The Scientific World Journal

Volume 2014 (2014), Article ID 234241, 10 pages

http://dx.doi.org/10.1155/2014/234241

Research Article

Evaluation about the Performance of E-Government Based on Interval-Valued Intuitionistic Fuzzy Set

Shuai Zhang, Dejian Yu, Yan Wang, and Wenyu Zhang

School of Information, Zhejiang University of Finance and Economics, Hangzhou 310018, China

Received 18 September 2013; Accepted 7 January 2014; Published 23 February 2014

Academic Editors: F. Di Martino and F. Schwenker

Copyright © 2014 Shuai Zhang 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.

The evaluation is an important approach to promote the development of the E-Government. Since the rapid development of E-Government in the world, the E-Government performance evaluation has become a hot issue in the academia. In this paper, we develop a new evaluation method for the development of the E-Government based on the interval-valued intuitionistic fuzzy set which is a powerful technique in expressing the uncertainty of the real situation. First, we extend the geometric Heronian mean (GHM) operator to interval-valued intuitionistic fuzzy environment and proposed the interval-valued intuitionistic fuzzy GHM (IIFGHM) operator. Then, we investigate the relationships between the IIFGHM operator and some existing ones, such as generalized interval-valued intuitionistic fuzzy HM (GIIFHM) and interval-valued intuitionistic fuzzy weighted Bonferoni mean operator. Furthermore, we validate the effectiveness of the proposed method using a real case about the E-Government evaluation in Hangzhou City, China.

1. Introduction

Intuitionistic fuzzy set (IFS), an extension of Zadeh’s fuzzy set, was first proposed by Atanassov [1]. Over the last decade, the IFS theory issue has become an important research area of mathematics, management, and computer sciences. It is generally known that the membership degree and nonmembership degree of the IFS are expressed by determined number [2–10]. Based on the IFS theory, Atanassov and Gargov [11] utilized the interval number rather than the determined number to express the membership degree and nonmembership degree and introduced the interval-valued IFS (IIFS). Researchers have many research works and have some results regarding IIFS theory.

Interval-valued intuitionistic fuzzy number (IIFN) is the basic ingredient of the IIFS theory and more powerful to express the uncertainty than intuitionistic fuzzy number (IFN) [12–14]. How to aggregate the IIFNs to a comprehensive one is a very active research area and is critical for artificial intelligence, decision making, and management science. So far there are many aggregation operators proposed to aggregate the IIFNs [15–17]. The Heronian mean (HM) is a mean type information aggregation technique, which is proposed by Beliakov et al. [18] and mainly used to aggregate determined numbers. In this paper, we extend the HM mean operator to adapt it to interval-valued intuitionistic fuzzy environment and then study the E-Government evaluation method based on IIFS theory.

To do this, we organize the paper as follows. Section 2 extends the GHM operator to interval-valued intuitionistic fuzzy environment and proposes the interval-valued intuitionistic fuzzy GHM (IIFGHM) operator. Some special cases are discussed in this section. Section 3 introduces the interval-valued intuitionistic fuzzy geometric weighted Heronian mean (IIFGWHM) and develops an approach for multicriteria decision making. A real case about the E-Government evaluation in Hangzhou City, China, is also provided in this section. Section 4 ends this paper with some concluding remarks.

2. The Interval-Valued Intuitionistic Fuzzy Geometric Heronian Mean Operator

Atanassov and Gargov [11] first proposed the IIFS and gave the definition of IIFS.

Definition 1. The IIFS on was defined as follows:

The and are two functions that indicated the degrees range of membership and nonmembership, respectively. Furthermore, the two functions are valued between and the sum of the maximum value of the two functions is also between [19].

Definition 2 (see [20]). Let and be any two IIFNs; then some operational rules of IIFN and IIFN are defined as

(1);(2);(3);(4).

And the score function of IIFN is defined as

The score function of IIFN is an important indicator for comparing any two IIFNs. In the general case, the bigger the score function, the bigger the IIFN.

Example 3. Let , , and be three IIFNs; we can get the following score functions based on (2)

Since

then

Heronian mean (HM) is able to characterize quantitatively the relations between the aggregated arguments. The definition of HM was given as follows.

