Research Article | Open Access
Yingjun Zhang, Peihua Li, Yizhi Wang, Peijun Ma, Xiaohong Su, "Multiattribute Decision Making Based on Entropy under Interval-Valued Intuitionistic Fuzzy Environment", Mathematical Problems in Engineering, vol. 2013, Article ID 526871, 8 pages, 2013. https://doi.org/10.1155/2013/526871
Multiattribute Decision Making Based on Entropy under Interval-Valued Intuitionistic Fuzzy Environment
Multiattribute decision making (MADM) is one of the central problems in artificial intelligence, specifically in management fields. In most cases, this problem arises from uncertainty both in the data derived from the decision maker and the actions performed in the environment. Fuzzy set and high-order fuzzy sets were proven to be effective approaches in solving decision-making problems with uncertainty. Therefore, in this paper, we investigate the MADM problem with completely unknown attribute weights in the framework of interval-valued intuitionistic fuzzy (IVIF) set (IVIFS). We first propose a new definition of IVIF entropy and some calculation methods for IVIF entropy. Furthermore, we propose an entropy-based decision-making method to solve IVIF MADM problems with completely unknown attribute weights. Particular emphasis is put on assessing the attribute weights based on IVIF entropy. Instead of the traditional methods, which use divergence among attributes or the probabilistic discrimination of attributes to obtain attribute weights, we utilize the IVIF entropy to assess the attribute weights based on the credibility of the decision-making matrix for solving the problem. Finally, a supplier selection example is given to demonstrate the feasibility and validity of the proposed MADM method.
Atanassov and Gargov extended the intuitionistic fuzzy (IF) set (IFS) to the interval-valued intuitionistic fuzzy (IVIF) set (IVIFS) [1–3], characterized by membership and nonmembership functions whose values are intervals rather than real numbers. Since IFS and IVIFS were proposed, a great deal of literature abounds on both theoretical research of IFS and IVIFS as well as on application research in various fields, such as image processing [4–6], pattern recognition [7–9], clustering analysis [10–12], supplier selection, and decision-making analysis [11, 13, 14].
In particular, IVIFS and IFS are effective in solving the fuzzy decision-making problems. In most fuzzy multi-attribute decision making (MADM) problems, the preference over alternatives provided by decision makers is usually not sufficient for the crisp membership and nonmembership degree values, because things are fuzzy, uncertain, and probably influenced by the subjectivity of the decision makers, or the knowledge and data about the problem domain are insufficient during the decision-making process [13–17]. Therefore, the preferences among alternatives with uncertainty may be denoted by IFS or IVIFS for decision-making problems [7, 11, 13–15]. Chen and Tan , Xu [11, 19], Ye , Lakshmana Gomathi Nayagam et al. , and Wang et al.  proposed some methods to rank alternatives expressed with IVIFSs/IFSs and discussed their application in the MADM field. Atanassov  introduced some aggregation operators on IFS and IVIFS. After the pioneering work of Atanassov, some aggregation operators were proposed and utilized to tackle the MADM problems with uncertainty [24–33]. Xu and Yager , Li et al. [35–38], and Dubey et al.  constructed IF and IVIF MADM models based on optimization theories, such as linear, nonlinear, and fractional programming. Meanwhile, decision-making methods based on some measures (such as distance, similarity degree, correlation coefficient, and entropy) were proposed in dealing with fuzzy IF and IVIF MADM problems [7, 19, 35, 39–41]. In [13, 17, 22, 34, 37], emphasis was given that not only the information of alternatives on attributes may be fuzzy but also the attribute weight information may be partially known or unknown in some situations. In fact, proper assessment of attribute weights plays an important and essential role in the decision-making process, because the variation of weight values may result in different final ranking order of alternatives [11, 14, 17].
