scholarly journals Fuzzy Attribute Expansion Method for Multiple Attribute Decision-Making with Partial Attribute Values and Weights Unknown and Its Applications

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 717
Author(s):  
Jinbao Zhuo ◽  
Weifeng Shi ◽  
Ying Lan
Keyword(s):  
Decision Making ◽  
Spline Interpolation ◽  
Expansion Method ◽  
Fuzzy Comprehensive Evaluation ◽  
Multiple Attribute Decision Making ◽  
Clustering Methods ◽  
Attribute Weights ◽  
Multiple Attribute ◽  
Interpolation Technique ◽  
Spline Interpolation Technique

In the real world, there commonly exists types of multiple attribute decision-making (MADM) problems with partial attribute values and weights totally unknown. Symmetry among some attribute information that is already known and unknown, and symmetry between the pure attribute set and fuzzy attribute membership set can be a considerable way to solve this type of MADM problem. In this paper, a fuzzy attribute expansion method is proposed to solve this type of problem based on two key techniques: the spline interpolation technique and the attribute weight reconfiguration technique, which are respectively used for the determination of attribute values and the reconfiguration of attribute weights. The spline interpolation technique to expand attribute values can enhance the performance of some regression methods and clustering methods by the comparisons between the results of these methods dealing with practical cases with and without the application of the technique, which further illustrates the effectiveness of this technique. For MADM problems with partial attribute values and weights totally unknown, compared with traditional fuzzy comprehensive evaluation (FCE), FCE with the application of fuzzy attribute expansion method can obtain results more similar with the ones when all attribute values and weights are known, which is proved by the practical power quality evaluation example.

scholarly journals Multiple Attribute Decision Making Based on Cross-Evaluation with Uncertain Decision Parameters

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Tao Ding ◽  
Liang Liang ◽  
Min Yang ◽  
Huaqing Wu
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Service Companies ◽  
Attribute Weights ◽  
Research Fields ◽  
Multiple Attribute ◽  
Single Attribute ◽  
The Stability ◽  
The City

Multiple attribute decision making (MADM) problem is one of the most common and popular research fields in the theory of decision science. A variety of methods have been proposed to deal with such problems. Nevertheless, many of them assumed that attribute weights are determined by different types of additional preference information which will result in subjective decision making. In order to solve such problems, in this paper, we propose a novel MADM approach based on cross-evaluation with uncertain parameters. Specifically, the proposed approach assumes that all attribute weights are uncertain. It can overcome the drawback in prior research that the alternatives’ ranking may be determined by a single attribute with an overestimated weight. In addition, the proposed method can also balance the mean and deviation of each alternative’s cross-evaluation score to guarantee the stability of evaluation. Then, this method is extended to a more generalized situation where the attribute values are also uncertain. Finally, we illustrate the applicability of the proposed method by revisiting two reported studies and by a case study on the selection of community service companies in the city of Hefei in China.

Extending ARAS with Integration of Objective Attribute Weighting under Spherical Fuzzy Environment

2021 ◽  
pp. 1-26
Author(s):  
Sait Gül
Keyword(s):  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Decision Makers ◽  
Owa Operator ◽  
Attribute Weighting ◽  
Preference Domain ◽  
Attribute Weights ◽  
Fuzzy Environment ◽  
Multiple Attribute ◽  
Collection Time

Various fuzzy sets have been developed in the recent years to model the uncertainty in judgments. Spherical fuzzy set (SFS) concept is one of these developments. It can provide an extensive preference domain for decision-makers by allowing them to state their hesitancy more explicitly. The peculiarity of SFS is that the squared sum of membership, nonmembership, and hesitancy degrees should be between 0 and 1 while each is independently defined in [0, 1]. In this study, ARAS as one of the most applied multiple attribute decision-making approaches is extended into a spherical fuzzy environment. Entropy-based and OWA operator-based objective attribute weights are also integrated with the newly proposed spherical fuzzy ARAS for coping with the drawbacks of subjective weighting such as longer data collection time and manipulation risk. The applicability of the proposition is shown in a hypothetical example of a product design problem and its robustness is shown by a comparative analysis.

PROJECTION METHOD FOR UNCERTAIN MULTI-ATTRIBUTE DECISION MAKING WITH PREFERENCE INFORMATION ON ALTERNATIVES

2004 ◽  
Vol 03 (03) ◽  
pp. 429-434 ◽  
Author(s):  
ZESHUI XU ◽  
QINGLI DA
Keyword(s):  
Decision Making ◽  
Projection Method ◽  
Multiple Attribute Decision Making ◽  
Projection Model ◽  
Preference Information ◽  
Objective Information ◽  
Attribute Weights ◽  
Multiple Attribute ◽  
Multi Attribute Decision Making

In this paper, we study the uncertain multiple attribute decision making problems with preference information on alternatives (UMADM-PIA, for short), in which the information on attribute weights is not precisely known, but value ranges can be obtained. A projection method is proposed for the UMADM-PIA. To reflect the decision maker's preference information, a projection model is established to determine the weights of attributes, and then to select the most desirable alternative(s). The method can reflect both the objective information and the decision maker's subjective preferences, and can also be performed on computer easily. Finally, an illustrative example is given to verify the proposed method and to demonstrate its feasibility and practicality.