Definition 4 (see [18]). Let be a collection of nonnegative numbers. If

then HM is called the Heronian mean (HM).

Example 5. Let , , be three nonnegative numbers; based on the HM operator, we can get

Based on Definition 4, Yu [21] proposed the geometric Heronian mean as follows.

Definition 6. Let , , be a collection of nonnegative numbers. If

then is called the geometric Heronian mean (GHM).

In order to deal with the situation of interval-valued intuitionistic fuzzy environment, we extend the GHM and propose the interval-valued intuitionistic fuzzy GHM as follows.

Definition 7. Let , , be a collection of IIFNs; if

then IIFGHM is called the interval-valued intuitionistic fuzzy geometric Heronian mean (IIFGHM).

Based on the operational laws of the IIFNs described in Definition 2, we can derive the following results.

Theorem 8. Let , , be a collection of IIFNs; then the aggregated value by using the IIFGHM is also an IIFN, and

Proof. We can prove Theorem 8 by mathematical induction and the similar proof method can be referred to Yu [21].

We studied the interval-valued intuitionistic fuzzy Heronian mean and proposed the generalized interval-valued intuitionistic fuzzy HM (GIIFHM) in our previous works [22]. It should be noted that the GIIFHM operator is a kind of averaging mean operator and the IIFGHM proposed in this paper is a kind of geometric mean operator. We try to apply a numeric example in simulation in order to compare the IIFGHM and GIIFHM operators.

Example 9. Let , , and be three IIFNs; when the parameters , take different values, scores values are obtained based on IIFGHM and GIIFHM operators which are shown in Figures 1 and 2.

Figure 1: Scores obtained by the IIFGHM operator (, ).

Figure 2: Scores obtained by the GIIFHM operator (, ).

3. Interval-Valued Intuitionistic Fuzzy Multicriteria Decision Making Based on IIFGWHM Operator

The IIFGHM operator does not consider the weight of the aggregated arguments and it should be improved. In this section we first introduce the weighted form of IIFGHM (IIFGWHM) operator and then introduce a multicriteria decision making method based on IIFGWHM operator.

Definition 10. Let be a collection of IIFNs and be the weight vector of , where indicates the importance degree of , satisfying , , and . If

then IIFGWHM is called the interval-valued intuitionistic fuzzy geometric weighted Heronian mean (IIFGWHM).

Similar to Theorem 8, Theorem 11 can be derived easily.

Theorem 11. Let , , be a collection of IIFNs, whose weight vector is , which satisfies , , and . Then the aggregated value by using the IIFGWHM is also an IIFN, and

In a presumed multicriteria decision making problem [23–29], let be a set of Districts and let a set of criteria, whose weight vector is , satisfying , and . The performance of District with respect to the criterion is measured by an IIFN , where indicates the degree range in which District satisfies the criterion and indicates the degree range in which District does not satisfy the criterion and construct the interval-valued intuitionistic fuzzy decision matrix .

Step 1. Normalize the decision making matrix into standardized matrix. In other words, if the criteria is the benefit criteria, then the values do not need changing; if criteria is the cost criteria, then use instead of [30–33], where is the complement of .

Step 2. Aggregate all the performance values of the th line, and get the overall performance value corresponding to District by the IIFGWHM:

where .

Step 3. Rank the overall performance values according to Definition 2 and obtain the priority of Districts according to .

Example 12. Advocating the E-Government has important value for establishing a harmonious and efficient government. Experience has confirmed the potential effect of E-Government on the development of whole society. It is a fact to the academic circles that the continual development of E-Government needs the support of the performance evaluation. Hangzhou city is the capital of Zhejiang Province, China, and is the political, economic, cultural, and financial and transportation center of Zhejiang Province. At present, the performances of the E-Government of the four Districts in Hangzhou city need to be evaluated. Based on the result of many researches [34, 35], this evaluation proceeds in the following three aspects: construction costs of the E-Government (), the effectiveness of the E-Government system (), and the quality of E-Government system (). The three criteria may occur to different degrees and suppose as the weight vector of the three criteria. The evaluation information on the four Districts under the factors are represented by the IIFNs and shown in Table 1.

Table 1: The interval-valued intuitionistic fuzzy decision matrix .