Generally speaking, the attribute weights in MADM can be classified as subjective and objective attribute weights based on the information acquisition approach . The subjective attribute weights are determined by preference information on the attributes given by the decision maker, who provides subjective intuition or judgments on specific attributes. The objective attribute weights are determined by the decision-making matrix. Analytic hierarchy process (AHP) method  and Delphi method  are two classical methods for generating subjective attribute weights. In terms of determining objective attribute weights, one of the most famous approaches is the Shannon entropy method, which expresses the relative intensities of attribute importance to signify the average intrinsic information transmitted to the decision maker. So far, a lot of literature pertaining to MADM analysis under IF/IVIF environment has been published using subjective weights [11, 15–17]. In the course of determining objective attribute weights under IF environment, Chen and Li  utilized IF entropy to assess the objective attribute weights in dealing with the IF MADM problem with completely unknown attribute weights. Despite existing research effort, solving the fuzzy MADM problems with completely unknown attribute weights in the framework of IVIFS remains an open problem [11, 14, 17, 35, 37, 38].
In an attempt to address such problems, we propose a novel entropy-based decision-making method under IVIF environment. Our focus is on the assessment of attribute weights. Meanwhile, we propose a definition of entropy on IVIFS, as well as a method to calculate it. Instead of using traditional methods, which use divergence among attributes or the probabilistic discrimination of attributes to obtain attribute weights, IVIF entropy is utilized to assess the objective weights based on the credibility of the input data. Furthermore, we construct a MADM model based on the IVIF weighted averaging (IIFWA) operator and the ranking functions on IVIFS.
The rest of this paper is organized as follows: in Section 2, we briefly review the concepts of IFS, IVIFS and some of their operations. In Section 3, we propose a definition of IVIF entropy and some calculation methods on entropy. An entropy-based MADM method within the framework of IVIFS is proposed in Section 4. A numerical example is utilized to illustrate the applicability of the proposed method in Section 5. Finally, conclusion is given in Section 6.
In this section, we briefly review some basic concepts, for example, IFS, IVIFS, and their relevant operations.
Notation 2. is the universal set (it is clear that ). , , and denote all the fuzzy sets on , all the IFSs on , and all the IVIFSs on , respectively.
Definition 1 (see ). Let be a set. An IFS in is defined as where are two maps satisfying
and denote the membership and nonmembership degrees of to , respectively. For each IFS in , we designate an intuitionistic index of in .
The intervals and denote the membership and nonmembership degrees of to , respectively. Let where , , , and . If and , then IVIFS reduces to an IFS.
For each IVIFS , we designate an intuitionistic interval of in , where
The following expressions are defined for all and belonging to :(1),(2) if and only if , , , and for all ,(3) if and only if , , , and for all ,(4) if and only if and .
Ranking the alternatives expressed with IVIFSs is necessary to deal with the MADM problem within the framework of IVIFS. We introduce the score function and accuracy function of IVIFS proposed by Xu .
Definition 3 (see ). Let be an IVIF number. The score function of is defined as follows: where .
Definition 4 (see ). Let be an IVIF number. The accuracy function of is defined as follows: where .
Based on the score function and the accuracy function, Xu further introduced an algorithm of ranking alternatives expressed with IVIFSs.
Definition 5 (see ). Let and be two IVIF numbers; then the ranking order between and is defined as follows: (1)If , then . (2)If , then . (3)If , then (a)if , then ; (b)if , then ; (c)if , then .
3. Entropy on IVIFSs
The definition of entropy on IFSs/IVIFSs has a great importance in the theoretical research of IFSs/IVIFSs. In 1996, Burillo and Bustince  introduced the IF entropy for the first time, and, in 2001, Szmidt and Kacprzyk  proposed a non-probabilistic-type entropy measure for IFSs. Later, Hung  and Zhang et al.  constructed the IF entropy based on the distance measures among IFSs. Vlachos and Sergiadis  introduced the De Luca-Termini nonprobabilistic entropy for IFSs. Zeng and Su  introduced the IF entropy based on the similarity among IFSs. Ye  constructed two IF entropies based on the fuzzy entropy established by Zadeh . Chen and Li  presented different entropies on IFSs. So far, a great deal of literature on entropy on IFSs is available, but, unfortunately, to our knowledge, only a few works on entropy on IVIFSs exist. Two published papers are found in  which extended De luca-Termini axioms and proposed an IVIF entropy, and in  which proposed an IVIF entropy in dealing with multiple-attribute decision-making problem.
3.1. Entropy on IFSs
To construct the entropy on IFSs, Burillo and Bustince introduced function , where . Meanwhile, function satisfies the following conditions:(a), if and only if ,(b), if and only if and ,(c),(d)if and , then .
