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Mathematical Problems in Engineering
Volume 2017, Article ID 9376531, 8 pages
https://doi.org/10.1155/2017/9376531
Research Article

A New Method of Multiattribute Decision-Making Based on Interval-Valued Hesitant Fuzzy Soft Sets and Its Application

1College of Science, Southwest Petroleum University, Chengdu 610500, China
2School of Information and Computation Science, Chengdu Technological University, Chengdu 610500, China

Correspondence should be addressed to Yan Yang; moc.qq@08788124

Received 11 April 2017; Revised 9 June 2017; Accepted 19 June 2017; Published 30 July 2017

Academic Editor: Franck Massa

Copyright © 2017 Yan Yang 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

Combining interval-valued hesitant fuzzy soft sets (IVHFSSs) and a new comparative law, we propose a new method, which can effectively solve multiattribute decision-making (MADM) problems. Firstly, a characteristic function of two interval values and a new comparative law of interval-valued hesitant fuzzy elements (IVHFEs) based on the possibility degree are proposed. Then, we define two important definitions of IVHFSSs including the interval-valued hesitant fuzzy soft quasi subset and soft quasi equal based on the new comparative law. Finally, an algorithm is presented to solve MADM problems. We also use the method proposed in this paper to evaluate the importance of major components of the well drilling mud pump.

1. Introduction

In 1965, Zadeh [1] introduced the notion of fuzzy set. After that, a variety of extended fuzzy set theories have been proposed such as intuitionistic fuzzy set [2], interval-valued intuitionistic fuzzy set [3], hesitant fuzzy set [4, 5], and interval-valued hesitant fuzzy set [6]. In 1999, Molodtsov [7] firstly proposed soft set theory, which has been widely used in many different areas such as decision analysis [8] and military research [9]. With diversity and complexity of practical problems, the original theories are inapplicable to solve these problems. Maji et al. [10] firstly explored fuzzy soft set, which is a more general notion combining fuzzy set and soft set. Some extensions of fuzzy soft set theory have also been presented such as interval-valued fuzzy soft set [11], intuitionistic fuzzy soft set [12], and interval-valued intuitionistic fuzzy soft set [13]. In 2015, Peng and Yang [14] proposed interval-valued hesitant fuzzy soft set (IVHFSS) combining interval-valued hesitant fuzzy set and soft set.

In the aspect of the research works of MADM problems, Roy and Maji [15] presented a method according to a comparison table from fuzzy soft sets. Kong et al. [16] modified Roy and Maji’s algorithm to give a novel one, which is based on the comparison of choice values of different objects. Feng et al. [17] proposed a new approach by using level soft sets to solve MADM problems based on fuzzy soft sets. Yang et al. [18] used Feng et al.’s algorithm to solve MADM problems based on multiple fuzzy soft sets. Xu and Zhang [19] developed a novel approach by TOPSIS with incomplete weight information for solving hesitant fuzzy MADM problems. Wang et al. [20] proposed a MADM method based on hesitant fuzzy soft sets. Wang et al. [21] proposed a MADM approach based on the aggregation operator to aggregate hesitant fuzzy soft information. Peng and Yang [14], by means of TOPSIS and the maximizing deviation method, presented a MADM method based on interval-valued hesitant fuzzy soft sets.

The method proposed by Peng and Yang is very effective and allows a decision-maker to quantify his opinion with an interval value within , since it may be difficult for a decision-maker to exactly quantify his opinion with a crisp number to an attribute due to the insufficiency of available information in practical MADM problems. However, there are two problems with this method. One is that it only considers opinion of a decision-maker and the other is that the comparative law based on the score function is not accurate when it is used to compare two IVHFEs in dealing with MADM problems. Based on the above analysis, we improve Peng and Yang’s method to propose a new one of MADM methods based on IVHFSSs, which not only synthetically considers opinions of many decision-makers but also increases the accuracy of the comparison between two IVHFEs.

The remainder of this paper is organized as follows. In Section 2, some basic definitions including interval value, possibility degree, interval-valued hesitant fuzzy set, and interval-valued hesitant fuzzy soft set are briefly reviewed. In Section 3, a characteristic function of two interval values and a comparative law of IVHFEs based on the possibility degree are proposed. In Section 4, we define the interval-valued hesitant fuzzy soft quasi subset and soft quasi equal based on the new comparative law presented in Section 3. In Section 5, a MADM method based on IVHFSSs is presented. In Section 6, we apply this method to evaluate the importance of major components of the well drilling mud pump. Analysis and discussion are presented in Section 7. The paper is concluded in Section 8.

