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2012 TFSA. 413. An Improved Ranking Method for Fuzzy Numbers Based on the. Centroid-Index. Luu Quoc Dat, Vincent F. Yu, and Shuo-Yan Chou. Abstract. 1.
International Journal of Fuzzy Systems, Vol. 14, No. 3, September 2012

413

An Improved Ranking Method for Fuzzy Numbers Based on the Centroid-Index Luu Quoc Dat, Vincent F. Yu, and Shuo-Yan Chou Abstract1 Ranking fuzzy numbers plays a very important role in the decision process, data analysis, and applications. The last few decades have seen a large number of methods investigated for ranking fuzzy numbers. The most commonly used approach for ranking fuzzy numbers is ranking indices based on the centroids of fuzzy numbers. However, there are some weaknesses associated with these indices. This paper reviews several fuzzy number ranking methods based on centroid indices and proposes a new centroid-index ranking method that is capable of effectively ranking various types of fuzzy numbers. The contents herein present several comparative examples demonstrating the usage and advantages of the proposed centroid-index ranking method for fuzzy numbers. Keywords: Fuzzy Numbers, centroid index, centroid of fuzzy numbers, ranking.

1. Introduction Ranking fuzzy numbers plays a very important role in decision-making, optimization, and other usages. Numerous ranking approaches have been proposed and investigated in the literature [1-24]. Among the ranking approaches, the centroid methods are commonly used approaches to rank fuzzy numbers. Ever since Yager [24] presented the centroid concept in the ranking approach, numerous ranking techniques using the centroid concept

Corresponding Author: Luu Quoc Dat is with the Department of Industrial Management, National National Taiwan University of Science and Technology, 43, Section 4, Keelung Road, Taipei 10607, Taiwan. E-mail: [email protected] Vincent Y. Fu is with the Department of Industrial Management, National National Taiwan University of Science and Technology, 43, Section 4, Keelung Road, Taipei 10607, Taiwan. E-mail: [email protected] Shuo-Yan Chou is with the Department of Industrial Management, National National Taiwan University of Science and Technology, 43, Section 4, Keelung Road, Taipei 10607, Taiwan. Email: [email protected] Manuscript received 15 Apr. 2011; revised 07 Jan. 2012; accepted 26 Aug. 2012.

have been proposed and investigated [6-9, 11-12, 14-19, 21-23]. A comparison of some existing centroid approaches can be found in Wang and Lee [19] and more recently in Ramli and Mohamad [17]. Yager [24] was the first researcher to propose a centroid-index ranking method to calculate the value x *A

∫ w( x) f ( x)dx , where ∫ f ( x)dx 1

for a fuzzy number A as x*A =

A

0

1

0

A

w( x) is a weighting function measuring the importance of the value x, and f A denotes the membership function of the fuzzy number A . When w( x) = x , the value x*A becomes the geometric Center of Gravity (COG)

∫ xf ( x)dx . The larger the value is of ∫ f ( x)dx 1

with xA* =

A

0

x*A , the

1

0

A

better the ranking of A . However, Yager [24] made no assumption on the normality and on the convexity of the fuzzy number [17]. Murakami et al. [16] proposed a centroid-index ranking approach that calculates the COG point ( x*A , y *A ) for each fuzzy number. The definition of x*A is the same as that used in COG by Yager [24], and y*A is defined as

∫ xf ( x)df ( x) . ∫ f ( x)dx 1

yA* =

A

0

A

1

The larger the values are of x*A

A

0

*

and (or) yA , the better the ranking will be of the fuzzy numbers. However, Murakami et al.’s [16] approach was only applicable to normal and convex fuzzy numbers. Cheng [11] used a centroid-based distance approach to rank fuzzy numbers. For a trapezoidal fuzzy number A = (a, b, c, d ;ϖ ), the distance index can be defined as

∫ xf dx + ∫ xdx + ∫ xf dx , = ∫ f dx + ∫ dx + ∫ f dx b

2

2

R( A) = x A + y A , where x

L A

a

A

b

a

yA

∫ =ϖ.

