Advances in Fuzzy Systems
Volume 2018 (2018), Article ID 9578270, 7 pages
https://doi.org/10.1155/2018/9578270

Research Article

A Direct Method to Compare Bipolar LR Fuzzy Numbers

Reza Ghanbari,¹ Khatere Ghorbani-Moghadam,² and Nezam Mahdavi-Amiri²

¹Faculty of Mathematical Sciences, Department of Applied Mathematics, Ferdowsi University of Mashhad, Mashhad, Iran
²Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran

Correspondence should be addressed to Reza Ghanbari; rghanbari@um.ac.ir

Received 12 November 2017; Accepted 13 February 2018; Published 14 March 2018

Academic Editor: Mehmet Onder Efe

Copyright © 2018 Reza Ghanbari et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

We propose a new method for ordering bipolar fuzzy numbers. In this method, for comparison of bipolar LR fuzzy numbers, we use an extension of Kerre’s method being used in ordering of unipolar fuzzy numbers. We give a direct formula to compare two bipolar triangular fuzzy numbers in operations, making the process useful for many optimization algorithms. Also, we present an application of bipolar fuzzy number in a real life problem.

1. Introduction

Fuzzy sets are useful mathematical structures to represent a collection of objects whose boundary is vague. There is a bipolar judgmental thinking on a negative side as well as a positive side in a human decision making (see [1]). This domain has recently invoked many interesting research topics such as algebra [2, 3], psychology [4], image processing [5], and human reasoning [6].

Zhang [7] initiated the concept of bipolar fuzzy sets as a generalization of fuzzy sets. He defined bipolar fuzzy sets as an extension of fuzzy sets whose membership degree range is . Also, Zhang [8] proposed a family of bipolar models. Zhou and Li [1] presented the concepts of bipolar fuzzy -ideals and normal bipolar fuzzy -ideals. Then, they investigated characterizations of bipolar fuzzy -ideals by means of positive -cut, negative -cut, homomorphism, and equivalence relation.

Akram [9, 10] used the concept of bipolar fuzzy sets in graph theory. Talebi and Rashmanlou [11] presented some properties of the self-complement and self-weak complement bipolar fuzzy graphs. Tahmasbpour and Borzooei [12] introduced two different approaches corresponding to chromatic number of a bipolar fuzzy graph. They computed total chromatic number based on -cut and -cut of a bipolar fuzzy graph with the edges and vertices both being bipolar fuzzy sets.

Comparison of two fuzzy numbers is a major computational task in various algorithms. Kerre’s method [13] for comparison of two unipolar fuzzy numbers is a well-known method in ordering unipolar fuzzy numbers. In this method, first, using the extension principle or -cut computations, the fuzzy maximum of two fuzzy numbers is computed and then, using the Hamming distance, the comparison is carried out. Inspired by Kerre’s method for comparison of two unipolar fuzzy numbers, we develop a method for comparison of two bipolar fuzzy numbers.

Here, we review the fundamental notions of bipolar fuzzy sets and Kerre’s method for unipolar numbers in Section 2. In Section 3, we propose an extension of Kerre’s method for bipolar fuzzy numbers and give a direct formula to compare two bipolar triangular fuzzy numbers. In Section 4, we present an application of bipolar fuzzy numbers in a real life problem. We conclude in Section 5.

2. Preliminaries

Here, we give some necessary definitions and new results on bipolar fuzzy set theory.

Definition 1 (see [7]). Let be a nonempty set. A bipolar fuzzy set in is an object having the form

where and .

Definition 2 (see [2]). Let be a bipolar-valued fuzzy set and . The sets and are, receptively, called the positive -cut of and the negative -cut of . For every , the set

is called the -cut of .

We now define a bipolar triangular fuzzy number.

Definition 3. A bipolar triangular fuzzy number is defined as a quadruple with positive and negative membership functions and as follows:

where and are, respectively, the membership functions of positive and negative polars (see Figure 1).

Figure 1: Triangular bipolar fuzzy number .

Proposition 4. Let and be two bipolar fuzzy numbers. One has the following results:

Proof. The results are proved by using the extension principle.

Note 1. We denote the set of all bipolar triangular fuzzy numbers by .

2.1. Kerre’s Method to Compare Two Unipolar Fuzzy Numbers

In Kerre’s method [13], first, using the extension principle or -cut computations, the fuzzy maximum of two fuzzy numbers is computed and then, using the Hamming distance, the comparison is carried out.