([0.2, 0.3], [0.6, 0.7])
([0.2, 0.4], [0.4, 0.5])
([0.1, 0.3], [0.3, 0.5])

([0.3, 0.5], [0.1, 0.3])
([0.3, 0.4], [0.4, 0.6])
([0.7, 0.9], [0.0, 0.1])

([0.4, 0.6], [0.3, 0.4])
([0.7, 0.9], [0.0, 0.1])
([0.2, 0.3], [0.4, 0.6])

([0.4, 0.6], [0.2, 0.3])
([0.3, 0.4], [0.4, 0.5])
([0.2, 0.4], [0.4, 0.5])

Since the construction costs of the E-Government () is the cost criteria, therefore, it needs normalization. The Normalized decision matrix is shown in Table 2.

Table 2: The interval-valued intuitionistic fuzzy decision matrix .

([0.6, 0.7], [0.2, 0.3])
([0.2, 0.4], [0.4, 0.5])
([0.1, 0.3], [0.3, 0.5])

([0.1, 0.3], [0.3, 0.5])
([0.3, 0.4], [0.4, 0.6])
([0.7, 0.9], [0.0, 0.1])

([0.3, 0.4], [0.4, 0.6])
([0.7, 0.9], [0.0, 0.1])
([0.2, 0.3], [0.4, 0.6])

([0.2, 0.3], [0.4, 0.6])
([0.3, 0.4], [0.4, 0.5])
([0.2, 0.4], [0.4, 0.5])

From the Definition of IIFGWHM operator, we know that the values of parameters and may largely affect the aggregated IIFEs. In the following, we study the aggregated results as the values of the parameters and change. Tables 3 and 4 show the details of the results.

Table 3: Aggregated IIFNs based on IIFGWHM operator.

,
([0.6107, 0.7546], [0.1246, 0.1830])
([0.6569, 0.7705], [0.1098, 0.1944])
([0.7351, 0.8111], [0.0913, 0.1675])
([0.6226, 0.7181], [0.1570, 0.2222])

,
([0.5398, 0.7056], [0.1727, 0.2278])
([0.5950, 0.7004], [0.1752, 0.2892])
([0.6862, 0.7564], [0.1181, 0.2027])
([0.6140, 0.6985], [0.1790, 0.2389])

,
([0.5363, 0.7016], [0.1732, 0.2296])
([0.5848, 0.7069], [0.1718, 0.2824])
([0.7006, 0.7646], [0.1193, 0.2030])
([0.6151, 0.6997], [0.1771, 0.2378])

,
([0.5236, 0.6902], [0.1839, 0.2425])
([0.5710, 0.7007], [0.1779, 0.2944])
([0.6954, 0.7605], [0.1210, 0.2071])
([0.6093, 0.6898], [0.1861, 0.2459])

([0.5140, 0.6836], [0.1916, 0.2497])
([0.5665, 0.6836], [0.1916, 0.3145])
([0.6762, 0.7450], [0.0000, 0.2117])
([0.6049, 0.6834], [0.1916, 0.2512])

Table 4: Score values obtained by the IIFGWHM operator and the rankings of Districts.

Ranking

,
0.5288
0.5616
0.6437
0.4807

,
0.4224
0.4155
0.5609
0.4473

,
0.4176
0.4188
0.5715
0.4500

,
0.3937
0.3997
0.5640
0.4335

0.3781
0.3720
0.6048
0.4228

If we let the parameter be fixed, different scores and rankings of the Districts can be obtained as the parameter changes, as is shown in Figure 3.

Figure 3: Scores of IIFWGHM ().

From Figure 3, we can find that,

(1)when , the ranking order of the Districts is ,(2)when , the ranking order of the Districts is ,(3)when , the ranking order of the Districts is ,(4)when , the ranking order of the Districts is ,(5)when , the ranking order of the Districts is .

On the other hand, if we let the parameter be fixed, different rankings of the Districts can be obtained as the parameter changed which was shown in Figure 4.

Figure 4: Scores of IIFGWHM ().

From Figure 4, we can find that,

(1)when , the ranking order of the Districts is ,(2)when , the ranking order of the Districts is ,(3)when , the ranking order of the Districts is ,(4)when , the ranking order of the Districts is .