Multiple Criteria Decision Making Research of Flight Control System Design

2013 ◽  
Vol 648 ◽  
pp. 334-343
Author(s):  
Rui Nie ◽  
Bai Nan Zhang ◽  
Bao Ning Liu ◽  
Wei Guo Zhang ◽  
Jing Yuan
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Flight Control ◽  
Multiple Criteria Decision Making ◽  
Multiple Attribute Decision Making ◽  
Multiple Objective ◽  
Flight Control System ◽  
Attribute Weights ◽  
Multiple Attribute ◽  
Multiple Objective Decision Making

Flight control system; multiple criteria decision making; multiple objective decision making; multiple attribute decision making; neural network; mean impact value index. Abstract. It is complex and difficult to tune the parameters of the flight controller. To solve such problem, a multiple objective decision making (MODM) method by using the reference model which is built based on the criteria, is proposed. In order to resolve defects of the multiple attribute decision making (MADM) that the arbitrary of the subjective attribute weights and ignoring the objective message of the objective attribute weights, a subjective attribute weights based on the BP neural network by using the MIV (mean impact value) index is proposed. Finally, a combining method based on the TOPSIS is used to give the final attribute weights. The simulation results show that the method could obtain a set of trade-off solutions which satisfy the requirements of the MODM and could tune the controller effectively.

scholarly journals New Distance Measures for Dual Hesitant Fuzzy Sets and Their Application to Multiple Attribute Decision Making

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 191
Author(s):  
Wang ◽  
Li ◽  
Zhang ◽  
Han
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Multiple Attribute Decision Making ◽  
Distance Measures ◽  
Hesitant Fuzzy Set ◽  
Hesitant Fuzzy Sets ◽  
Attribute Weights ◽  
Multiple Attribute

Multiple attribute decision making (MADM) is full of uncertainty and vagueness due to intrinsic complexity, limited experience and individual cognition. Representative decision theories include fuzzy set (FS), intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS), dual hesitant fuzzy set (DHFS) and so on. Compared with IFS and HFS, DHFS has more advantages in dealing with uncertainties in real MADM problems and possesses good symmetry. The membership degrees and non-membership degrees in DHFS are simultaneously permitted to represent decision makers’ preferences by a given set having diverse possibilities. In this paper, new distance measures for dual hesitant fuzzy sets (DHFSs) are developed in terms of the mean, variance and number of elements in the dual hesitant fuzzy elements (DHFEs), which overcomes some deficiencies of the existing distance measures for DHFSs. The proposed distance measures are effectively applicable to solve MADM problems where the attribute weights are completely unknown. With the help of the new distance measures, the attribute weights are objectively determined, and the closeness coefficients of each alternative can be objectively obtained to generate optimal solution. Finally, an evaluation problem of airline service quality is conducted by using the distance-based MADM method to demonstrate its validity and applicability.

MODELS FOR MULTIPLE ATTRIBUTE DECISION MAKING WITH INTUITIONISTIC FUZZY INFORMATION

2007 ◽  
Vol 15 (03) ◽  
pp. 285-297 ◽  
Author(s):  
Z. S. XU
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Membership Function ◽  
Fuzzy Set ◽  
Intuitionistic Fuzzy Set ◽  
Multiple Attribute Decision Making ◽  
Fuzzy Information ◽  
Attribute Weights ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute

The intuitionistic fuzzy set (IFS) characterized by a membership function and a non-membership function, was introduced by Atanassov [K. Atanassov, "Intuitionistic fuzzy sets", Fuzzy Sets and Systems 20 (1986) 87–96] as a generalization of Zadeh' fuzzy set [L. A. Zadeh, "Fuzzy Sets", Information and Control 8 (1965) 338–353] to deal with fuzziness and uncertainty. In this paper, we investigate the multiple attribute decision making (MADM) problems, in which the information about attribute weights is incomplete, and the attribute values are expressed in intuitionistic fuzzy numbers (IFNs). We first define the concept of intuitionistic fuzzy ideal solution (IFIS), and then, based on the IFIS and the distance measure, we establish some optimization models to derive the attribute weights. Furthermore, based on the developed models, we develop some procedures for the rankings of alternatives under different situations, and extend the developed models and procedures to handle the MADM problems with interval-valued intuitionistic fuzzy information. Finally, we give some illustrative examples to verify the effectiveness and practicability of the developed models and procedures.

STATIC STRATEGIC GAME APPROACH FOR MULTIPLE ATTRIBUTE DECISION MAKING PROBLEMS WITHOUT WEIGHT INFORMATION

2011 ◽  
Vol 20 (03) ◽  
pp. 577-588 ◽  
Author(s):  
LIHUA ZHOU ◽  
WEIYI LIU ◽  
LIZHEN WANG
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Multiple Attribute Decision Making ◽  
Strategic Game ◽  
Arithmetic Average ◽  
Attribute Weights ◽  
A Value ◽  
Multiple Attribute ◽  
Ranking Of Alternatives ◽  
The One

In multiple attribute decision making (MADM) problems, it is usual that no single alternative works best for all performance attributes, so it is difficult to select the best from among multiple available alternatives, especially in the situation that the attribute weights are completely unknown. This research propose a game theory-based approach (GMADM) which incorporates static strategic game theory into MADM problems to derive the attribute weights, and then utilize the weight arithmetic average (WAA) operator to aggregate the attribute values corresponding to each alternative and rank alternatives by means of aggregated information. In GMADM, each attribute is regarded as a player taking part in the game, and the player's strategy is to select a value from interval [0,1] to assign corresponding attribute weight, and the player's utility is defined as the agreement between the ranking of alternatives determined by the aggregated information and the one determined by the attribute values. When the game is in equilibrium status, the strategy profile is the best attribute weights which make each player have good utilities. Moreover, the equilibrium solution of game and the resolution method for the MADM problem without weight information have also been developed. Finally, the result of proposed approach for a practical MADM problem and its comparisons with one of other methods are given.

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