2. Preliminaries

In this section, we mainly review some definitions including interval value, possibility degree of interval value, interval-valued hesitant fuzzy set, and interval-valued hesitant fuzzy soft set. Let be an initial universe set of objects and let be the set of attributes in relation to objects in ; .

Definition 1 (see [22]). Let ; then is called a nonnegative interval value. Particularly, is a nonnegative real number, if .

The comparison of two interval values can be roughly divided into two categories: one is deterministic ordering method [23] and the other is possibility degree method [24]. In this paper, we select the latter method.

Definition 2 (see [24]). Let and be two interval values, and ; then(1);(2); particularly, , if .

Definition 3 (see [24]). Let and be two interval values; then the possibility degree of is defined as follows:

Definition 4 (see [25]). Let and :(1)If , then a is quasi-equal to b denoted by .(2)If , then a is more than b denoted by .(3)If , then a is less than b denoted by .

Definition 5 (see [4, 5]). Let X be a fixed set. A hesitant fuzzy set on X is in terms of a function that when applied to X returns a subset of , which can be represented as the following mathematical symbol:where is a set of values in , denoting the possible membership degrees of the element to the set H. For convenience, we call a hesitant fuzzy element.

Definition 6 (see [6]). Let X be a reference set, and let be the set of all closed subintervals of . An interval-valued hesitant fuzzy set (IVHFS) on X is where denotes all possible interval-valued membership degrees of the element to the set . For convenience, we call an interval-valued hesitant fuzzy element (IVHFE), which reads .

Definition 7 (see [6]). Let and be two IVHFEs, where and ; then the operational laws of IVHFEs are defined as follows: (1), .(2).(3).

Definition 8 (see [7]). Let be the set of all subsets of U. A pair is called a soft set over U, where is a mapping given by .

Definition 9 (see [14]). Let be the set of all interval-valued hesitant fuzzy subsets of U. A pair is called an interval-valued hesitant fuzzy soft set (IVHFSS) over U, where is a mapping given by .

In [14], some properties of IVHFSSs were given. We give another three properties as follows.

Property 10. Given an IVHFSS over U,(1)if , then is also an interval-valued hesitant fuzzy set,(2)if there is only one interval value of for all and , then is also an interval-valued fuzzy soft set,(3)if the upper and lower boundaries of each interval value of are equal for all and , then is also a hesitant fuzzy soft set.

Definition 11 (see [14]). The union operation on two IVHFSSs and over is an IVHFSS , where and, for all ,We write .

Definition 12 (see [14]). The intersection operation on two IVHFSSs and with over is an IVHFSS , where and, for all , . We write .

3. A New Comparative Law of Interval-Valued Hesitant Fuzzy Elements Based on the Possibility Degree

In this section, a new comparative law of IVHFEs will be proposed which can overcome some defects of the comparative law based on the score function.

For convenience, let denote the length of and let denote the th maximum interval value in . is the maximum value of and .

Definition 13 (see [6]). The score function of is defined as follows: Obviously, is still an interval value. Given two IVHFEs and ,(1)if , then ;(2)if , then ;(3)if , then .

Example 14. There are and . Obviously, . However, by Definition 13, we have ; thus .

Given two IVHFEs and , usually, . However, the comparative law used in Example 14 ignores this important point. In fact, according to the attitude of decision-makers, there are three different ways to make them equal in length by adding some suitable interval values. When the decision-makers are optimistic, we make them equal in length by adding the maximum interval value. Analogously, we add the minimum interval value when the decision-makers are pessimistic. The decision-makers remain neutral; we add the average of the maximum interval value and the minimum interval value. In this paper, if , without loss of generality, we take the pessimistic principle to make and equal in length such that .

Example 15. Two IVHFEs are given in Example 14. Obviously, . Since , . Based on the pessimistic principle, we can extend such that .

In 2014, Zhou [9] proposed a characteristic function of two real numbers. Based on this, we give a characteristic function of two interval values.

