An integrated dynamic intuitionistic fuzzy MADM approach for personnel promotion problem

Kybernetes ◽  
2015 ◽  
Vol 44 (10) ◽  
pp. 1422-1436 ◽  
Author(s):  
Ozkan Bali ◽  
Metin Dagdeviren ◽  
Serkan Gumus
Keyword(s):  
Fuzzy Set ◽  
Success Factors ◽  
Intuitionistic Fuzzy Set ◽  
Senior Managers ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Dynamic Evaluation ◽  
Intuitionistic Fuzzy ◽  
Qualified Personnel ◽  
Fuzzy Madm

Purpose – One of the key success factors for an organization is the promotion of qualified personnel for vacant positions. Especially, the promotion of middle and senior managers play an important role in terms of organization’s success. In personnel promotion problem in which the candidates are nominated within the organization and they have been working for a specific period of time and are known in their organization, the candidates should be evaluated based on their recent as well as past performances to make right selection for the vacant position. For this reason, the purpose of this paper is to propose an integrated dynamic multi-attribute decision-making (MADM) model based on intuitionistic fuzzy set for solving personnel promotion problem. Design/methodology/approach – The proposed model integrates analytic hierarchy process (AHP) technique and the dynamic evaluation by intuitionistic fuzzy operator for personnel promotion. AHP is employed to determine the weight of attributes based on decision maker’s opinions, and the dynamic operator is utilized to aggregate evaluations of candidates for different years. Atanassov’s intuitionistic fuzzy set theory is utilized to represent uncertainty and vagueness in MADM process. Findings – A numerical example is presented to show the applicability of the proposed method for personnel promotion problem and a sensitivity analysis is conducted to demonstrate efficiency of dynamic evaluation. The findings indicate that the varying weights of years employed determined the best candidate for promotion. Originality/value – The novelty of this study is defining personnel promotion as a MADM problem in the literature for the first time and proposing an integrated dynamic intuitionistic fuzzy MADM approach for the solution, in which the candidates are evaluated at different years.

scholarly journals AN APPROACH FOR MADM PROBLEMS WITH INTERVAL-VALUED INTUITIONISTIC FUZZY SETS BASED ON NONLINEAR FUNCTIONS

2015 ◽  
Vol 22 (3) ◽  
pp. 336-356 ◽  
Author(s):  
Jian WU ◽  
Qingwei CAO ◽  
Hui LI
Keyword(s):  
Score Function ◽  
Multiple Attribute Decision Making ◽  
Nonlinear Functions ◽  
Intuitionistic Fuzzy Numbers ◽  
Attribute Weights ◽  
Intuitionistic Fuzzy ◽  
Multiple Attribute ◽  
Fuzzy Madm ◽  
Optimized Model ◽  
Interval Valued

This paper investigates an approach for multiple attribute decision making (MADM) problems with interval-valued intuitionistic fuzzy numbers (IVIFNs). To do that, the nonlinear score, accuracy and hesitation functions of IVIFNs are developed based on the normal distribution. The novelty of these nonlinear functions is that they have an additional variance value, which can have more information to rank IVIFNs than Xu and Chen’s score function and Ye’s accuracy function. Based on these nonlinear functions, a ranking method for IVIFNs is proposed. Furthermore, a nonlinearly optimized model is proposed to obtain attribute weights by integrating these nonlinear functions. Then, we develop an approach for interval-valued intuitionistic fuzzy MADM programs in which two cases are considered: the attribute weight information is known and particularly known. In the end, we apply the proposed approach to select green supplier.

Minimum Weighted Minkowski Distance Power Models for Intuitionistic Fuzzy Madm with Incomplete Weight Information

2017 ◽  
Vol 16 (05) ◽  
pp. 1387-1408 ◽  
Author(s):  
Deng-Feng Li ◽  
Shu-Ping Wan
Keyword(s):  
A Priori ◽  
Real Life ◽  
Information Structures ◽  
Minkowski Distance ◽  
Intuitionistic Fuzzy ◽  
Multi Objective Programming ◽  
Power Models ◽  
Fuzzy Madm ◽  
Incomplete Weight ◽  
Incomplete Weight Information

Owing to more vague concepts frequently represented in decision data, intuitionistic fuzzy sets (IFSs) are more flexibly used to model real-life decision situations. At the same time, with ever increasing complexity in many decision situations in reality, there are often some challenges for a decision maker to provide complete attribute preference information, i.e., the weights may be completely unknown or partially known. The aim of this paper is to develop an effective method for solving intuitionistic fuzzy multi-attribute decision making (MADM) problems with incomplete weight information. In this method, ratings of alternatives on attributes are expressed with IFSs. The multi-objective programming models are established to calculate unknown weights by using weight information partially known a priori. The derived minimum weighted Minkowski distance power models are used to determine the unknown weights and to generate the ranking order of the alternatives simultaneously. The proposed models are easily extended to intuitionistic fuzzy MADM problems with different weight information structures. An example of the supplier selection problem is examined to demonstrate applicability and flexibility of the proposed models and method.

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