1

0

yg AL dy + ∫ yf AR dy

L A

c

d

b

c

b

c d

c

R A

R A

1

0 1

∫ g dy + ∫ g dy 1

0

L A

0

.

f AR and f AL are the respec-

R A

tive right and left membership functions of A, and g AR and g AL are the inverse of f AR and f AL , respectively.

© 2012 TFSA

International Journal of Fuzzy Systems, Vol. 14, No. 3, September 2012

414

The larger the value is of R( A) , the better the ranking will be of A. Cheng [11] further proposed a coefficient of variation (CV) index that improves the concept of ranking fuzzy numbers, using fuzzy mean and fuzzy spread as presented by Lee and Li [15]. Chu and Tsao [12] found that the distance approach and CV index proposed by Cheng [11] still had some shortcomings. Hence, to overcome the problems, Chu and Tsao [12] proposed a new ranking index function S = x A y A , where x A is as defined in Cheng [11] and

yA

∫ =

yg AL dy + ∫ yg AR dy

w

w

0



w

0

0 w

g dy + ∫ g AR dy L A

. The larger the value is of

0

S ( A), the better the ranking will be of A. In some special cases, Chu and Tsao’s [12] approach also has the same shortcomings as those in Cheng’s [11] approach. The shortcomings of Cheng’s and Chu and Tsao’s centroid-index are as follows. For fuzzy numbers A, B, C and − A, − B, −C , according to Cheng’s centroid-index R =

( x) + ( y) , 2

2

whereby the same results

that is, if A ≺ B ≺ C , then − A ≺ − B ≺ −C. This is clearly inconsistent with the mathematical logic. For Chu and Tsao’s centroid-index S = x y , if x = 0 , then the value of S = x y is a constant zero. In other words, the fuzzy numbers with centroids (0, y1 ) and (0, y2 ), ( y1 ≠ y2 ) are considered the same. This is also obviously unreasonable. Chen and Chen [6] proposed an approach for ranking generalized trapezoidal fuzzy numbers based on centroid point and standard deviations to overcome the drawbacks of Cheng’s [11], Murakami et al.’s [16] and Yager’s [24] approaches. The ranking value for a generalized trapezoidal fuzzy number A = ( a1 , a2 , a3 , a4 ; w) is defined as Rank ( A) = x A + ( w − y A ) s ( y A + 0.5)1−w , are

obtained,

where y A =

w ⎛ a3 − a2 ⎞ + 2 ⎟ for a ≠ a ; w / 2 other⎜ 6 ⎝ a4 − a1 ⎠ 1

∑ (a − a) 4

i

2

4

is defined as

= ( xAi − xAi min ) 2 + ( y Asi ) 2 ,

Score( Ai )

where ( x Ai , y Ai ) is as defined in Chen and Chen [6], y Asi = wAi / 2 − ( y Ai × s Ai ),

where

sAi =

(∑

4 j =1

)

(a ji − ai )2 / 3

and ai = (a1i + a2i + a3i + a4 i ) / 4. The higher the value of Score( Ai ), the better the ranking of the fuzzy number Ai . Chen and Chen [8] found that the approaches proposed by Chen and Chen [6] and Chen and Chen [7] still have shortcomings. Thus, Chen and Chen [8] proposed an approach for ranking generalized fuzzy numbers with different heights and different spreads. The score value of each standardized generalized fuzzy number is defined as Ai = ( ai1 , ai 2 , ai 3 , ai 4 ; wAi )



4

Score( Ai ) = ( xAi × wAi ) / (1 + sAi ), where sAi =

j=1

(aij − xAi )2

, 3 xAi = (ai1 + ai 2 + ai 3 + ai 4 ) / 4. The larger the value of Score( Ai ), the better the ranking of Ai . However, in some special cases, Chen and Chen’s [8] approach also led to some inconsistencies as pointed out by Chen and Sanguansat [9] and Kumar et al. [14]. In a study conducted by Wang et al. [23], the centroid formulae proposed by Cheng [11] and Chu and Tsao [12] are shown to be incorrect. Therefore, to avoid any more misapplication, Wang et al. [23] presented the correct centroid formulae as xA