Different scores of the four Districts can be obtained as the parameters and changed. Figures 5, 6, 7, and 8 illustrate the scores of four Districts obtained by the IIFGWHM operator in detail.

Figure 5: Scores for District obtained by the IIFGWHM operator ().

Figure 6: Scores for District obtained by the IIFGWHM operator ().

Figure 7: Scores for District obtained by the IIFGWHM operator ().

Figure 8: Scores for District obtained by the IIFGWHM operator ().

From the above analysis, we can easily find that when the parameters are assigned different values, different decision results may be generated. Therefore, it is a very flexible interval-valued intuitionistic fuzzy decision making method.

In order to compare the IIFGWHM operator with the IIFWBM operator which was proposed by Xu and Chen [33], we utilize the IIFWBM operator to replace (13) and analyze the decision making method. The IIFWBM operator was given as follows [33]:

If we aggregate the IIFNs based on the IIFWBM operator, the aggregated IIFNs can be obtained as the values of the parameters and change. The results are shown in Table 5. The corresponding score values and the ranking of Districts are shown in Table 6.

Table 5: Aggregated IIFNs based on IIFWBM operator.

,
([0.1006, 0.1855], [0.6761, 0.7619])
([0.1347, 0.2312], [0.5041, 0.7337])
([0.1523, 0.2290], [0.5928, 0.7507])
([0.0839, 0.1392], [0.7424, 0.8139])

,
([0.1636, 0.2286], [0.6689, 0.7444])
([0.2107, 0.3510], [0.2351, 0.6386])
([0.2985, 0.4560], [0.2717, 0.5350])
([0.1172, 0.1695], [0.7129, 0.7777])

,
([0.1388, 0.2171], [0.6718, 0.7493])
([0.1890, 0.2966], [0.3952, 0.6917])
([0.2147, 0.3227], [0.4655, 0.6632])
([0.0982, 0.1543], [0.7297, 0.7963])

,
([0.1770, 0.2391], [0.6658, 0.7395])
([0.2267, 0.3783], [0.1772, 0.6136])
([0.3287, 0.5029], [0.2059, 0.4899])
([0.1256, 0.1790], [0.7005, 0.7658])

([0.0000, 0.2282], [0.6673, 0.7392])
([0.1939, 0.2947], [0.4418, 0.7035])
([0.1871, 0.2751], [0.5412, 0.7157])
([0.0000, 0.1593], [0.7309, 0.7951])

Table 6: Score values obtained by the IIFWBM operator and the rankings of Districts.

Ranking

,
−0.2453
−0.1364
−0.1819
−0.3016

,
−0.2201
0.0579
0.0922
−0.2717

,
−0.2273
−0.0493
−0.0714
−0.2877

,
−0.2133
0.1006
0.1485
−0.2608

−0.2196
−0.0735
−0.1331
−0.2858

Different scores of the four Districts can be obtained as the parameters and change. Figures 9, 10, 11, and 12 illustrate the scores of four Districts obtained by the IFWBM operator in detail.

Figure 9: Scores for District obtained by the IIFWBM operator .

Figure 10: Scores for District obtained by the IIFWBM operator .

Figure 11: Scores for District obtained by the IIFWBM operator .

Figure 12: Scores for District obtained by the IIFWBM operator .

4. Concluding Remarks

In this paper, we have put forward an associated aggregation operator for IIFNs called the IIFGHM operator. We have analyzed the weighted form of IIFGHM operator and introduced the IIFGWHM operator. A flexible multicriteria decision making method has been introduced, by which the optimal alternative(s) can be derived. We have studied the applicability of the IIFGWHM operator in multicriteria decision making problems and we have carried out the evaluation of the performance of E-Government in Hangzhou city, China. In future research, we will consider other applications of this approach, such as investment management, teaching quality evaluation, and supply chain management.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgment

The authors would like to thank the anonymous reviewers for their valuable comments on their paper. This research was supported by the National Natural Science Foundation of China (nos. 71301142 and 51375429), the Zhejiang Natural Science Foundation of China (no. LQ13G010004), and the Zhejiang Science & Technology Plan of China (no. 2013C31099).

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