scholarly journals How different are ranking methods for fuzzy numbers? A numerical study

2013 ◽  
Vol 54 (5) ◽  
pp. 627-639 ◽  
Author(s):  
Matteo Brunelli ◽  
József Mezei
Keyword(s):  
Fuzzy Numbers ◽  
Numerical Study ◽  
Ranking Methods

Planning of a Project with Imprecise Activity Time

2016 ◽  
pp. 505-515
Author(s):  
Sk. Md. Abu Nayeem ◽  
Madhumangal Pal
Keyword(s):  
Fuzzy Numbers ◽  
Critical Path ◽  
Project Evaluation ◽  
Triangular Fuzzy Numbers ◽  
Activity Time ◽  
Ranking Methods ◽  
Mathematical Techniques ◽  
Activity Times

A project where activity times are characterized by intervals or triangular fuzzy numbers is considered in this chapter. Classical project evaluation and review technique (PERT) cannot be directly applied to such projects. Comparison of two intervals or two triangular fuzzy numbers plays an important role in this problem. Ranking methods based on some fuzzy mathematical techniques are discussed and two algorithms for finding the critical path for such a project are given. An illustrative example is also provided.

A New Interval-Valued Intuitionistic Fuzzy Numbers: Ranking Methodology and Application

2018 ◽  
Vol 14 (03) ◽  
pp. 363-381 ◽  
Author(s):  
S. K. Bharati ◽  
S. R. Singh
Keyword(s):  
Fuzzy Numbers ◽  
Optimal Solutions ◽  
Intuitionistic Fuzzy Number ◽  
Ranking Methods ◽  
Intuitionistic Fuzzy Numbers ◽  
Industrial Management ◽  
Intuitionistic Fuzzy ◽  
Interval Valued ◽  
Better Than ◽  
Real World Problems

Ranking of interval-valued intuitionistic fuzzy (IVIF) numbers is a most popular and elegant work in the area of decision-making of several real-world problems. Some limited methods have been presented concerning the ranking of IVIF sets in literature. In the present paper, we generalize the intuitionistic fuzzy (IF) number to interval-valued intuitionistic fuzzy number by defining interval membership and nonmembership functions instead of fixed-valued function and hence it will present uncertain situation better than IF numbers. It may also be applied in data analysis, industrial management, artificial intelligence, forecasting, time series and so on. In this paper, ranking methodology of IVIF numbers is presented, for this first we define the value and ambiguity of IVIF numbers. Proposed ranking method also is compared with existing ranking methods. Further, IVIF numbers are used to capture fuzziness and hesitation in transportation problem (TP), and we propose a new method to find optimal solutions of TP with IVIF number parameters and finally, a numerical example is given to demonstrate the proposed method.

scholarly journals Fuzzy Risk Analysis for a Production System Based on the Nagel Point of a Triangle

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Handan Akyar
Keyword(s):  
Risk Analysis ◽  
Fuzzy Numbers ◽  
New Method ◽  
Economic Systems ◽  
Triangular Fuzzy Numbers ◽  
Ranking Methods ◽  
Analysis Problem ◽  
Centroid Point ◽  
Fuzzy Risk Analysis ◽  
Fuzzy Ranking

Ordering and ranking fuzzy numbers and their comparisons play a significant role in decision-making problems such as social and economic systems, forecasting, optimization, and risk analysis problems. In this paper, a new method for ordering triangular fuzzy numbers using the Nagel point of a triangle is presented. With the aid of the proposed method, reasonable properties of ordering fuzzy numbers are verified. Certain comparative examples are given to illustrate the advantages of the new method. Many papers have been devoted to studies on fuzzy ranking methods, but some of these studies have certain shortcomings. The proposed method overcomes the drawbacks of the existing methods in the literature. The suggested method can order triangular fuzzy numbers as well as crisp numbers and fuzzy numbers with the same centroid point. An application to the fuzzy risk analysis problem is given, based on the suggested ordering approach.

scholarly journals Strategic Management in Marketing: A Game Theoretic Approach

2021 ◽  
Vol 12 (6) ◽  
pp. 2518-2524
Author(s):  
Vinod Jangid, Gaurav Sharma, Ganesh Kumar
Keyword(s):  
Fuzzy Numbers ◽  
Status Quo ◽  
Theoretic Approach ◽  
Ranking Methods ◽  
Matrix Games ◽  
Fuzzy Matrix ◽  
Game Theoretic ◽  
Zero Sum ◽  
Determining Factor ◽  
Game Theoretic Approach

In marketing, a real-world dilemma emerging between two rivals, McDonald's and Burger King, is investigated. Both firms use three strategies: discounted pricing, status quo, and aggressive commercial. In such cases, ambiguity is a determining factor. To deal with confusion in payoffs, octagonal fuzzy numbers are used. To rank fuzzy numbers, the average of odd positions, average of even positions, and quartile deviations are used. To solve the reduced modelled two competitors zero sum fuzzy matrix games, the proposed ranking methods are used. Finally, the findings are compared to current approaches that are quite similar to the proposed approach.

A Method for Analyzing Queues in Fuzzy Environment

2016 ◽  
pp. 1-21
Author(s):  
Thillaigovindan Natesan
Keyword(s):  
Fuzzy Numbers ◽  
Numerical Study ◽  
Compound Poisson Process ◽  
Arrival Rate ◽  
New Method ◽  
Service Rate ◽  
Fuzzy Environment ◽  
Restriction Policy ◽  
Bulk Queue ◽  
Number Of Customers

In this chapter a new method for analyzing queues in fuzzy environment is presented. After explaining the new technique, it is applied to a fuzzy bulk queue with modified Bernoulli vacation and restricted admissible customers. Batches of customers arrive at the system according to a compound Poisson process. All arriving batches are not allowed to enter the system. The restriction policy depends on availability or otherwise of the server. This system is analyzed in fuzzy environment using the new method developed. Some special cases are discussed and a numerical study is also carried out. The new method can be applied to any queuing system in fuzzy environment. In this method the input parameters like arrival rate, service rate, vacation rate etc. are described by fuzzy numbers of specific type (triangular, trapezoidal, quadratic, Gaussian etc.) and the system performance measures like average queue size, average waiting time in the queue, average number of customers in the system are all obtained as fuzzy numbers of the same type, which include the crisp solution.

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