∫ =

b

a

xf AL dx + ∫ xwdx + ∫ xf AR dx c

d

b



b

a

c d

f AL dx + ∫ wdx + ∫ f AR dx c

b

,

c

and

∫ y[ g ( y) − g ( y)]dy . = ∫ [( g ( y) − g ( y)]dy w

yA

0

w

0

R A

R A

L A

L A

The correct formula proposed by Wang et al. [23] is only limited to trapezoidal fuzzy numbers with invertible membership functions [17]. Shieh [18] presented the correct centroid formula, which can cater to both invertible and non-invertible fuzzy numbers. The formula of the horizontal point is the same as defined in Wang et al. [23], while the vertical point is defined as

a = (a1 + a2 + a3 + a4 ) / 4, , w 3 α | Aα | dα ∫ yA (a3 + a2 ) + (a4 + a1 )(wA − yA ) 0 , where | Aα | is the length of the . The larger the value of y A = w α xA = 2wA ∫0 | A | dα the better ranking of A . Rank ( A), α − cut Aα . In particular, for a trapezoidal fuzzy number In 2007, Chen and Chen [7] again indicated the A = (a, b, c, d ;ϖ ), the value of shortcomings of existing centroid methods, i.e. Chu and ⎡ ⎤ (c − b) Tsao’s [12], Cheng’s [11], Murakami et al.’s [16] ap- y( A) = (ϖ ) ⎢1 + , which coincides with 3 ⎣ (d + c) − (a + b) ⎥⎦ proaches. They proposed using the score values to rank fuzzy numbers. The score value of the fuzzy number Ai Wang et al.’s [23] formula. Table 1 shows the comparison of formulae of aforewise

s=

i =1

Luu Quoc Dat et al.: An Improved Ranking Method for Fuzzy Numbers Based on the Centroid-Index

mentioned centroid ranking approaches, which is adapted from Ramli and Mohamad [17]. It indicates that some approaches have the same formulae of centroid points such as between Chen and Chen [6] and Chen and Chen [7] and between Chu and Tsao’s [12] and Wang and Lee’s [19]. Some approaches also share the formulae of either the x value or the y value. The limitations of each approach are indicated in Table 2. It reveals that almost no approach can rank fuzzy numbers satisfactorily in all situations. In some situations, almost all methods cannot rank fuzzy numbers correctly with the same centroid point and only be applicable to invertible fuzzy numbers. Table 1. Comparison of formulate centroid point and the ranking index (adapted from [17]). Approaches

Formula of x

Formula of y

Ranking index

No

x value

∫ w(x) f (x)dx ∫ f (x)dx 1

Yager [24]

x*A =

A

0

1

0

A

∫ xf ( x)df ( x) ∫ f ( x)dx

x or y value

∫ yg dy + ∫ yf dy ∫ g dy + ∫ g dy

Distance

1

Murakami et al. [16]

yA* =

Same as Yager [24]

A

0

A

0

L R ∫ xfA dx+∫ xdx+∫ xfA dx b

Cheng [11]

Chu & Tsao [12]

xA =

c

a

d

b



b

a

A

1

c d

fALdx+∫ dx+∫ fARdx c

b

1

yA =ϖ.

c

L A

0

1

yA =



0

R A

R A

ygAL dy + ∫ ygARdy

w

0



w

0 w

gAL dy + ∫ gAR dy

⎧w⎛ a3 − a2 ⎞ ⎪ +2 , a ≠ a yA = ⎨ 6 ⎜⎝ a4 − a1 ⎟⎠ 1 4 ⎪⎩ wA / 2, a1 = a4

xA =

Chen & Chen [7] Wang & Lee [19] Chen & Chen [8]

