example is introduced to illustrate the proposed method.

A Method for Multi-Attribute Decision-Making with Complementary Preference Information on Alternatives

2013 ◽  
Vol 658 ◽  
pp. 541-545
Author(s):  
Hong An Zhou
Keyword(s):  
Decision Making ◽  
Goal Programming ◽  
Programming Model ◽  
Preference Relation ◽  
Weighting Method ◽  
Preference Information ◽  
Attribute Weights ◽  
Translation Function ◽  
Goal Programming Model ◽  
Multi Attribute Decision Making

The multi-attribute decision making (MADM) problem, in which the information about attribute weights are known partly and the decision maker (DM) has fuzzy complementary preference relation on alternatives, is investigated in this paper. Firstly, The objective decision-making information based on the subjective fuzzy complementary preference information on alternatives is uniformed by using a translation function. Secondly, a goal programming model is established. The attribute weights are obtained by solving the model, thus the overall values of the alternatives are gained by using the additive weighting method. Based on these values, the ranking priorities or selecting the best on alternatives are processed. The method can sufficiently utilize the objective information of alternatives and meet the subjective requirements of the DM as much as possible, and it is also characterized by simple operation and easy to implement on a computer. Finally, a practical example is illustrated to show the feasibility and availability of the developed method.

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