Dynamic multi-attribute decision making model with grey number evaluations

The aim of this paper is to explore dynamic multi-attribute decision making (DMADM) problems in which the decision making information of alternatives is collected at different stages. Firstly, the area closeness degree is applied in normalizing the raw data. Secondly, the weights of different stages are determined by according to the principle of new information priority. The technique for preference by similarity to ideal solution (TOPSIS) is improved to aggregate the information from different stages. Finally, the example is illustrated to demonstrate the practicality and effectiveness of the proposed methods.

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Decision Method of Optimal Investment Enterprise Selection under Uncertain Information Environment

International Journal of Fuzzy System Applications ◽

10.4018/ijfsa.2015010102 ◽

2015 ◽

Vol 4 (1) ◽

pp. 33-42 ◽

Cited By ~ 5

Author(s):

Xiaoyong Liao

Keyword(s):

Decision Making ◽

Optimal Investment ◽

Information Environment ◽

Investment Risk ◽

Uncertain Information ◽

Fuzzy Information ◽

Selection Decision ◽

Deviation Degree ◽

Grey Number ◽

Decision Making Model

To select an optimal investment enterprise is the key to effectively reduce the investment risk for an investment company. In this paper, the author studies the problem of optimal investment enterprise selection decision under uncertain information environment (fuzzy information and grey information coexist), and present a fuzzy grey multi-attribute group decision making model to select the optimal investment enterprise. In this model, the author defines the concept and operations of fuzzy grey number, and present a ranking method based on fuzzy grey deviation degree to rank the alternative investment enterprises. The author also gives an application example of selecting optimal investment enterprise to highlight the implementation, availability, and feasibility of the proposed decision making model.

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Dynamic Multi-attribute Decision Making model based on intuitionistic trapezoidal fuzzy numbers

2013 6th International Conference on Information Management, Innovation Management and Industrial Engineering ◽

10.1109/iciii.2013.6703641 ◽

2013 ◽

Author(s):

Yuwen Shi

Keyword(s):

Decision Making ◽

Fuzzy Numbers ◽

Trapezoidal Fuzzy Numbers ◽

Model Based ◽

Multi Attribute Decision Making ◽

Decision Making Model

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Selecting the Most Suitable Method for Extracting Construction Materials in Iran Through the Fuzzy Multi-attribute Decision-Making Model

Journal of The Institution of Engineers (India) Series D ◽

10.1007/s40033-020-00239-w ◽

2020 ◽

Author(s):

Alireza Afradi ◽

Iraj Alavi ◽

Mahdieh Moslemi

Keyword(s):

Decision Making ◽

Construction Materials ◽

Suitable Method ◽

Multi Attribute Decision Making ◽

Decision Making Model

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A two-stage multi-attribute decision-making model for selecting appropriate locations of waste transfer stations in urban centers

Waste Management ◽

10.1016/j.wasman.2020.05.024 ◽

2020 ◽

Vol 114 ◽

pp. 80-88 ◽

Cited By ~ 1

Author(s):

Vinay Yadav ◽

Pradip P. Kalbar ◽

Subhankar Karmakar ◽

A.K. Dikshit

Keyword(s):

Decision Making ◽

Urban Centers ◽

Two Stage ◽

Multi Attribute Decision Making ◽

Waste Transfer ◽

Transfer Stations ◽

Decision Making Model

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Grey relational decision making model of three-parameter interval grey number based on AHP and DEA

Grey Systems Theory and Application ◽

10.1108/gs-10-2018-0049 ◽

2019 ◽

Vol 10 (1) ◽

pp. 25-37

Author(s):

Bingjun Li ◽

Xiaoxiao Zhu

Keyword(s):

Decision Making ◽

Analytic Hierarchy ◽

Content Type ◽

Agriculture Industry ◽

Parameter Interval ◽

Grey Number ◽

Index Weight ◽

Decision Making Model ◽

Grey Relational ◽

Grey Relation

Purpose The purpose of this paper is to put forward the grey relational decision-making model of three-parameter interval grey number based on Analytic Hierarchy Process (AHP) and Data Envelopment Analysis (DEA), based on the previous study of grey relational decision-making model, and it considers the advantages of the decision-making schemes and the subjective preferences of decision makers. Design/methodology/approach First of all, through AHP, the preference of each index is analyzed and the index weight is determined. Second, the DEA model is adopted to obtain the index weight from the perspective of the most beneficial to each scheme and objectively reflect the advantages of different schemes. Then, assign the comprehensive weights to each index of the grey relational decision-making model of three-parameter interval grey number, and calculate the grey relation degree of each scheme to rank the schemes. Findings The effectiveness of the model is proved by an example of carrier aircraft selection. Practical implications The applicability of this model is analyzed by taking carrier aircraft selection as an example. In fact, this model can also be widely used in agriculture, industry, economy, society and other fields. Originality/value In this paper, the combination of AHP and DEA is used to determine the index weight. Based on which, the grey relation degree under the three-parameter interval grey number is calculated. It intended the application space of the grey relational decision-making model.

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