Using the Fuzzy Preference Relations to Measure the Aggregative Risk Degree of Implementing E-Manufacturing

2014 ◽  
Vol 563 ◽  
pp. 351-355
Author(s):  
Hsiu Fen Chen ◽  
Jui Hsiang Tsai
Keyword(s):  
Risk Factors ◽  
Lean Manufacturing ◽  
Manufacturing Industry ◽  
Analytic Hierarchy ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Sustainable Value ◽  
Risk Grade ◽  
Risk Degree ◽  
Fuzzy Preference

Today’s competition in manufacturing industry depends not just on lean manufacturing but also on the ability to provide customers with total solutions and life-cycle costs for sustainable value. This study therefore proposes an analytic hierarchy model to help the administrators understand the critical risk factors influence the E-manufacturing system initiation, and an aggregative risk degree is indicated which risk grade they are in. The importance weights of risk factors and possible occurrence ratings of four risk grade (high-risk, medium-risk, low-risk and none-risk) are determined by using consistent fuzzy preference relations. Keywords: E-manufacturing, risk management, fuzzy preference relations.

scholarly journals On the Analytic Hierarchy Process Structure in Group Decision-Making Using Incomplete Fuzzy Information with Applications

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 609
Author(s):  
Atiq ur Rehman ◽  
Andrii Shekhovtsov ◽  
Nighat Rehman ◽  
Shahzad Faizi ◽  
Wojciech Sałabun
Keyword(s):  
Decision Making ◽  
Fuzzy Information ◽  
Decision Matrix ◽  
Analytic Hierarchy ◽  
Preference Relations ◽  
Fuzzy Preference Relations ◽  
Quantitative Results ◽  
Process Structure ◽  
Hierarchy Process ◽  
Fuzzy Preference

The multi-criteria decision-making (MCDM) problem has a solution whose quality can be affected by the experts’ inclinations. Under essential conditions, the fuzzy MCDM method can provide more acceptable and efficient outcomes to select the best alternatives. This work consists of a consensus-based technique for selecting and evaluating suppliers in an incomplete fuzzy preference relations (IFPRs) environment utilizing TL-transitivity (Lukasiewicz transitivity). The suggested method is developed based on the criteria of the Analytical Hierarchy Process (AHP) Fframework, and the decision matrix is construtced using consistent fuzzy preference relations (FPRs). We use the symmetrical decisional matrix approach. A variety of numerical explanations and an analysis of quantitative results illustrate the suggested methodology’s logic and effectiveness.

Decision making based on intuitionistic fuzzy preference relations with additive approximate consistency

2020 ◽  
Vol 39 (3) ◽  
pp. 4041-4058
Author(s):  
Fang Liu ◽  
Xu Tan ◽  
Hui Yang ◽  
Hui Zhao
Keyword(s):  
Decision Making ◽  
Decision Makers ◽  
Real Interval ◽  
Preference Relations ◽  
Intuitionistic Fuzzy ◽  
Proposed Model ◽  
Fuzzy Preference Relations ◽  
Novel Concept ◽  
Fuzzy Weights ◽  
Fuzzy Preference

Intuitionistic fuzzy preference relations (IFPRs) have the natural ability to reflect the positive, the negative and the non-determinative judgements of decision makers. A decision making model is proposed by considering the inherent property of IFPRs in this study, where the main novelty comes with the introduction of the concept of additive approximate consistency. First, the consistency definitions of IFPRs are reviewed and the underlying ideas are analyzed. Second, by considering the allocation of the non-determinacy degree of decision makers’ opinions, the novel concept of approximate consistency for IFPRs is proposed. Then the additive approximate consistency of IFPRs is defined and the properties are studied. Third, the priorities of alternatives are derived from IFPRs with additive approximate consistency by considering the effects of the permutations of alternatives and the allocation of the non-determinacy degree. The rankings of alternatives based on real, interval and intuitionistic fuzzy weights are investigated, respectively. Finally, some comparisons are reported by carrying out numerical examples to show the novelty and advantage of the proposed model. It is found that the proposed model can offer various decision schemes due to the allocation of the non-determinacy degree of IFPRs.

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