Definition 16. Assume that and ; then the characteristic function is defined as follows:

Definition 17. Let and be two IVHFEs. The possibility degree of is defined as follows:

Definition 18. Let and be two IVHFEs:(1)If , then IVHFE is quasi-equal to , denoted by .(2)If , then IVHFE is more than , denoted by .(3)If , then IVHFE is less than , denoted by .(4)If , then IVHFE is more than or equal to , denoted by .(5)If , then IVHFE is less than or equal to , denoted by .

Example 19. We consider two IVHFEs given in Example 14. Since , we extend based on the pessimistic principle. By Definition 17, we have ; then .

Theorem 20. Let and be two IVHFEs. The following conclusions hold:(1).(2) if and only if for all .(3) if and only if for all .

Proof. (i) It is easy to prove by Definition 17.
(ii) For necessity, if , then for all by Definitions 16 and 17. This implies that for all .
For sufficiency, if , then for all by Definition 16; thus, by Definition 17.
(iii) Similar to , we can prove .

Remark 21. Formula (7) in Definition 17 can also be defined as follows:However, it does not satisfy Theorem 20. Example 22 can illustrate the point as follows.

Example 22. There are and . Based on the pessimistic principle, we extend , and then by Formula (8) in Remark 21. There is a contradiction to Theorem 20, since for all .

Definition 23. Let and be two interval-valued hesitant fuzzy sets on X:(1)If , for all , then is an interval-valued hesitant fuzzy quasi subset of , denoted by .(2)If , for all , then is quasi-equal to , denoted by .

4. Two Important Definitions of Interval-Valued Hesitant Fuzzy Soft Sets Based on the New Comparative Law

In this section, we give definitions of the interval-valued hesitant fuzzy soft quasi subset and soft quasi equal according to Definition 23. Let and let and be two IVHFSSs over U.

Definition 24. is an interval-valued hesitant fuzzy soft quasi subset of , if the following two conditions are satisfied:(1).(2) for all . That is, , for all and , denoted by .

Definition 25. is soft quasi-equal to , if the following two conditions are satisfied:(1).(2) for all . That is, , for all and , denoted by .

Theorem 26. if and only if and .

Proof. For necessity, since , we have and by Definition 25. This implies that and for all Analogously, and for all . By Definition 24, we have and .
For sufficiency, since and , we have and for all by Definition 24. Analogously, and for all . That is, and for all . Therefore, by Definition 25.

5. A New Method of Multiattribute Decision-Making Based on Interval-Valued Hesitant Fuzzy Soft Sets

In the case of multiple attributes, the MADM is the decision problem to select the optimal alternative or rank the alternatives. Let be the set of n alternatives, let be the set of t attributes, and let be the set of s decision-makers. The decision-makers evaluate n alternatives from , where and represents the number of attributes in . The attribute weight vector of is , where .

IVHFSSs can be expressed by a tabular representation shown in Table 1. Firstly, decision-maker selects relevant attribute set according to his preference. Next, we collect the historical data of each attribute in the attribute set E and use it as the basis for the evaluation of decision-maker. For example, economy factor is determined by market price, and workforce factor is determined by population, and so forth. Finally, represents the IVHFE of the alternative satisfying the attribute which is given by the decision-maker , where and .

Table 1: Tabular representation of .

Definition 27. Suppose that and is an IVHFSS over U. A generalized comparison table (GCT) of is a square table in which the number of rows and number of columns are equal. Both rows and columns are named by , and elements in GCT are denoted by . Its formula is shown as follows:where t is the number of attributes.

Definition 28. Let be an IVHFE and let be a nonnegative real number; then a new multiplication operational law of IVHFEs is defined as follows:Obviously, it is different from Definition 7.

By the IVHFSSs based on the new comparative law of IVHFEs, we propose a new method of the MADM as follows.

Algorithm 29.
Step 1. Obtain the tabular representation of by for all decision-makers .
Step 2. Compute the weighted IVHFSS for all by .
Step 3. Compute , where .
Step 4. Calculate according to Formula (9) and construct the GCT.
Step 5. Compute row sum and column sum of the GCT, and calculate the score of alternative .
Step 6. If the score is the maximum value, then can be used as the optimal alternative.