Definition 5. Based on Kerre’s method, one says if and only if

where and are defined as in Definitions 6 and 7.

Definition 6 (see [13]). The fuzzy max between two fuzzy numbers and is

Definition 7 (see [13]). The Hamming distance between two fuzzy numbers and is

It has been shown that Kerre’s “” is transitive [13].

3. Proposed Method for Comparison of Two Bipolar Fuzzy Numbers

Here, we at first intend to extend Kerre’s method [13] for comparison of two bipolar LR fuzzy numbers. We need to find fuzzy maximum for positive and negative polars.

Proposition 8. Let and be two bipolar LR fuzzy numbers. Then,

where and are, receptively, fuzzy maximum on positive and negative polars.

Proof. These are proved by using the extension principle directly.

Let and be two arbitrary bipolar LR fuzzy numbers and let and . If then ; else .

3.1. Modified Kerre’s Method for Comparison of Two Bipolar Fuzzy Numbers

To compute the fuzzy maximum of two bipolar LR fuzzy numbers, we need to compute fuzzy maximum for each polar as given by (8). Here, we first give a result that leads to a direct and efficient formula to compute the fuzzy maximum of two arbitrary bipolar LR fuzzy numbers. Then, applying the direct formula for , we modify Kerre’s method to compare two bipolar LR fuzzy numbers. Next, using our modified Kerre’s method for comparing of two bipolar LR fuzzy numbers, we establish some simple formulas for comparison of bipolar triangular fuzzy numbers.

Define

Lemma 9. Suppose and are two bipolar LR fuzzy numbers. Then, for positive polar and for all , one has

and, for negative polar and for all , one has

Proof. First, we establish (10). Consider . Without loss of generality, suppose . Then, we need to show

There are two cases as described below.
(1) Case . Since ,   and so

(2) Case . Since ,   and so

Therefore, from (13) and (14), we have

and the proof is complete. In a similar manner, we can establish (11).

Next, in Theorem 10, we give a direct formula to compute the maximum of two arbitrary bipolar LR fuzzy numbers.

Theorem 10. Suppose and are two arbitrary bipolar LR fuzzy numbers and and . For , one has

Proof. First, we prove . Let . According to (6) and , we consider three cases.
(1) Case . Since the left side of the membership function of is an increasing function, we have

Then, we conclude from and that

Therefore,

But, , and so we have

to complete the proof for the case.
(2) Case . Without loss of generality, suppose that . Then, according to Lemma 9, it is clear that

Now, we show

We know that . Then, we have

But

and thus

Therefore, (22) is established and from (21) and (22), and the proof of the case is complete.
(3) Case . Since and are increasing functions on , we have

Thus, we have

and the proof is complete. Similarly, we can prove .

Definition 11. Suppose and are two arbitrary bipolar LR fuzzy numbers and let

If then ; else .

Theorem 12. Let and be two LR fuzzy numbers. If and , then

where is defined as in (28).

Proof. From Theorem 10 and Definition 7, we have

Thus, we have

Also, for negative polar we have

Thus, we have

And therefore

We can rewrite (29) as

Note that when and are bipolar triangular fuzzy numbers, we can simplify (35), and then the computation of can be simplified, if we can compute the intersection of and . Since each polar of and is a triangular fuzzy number, below,

is the length of the intersection point of and for the positive polar, and below,

is the length of the intersection point of and for the negative polar. We have the following proposition giving a reformulation of (29).

Proposition 13. Let and be two bipolar triangular fuzzy numbers with ,  ,  , and , where and are defined by (36) and (37). Then, one has

Note that the sign of is adequate to determine or . But, for bipolar LR fuzzy number linear programming problems, in some situations we need to compute the exact value of .

Example 14. Let and be two bipolar triangular fuzzy numbers. Then, ,  ,  ,  ,  ,  ,  , and are

According to (38), we have and this means .

Next, we give some corollaries, the proofs of which are straightforward.

Corollary 15. Let and be two bipolar triangular fuzzy numbers. If , then

Corollary 16. Let and be two bipolar triangular fuzzy numbers. If and , where , with as defined by (36), then

Corollary 17. Let and be two bipolar triangular fuzzy numbers. If and , where , with as defined by (36), and , with as defined by (37), then

A property of (42) is that for two bipolar triangular fuzzy numbers such as and we have , where .