According to Definition 6 and function , Bustince and Burillo proposed a theorem to construct the entropy on IFSs.
Theorem 7 (see ). Let , , and . If , where satisfies conditions (a)–(d); then is an entropy on .
3.2. Entropy on IVIFSs
Definition 8. A real function is called an entropy, if has the following properties:(1), if and only if is a fuzzy set;(2), if and only if and for all ;(3) for all ;(4)If , then .
For any , we introduce the following operator: where , , and . Obviously, is an IFS on .
The following theorem gives an expression that allows us to construct different entropies on IVIFSs.
Theorem 9. Let , , and . If then is an entropy of , where satisfies conditions (a)–(d), , , and .
Proof. (1) If is a fuzzy set, then , , and for all . Clearly, according to the condition (a) of .
implies that for any . If is not a fuzzy set, then for some . As and , for any . According to conditions (a) and (d) of , we have . Thus, , which contradicts . Thus, is a fuzzy set when .
(2) and for all which implies that , for any . Based on condition (b) of , we have .
implies that for any . As , based on the condition (b) of . If exists that satisfies , it means that for any . According to conditions (b) and (d) of , we have . Obviously, , which contradicts . Thus, we have and .
(3) As , clearly, .
(4) If , then , , , and ; then for any ; then , that is, . According to condition (d) of , we have , that is, implying .
The proof is completed.
According to (14), we can construct different entropies on IVIFSs based on various . Some example functions that satisfy the previous conditions are presented as follows:
3.3. Experimental Analysis of IVIF Entropies
Example 10. Let us assume six IVIFSs, namely, , , , , , and on .
In this example, we adopt , , and to calculate the entropies of by choosing different values of parameter . The entropies of are shown in Table 1.
3.4. Analysis of the Experimental Results
The results of Example 10 show that , , and are decreasing functions of and satisfy the four properties of Definition 8. The results of Example 11 show that , , and can be regarded as increasing functions of .
4. MADM Method within the Framework of IVIFS
In this section, we present a novel method to solve the decision-making problems with unknown attribute weights based on the ranking functions and IVIF entropy. Let be a set of alternatives and be a set of attributes. The IVIF decision matrix of on is where denotes the IVIF numbers. In the following, we introduce our method.
Definition 12. Let be the IVIF decision-making matrix. We call an IVIF entropy matrix of , where denotes the IVIF entropy of .
Step 1 (determine the attribute weights). Let . We introduce an IVIF entropy matrix. From Definition 12, we obtain the entropy matrix of the decision matrix . We normalize the entropy matrix to get , where for and . Finally, the attribute weight is calculated by where and .
Step 3. Rank all the alternatives according to Definition 5 and select the most desirable alternative.
5. Numerical Examples
Example 13. This example is adapted from . In this supplier selection problem, five possible alternatives denoted by exist on the following six attributes denoted by . The ratings of the alternatives are given in the IVIF decision matrix shown in Tables 3 and 4.
In this example, we adopt to solve the supplier selection problem. The decision-making process is as follows. Step 1. Based on , the attribute weights are obtained, as shown in Table 5. Step 2. The weighted arithmetic average values for are calculated. Step 3. The score values are calculated, and the decision-making results are shown in Table 6.
5.2. Experimental Analysis
Similarly, we adopt and to implement the decision-making process for this supplier selection problem. All the ranking results are the same as the ranking order of , that is, . By applying the methods in [11, 41] to Example 13, the ranking order is , and the most desirable one is which is identical to ours. From the aforementioned results, the proposed decision-making method in this paper can be suitably utilized to solve the IVIF MADM problem with completely unknown attribute weights. Different IVIF entropies clearly offer various choices for assessing the attribute weights.
In this paper, we have focused on solving the IVIF MADM problem with completely unknown attribute weights. We first introduced a new definition of IVIF entropy and some calculation methods of different IVIF entropies. Subsequently, we proposed an entropy-based MADM method in the framework of IVIFS. The proposed method can be utilized to solve fuzzy and uncertain decision-making problems derived from supplier selection, public risk, medical diagnosis, and other problems in any aspects.
The authors is very grateful to the anonymous referees and editors, for their constructive comments and suggestions that have led to an improved version of this paper. This research was supported by the Fundamental Research Funds for the Central Universities under Grant no. 2013JBM023.
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