6. Evaluate the Importance of the Major Components of the Well Drilling Mud Pumps

A mud pump, an important part of the well drilling equipment, is a large reciprocating pump, which is used to circulate the mud on a drilling rig. A mud pump is mainly composed of four parts which are pump case, piston, bearing, and impeller. However, the four parts are also most likely to be damaged. Once one of these parts is damaged, the mud pump will not work properly and probably cause huge losses. Aiming at this problem, we will use Algorithm 29 to evaluate the importance of the four parts of the well drilling mud pump.

Now, we consider four parameters, namely, economic factor, reliability factor, service life factor, and maintenance factor, to evaluate the importance of the four parts. In order to make evaluation by three experts reasonably and comprehensively, we investigated the market price of the four parts in recent years for economic factor, collected the information feedback of the four parts for reliability factor, surveyed the durable years of the four parts for service life factor, and counted the maintenance time of the four parts for maintenance factor. Let be a set of the four parts, let be the set of the three experts, and let be the set of the four attributes.

Based on the evaluation of the three experts, we construct three tabular representations of . The evaluations of experts are given as IVHFEs. The information is shown in Tables 24.

Table 2: Tabular representation of .
Table 3: Tabular representation of .
Table 4: Tabular representation of .

The weight vector is given by three technical experts , where , , and , respectively. Calculate the weighted IVHFSSs , , and . Their representations are shown in Tables 57.

Table 5: Tabular representation of .
Table 6: Tabular representation of .
Table 7: Tabular representation of .

Let ; then and

According to Formula (9), we can calculate and get the GCT of the IVHFSS shown in Table 8.

Table 8: The GCT of IVHFSS .

From Table 8, we can get the row sum , the column sum , and the score of shown in Table 9.

Table 9: The score of .

From Table 9, we have ; thus the part , that is, bearing, is the most important component.

7. Analysis and Discussion

In the above MADM problem, it is very difficult for to determine his evaluation on every by a crisp number; thus some interval values in are used. Therefore, the existing methods [1521] are inapplicable to solve this problem. Furthermore, it is necessary to evaluate the mud pump from different aspects, since it is a complex system; thus comprehensive opinions by more than one decision-maker are needed, which implies that the existing method [14] considering a decision-maker is not appropriate to deal with this problem, too.

In addition, in the process of evaluating the importance of the four parts of the well drilling mud pump with Algorithm 29, we need to calculate by IVHFEs and according to Formula (9). Therefore, the new comparative law given in Definition 17 is the base for computing .

8. Conclusions

Although Peng and Yang [14] have proposed IVHFSSs, the method they gave can only deal with MADM problems with a decision-maker. In most cases, it is necessary to synthesize opinions of more than one decision-maker for complex MADM problems. Based on IVHFSSs and the new comparative law, this paper presents a new method which can effectively solve MADM problems. We also apply this method to evaluate the importance of main parts of the well drilling mud pump, which can help engineers avoid the equipment failure as soon as possible.

Conflicts of Interest

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

Acknowledgments

This research is funded by the Open Fund of State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation (Southwest Petroleum University) (no. PLN 1511) and the National Natural Science Foundation of China (no. 11401494).