4. Application of Proposed Method in a Real Life Problem

Akram [10] studied an application of bipolar fuzzy sets in graph theory. He used bipolar fuzzy set for a social group. Here, we demonstrate an application of bipolar fuzzy number in maximum weighted matching problem; matching problem has some applications in various fields such as scheduling [14] and network [15] problems. We consider each vertex to be person and weight of each edge between two vertices be the influence of each person (vertex) to another person. In general, influence can be positive or negative. Suppose is an arbitrary weighted graph, where is the vertex set of and is the edge set of . The maximum weighted matching problem is

where , if two persons and are matched to each other, and , otherwise, and is the weight of edge (giving the influence of one person to another person), considered as a bipolar fuzzy number, since influence of a person cannot always be positive. The aim is to match every person to another person so that they have a stable relation.

5. Conclusions

We proposed a new efficient method for ordering bipolar fuzzy numbers. In this method, for comparison of bipolar LR fuzzy numbers, we used an extension of Kerre’s method used in ordering of unipolar fuzzy numbers. In our proposed method, we provided a formula to compare two bipolar triangular fuzzy numbers in operations, making the process useful for optimization algorithms. Also, we presented an application of bipolar fuzzy number in a real life problem.

Conflicts of Interest

The authors declare that there are no conflicts of interest regarding the publication of this paper.

Acknowledgments

The first author acknowledges the Research Council of Ferdowsi University of Mashhad, the second author would like to thank Dr. Reza Ghanbari and acknowledge the Department of Applied Mathematics at Ferdowsi University of Mashhad for the hospitality during her sabbatical leave there, and the third author acknowledges the Research Council of Sharif University of Technology for supporting this work.

References

M. Zhou and S. Li, “Applications of bipolar fuzzy theory to hemirings,” International Journal of Innovative Computing, Information and Control, vol. 10, no. 2, pp. 767–781, 2014.

K. J. Lee, “Bipolar fuzzy subalgebras and bipolar fuzzy ideals of BCK/BCI-algebras,” Bulletin of the Malaysian Mathematical Sciences Society, vol. 32, no. 3, pp. 361–373, 2009.

A. B. Saeid, “Bipolar-valued fuzzy BCK/BCI-algebras,” World Applied Sciences Journal, vol. 7, pp. 1404–1411, 2009.

J. T. Cacioppo, W. L. Gardner, and G. G. Berntson, “Beyond bipolar conceptualizations and measures: The case of attitudes and evaluative space,” Personality and Social Psychology Review, vol. 1, no. 1, pp. 3–25, 1997.

I. Bloch, “Mathematical morphology on bipolar fuzzy sets: general algebraic framework,” International Journal of Approximate Reasoning, vol. 53, no. 7, pp. 1031–1060, 2012.

R. D. S. Neves and P. Livet, “Bipolarity in human reasoning and affective decision making,” International Journal of Intelligent Systems, vol. 23, no. 8, pp. 898–922, 2008.

W.-R. Zhang, “Bipolar fuzzy sets,” in Proceedings of the 1998 IEEE International Conference on Fuzzy Systems, pp. 835–840, Anchorage, Alaska, USA, May 1998.

W.-R. Zhang, “NPN fuzzy sets and NPN qualitative algebra: A computational framework for bipolar cognitive modeling and multiagent decision analysis,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 26, no. 4, pp. 561–574, 1996.

M. Akram, “Bipolar fuzzy graphs,” Information Sciences, vol. 181, no. 24, pp. 5548–5564, 2011.

M. Akram, “Bipolar fuzzy graphs with applications,” Knowledge-Based Systems, vol. 39, pp. 1–8, 2013.

A. A. Talebi and H. Rashmanlou, “Complement and isomorphism on bipolar fuzzy graphs,” Fuzzy Information and Engineering, vol. 6, no. 4, pp. 505–522, 2014.

A. Tahmasbpour and R. A. Borzooei, “Chromatic number of bipolar fuzzy graphs,” Journal of Applied Mathematics & Informatics, vol. 34, no. 1-2, pp. 49–60, 2016.

J. J. Buckely and L. J. Jowers, “Monte carlo method in fuzzy optimization,” in Studies in Fuzziness and Soft Computing, 2007.

C. E. Bell, “Weighted matching with vertex weights: An application to scheduling training sessions in NASA space shuttle cockpit simulators,” European Journal of Operational Research, vol. 73, no. 3, pp. 443–449, 1994.

X. Yao, D. Gong, P. Wang, and L. Chen, “Multi-objective optimization model and evolutional solution of network node matching problem,” Physica A: Statistical Mechanics and its Applications, vol. 483, pp. 495–502, 2017.