References

  1. L. A. Zadeh, “Fuzzy sets,” Information and Control, vol. 8, no. 3, pp. 338–353, 1965. View at Publisher · View at Google Scholar · View at Scopus
  2. K. T. Atanassov, “Intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 20, no. 1, pp. 87–96, 1986. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. K. Atanassov and G. Gargov, “Interval valued intuitionistic fuzzy sets,” Fuzzy Sets and Systems, vol. 31, no. 3, pp. 343–349, 1989. View at Publisher · View at Google Scholar · View at MathSciNet
  4. Z. Xu and M. Xia, “Distance and similarity measures for hesitant fuzzy sets,” Information Sciences, vol. 181, no. 11, pp. 2128–2138, 2011. View at Publisher · View at Google Scholar · View at MathSciNet
  5. M. Xia and Z. Xu, “Hesitant fuzzy information aggregation in decision making,” International Journal of Approximate Reasoning, vol. 52, no. 3, pp. 395–407, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. N. Chen, Z. S. Xu, and M. M. Xia, “Interval-valued hesitant preference relations and their applications to group decision making,” Knowledge-Based Systems, vol. 37, pp. 528–540, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. D. Molodtsov, “Soft set theory–first results,” Computers and Mathematics with Applications, vol. 37, no. 4-5, pp. 19–31, 1999. View at Publisher · View at Google Scholar · View at MathSciNet
  8. Z. Li, G. Wen, and N. Xie, “An approach to fuzzy soft sets in decision making based on grey relational analysis and Dempster-Shafer theory of evidence: an application in medical diagnosis,” Artificial Intelligence in Medicine, vol. 64, no. 3, pp. 161–171, 2015. View at Publisher · View at Google Scholar · View at Scopus
  9. X. Q. Zhou, Soft set and hesitant fuzzy set theories with their application in decision making (in Chinese), Hunan University, 2014.
  10. P. K. Maji, R. Biswas, and A. R. Roy, “Fuzzy soft sets,” Journal of Fuzzy Mathematics, vol. 9, no. 3, pp. 589–602, 2001. View at Google Scholar · View at MathSciNet
  11. X. Yang, T. Y. Lin, J. Yang, Y. Li, and D. Yu, “Combination of interval-valued fuzzy set and soft set,” Computers and Mathematics with Applications, vol. 58, no. 3, pp. 521–527, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  12. P. K. Maji, “Operations on intuitionistic fuzzy soft sets,” Journal of Fuzzy Mathematics, vol. 20, no. 1, pp. 163–177, 2012. View at Google Scholar · View at MathSciNet
  13. Y. Jiang, “Interval-valued intuitionistic fuzzy soft sets and their properties,” Computers and Mathematics with Applications, vol. 60, no. 3, pp. 906–918, 2010. View at Publisher · View at Google Scholar · View at MathSciNet
  14. X. Peng and Y. Yang, “Interval-valued hesitant fuzzy soft sets and their application in decision making,” Fundamenta Informaticae, vol. 141, no. 1, pp. 71–93, 2015. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. A. R. Roy and P. K. Maji, “A fuzzy soft set theoretic approach to decision making problems,” Journal of Computational and Applied Mathematics, vol. 203, no. 2, pp. 412–418, 2007. View at Publisher · View at Google Scholar · View at Scopus
  16. Z. Kong, L. Gao, and L. Wang, “Comment on 'A fuzzy soft set theoretic approach to decision making problems',” Journal of Computational and Applied Mathematics, vol. 223, no. 2, pp. 540–542, 2009. View at Publisher · View at Google Scholar · View at Scopus
  17. F. Feng, Y. B. Jun, X. Liu, and L. Li, “An adjustable approach to fuzzy soft set based decision making,” Journal of Computational and Applied Mathematics, vol. 234, no. 1, pp. 10–20, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  18. Y. Yang, X. Tan, and C. Meng, “The multi-fuzzy soft set and its application in decision making,” Applied Mathematical Modelling. Simulation and Computation for Engineering and Environmental Systems, vol. 37, no. 7, pp. 4915–4923, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  19. Z. Xu and X. Zhang, “Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information,” Knowledge-Based Systems, vol. 52, pp. 53–64, 2013. View at Publisher · View at Google Scholar · View at Scopus
  20. F. Q. Wang, X. Li, and X. H. Chen, “Hesitant fuzzy soft set and its applications in multicriteria decision making,” Journal of Applied Mathematics, vol. 2014, Article ID 643785, 10 pages, 2014. View at Publisher · View at Google Scholar
  21. J. Q. Wang, X. E. Li, and X. H. Chen, “Hesitant fuzzy soft sets with application in multicriteria group decision making problems,” The Scientific World Journal, vol. 2015, Article ID 806983, 14 pages, 2015. View at Publisher · View at Google Scholar
  22. Z. Xu, “Dependent uncertain ordered weighted aggregation operators,” Information Fusion, vol. 9, no. 2, pp. 310–316, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. W. U. Jiang and D. S. Huang, “A review on ranking methods of interval numbers,” Systems Engineering, vol. 22, no. 8, p. 1, 2004. View at Google Scholar
  24. Z. S. Xu and Q. L. Da, “The uncertain OWA operator,” International Journal of Intelligent Systems, vol. 17, no. 6, pp. 569–575, 2002. View at Publisher · View at Google Scholar · View at Scopus
  25. F.-J. Gao, “Comprehensive ranking of possible degree and interval number,” System Engineering Theory and Practice, vol. 33, no. 8, pp. 2033–2040, 2013 (Chinese). View at Google Scholar · View at Scopus