Same as Chen & Chen [6] Same as Chu & Tsao [12]

Same as Chen & Chen [6] Same as Chu & Tsao [12]

xAi =(ai1 +ai2 +ai3 +ai4 )/4

No

∫ xf dx+∫ xwdx+∫ xf dx ∫ f dx+∫ wdx+∫ f dx b

Wang et al. [23]

Shieh [18]

xA =

L A

a

b

a

L A

c

b

c

b

d

c d

c

R A

R A

∫ y[g ( y) − g ( y)]dy ∫ [(g ( y) − g ( y)]dy w

yA =

Same as Wang et al. [23]

R A

L A

R A

L A

0

w

0

yA =



Area

w 0



Base on standard deviation Score index x or y value Score index

No

α | Aα | d α w 0

| Aα | d α

Yager [24] Murakami et al. [16] Cheng [11] Chu & Tsao [12] Chen & Chen [6] Chen &

N

Y

N

N

N N FNs - Fuzzy numbers NA - Data not available Y - Satisfy the properties N - Not satisfy the properties

To overcome the shortcomings of these existing centroid ranking approaches, this paper proposes a new centroid-index ranking method based upon the centroid formulae of Wang et al. [23] and Shieh [18]. This paper further presents several comparative examples demonstrating the efficiencies and advantages of the proposed centroid-index. 2. Improved Ranking Method Based on the Centroid-index of Fuzzy Numbers In this section the centroid point of a fuzzy number corresponds to a x value on the horizontal axis and y value on the vertical axis. The centroid point ( x, y ) for a fuzzy number A is as defined [18]: ∞

∫ xA( x)dx x = ∫ A( x)dx ∫ α | A | dα , y = ∫ | A | dα −∞ ∞

(1)

−∞

α

(2)

0

A

w

α

0

where A is a fuzzy number with sup A( x) = ϖ , and x∈R

| Aα | is the length of the α − cut Aα , 0 > α ≤ 1 , and | A |= Auα − Alα . If A is a crisp set with A( x0 ) = ϖ and A( x) = 0 if x ≠ x0 , then its centroid point is defined by ( x0 ,ϖ ) . For a trapezoidal fuzzy number A = (a, b, c, d ;ϖ ),

No

Table 2. The limitations of each ranking approach (adapted from [17]).

Approaches

N

w

0

Chen & Chen [6]

N

A

w

0

yA(a3 +a2)+(a4 +a1)(wA − yA) 2wA

0 1

L A

0

Same as Cheng [11]

1

Chen [7] Wang & Lee [19] Chen &Chen [8]

415

Consistent for FNs and images

Can rank correctly non-normal FNs

Applicable to invertible and non-invertible FNs

NA

N

NA

Can rank correctly FNs with the same centroid point N

NA

Y

NA

N

N

N

N

N

Y

N

N

N

N

Y

N

N

N

Y

N

N

the centroid point ( x A , y A ) is defined as in [18] and [23]. 1 dc − ab (3) x 0 ( A) = [a + b + c + d − ] 3 ( d + c ) − ( a + b) c −b ϖ (4) y 0 ( A) = [1 + ] 3 ( d + c ) − ( a + b) Remark: It is clear that (ϖ / 3) ≤ y 0 ( A) < (ϖ / 2). Proof: c −b ϖ ϖ y 0 ( A) = [1 + ]≥ 3 (d + c ) − (a + b) 3 c −b ⇒ 1+ ≥1 ( d + c ) − ( a + b)

International Journal of Fuzzy Systems, Vol. 14, No. 3, September 2012

416

c −b ≥0 ( d + c ) − ( a + b) ⇒c≥b

proach, Chen and Sanguansat’s [9] approach and the proposed approach get the same ranking order, i.e., A1 ≺ A2 .



(5) In the case of a triangular fuzzy number, b = c so y 0 ( A) = (ϖ / 3) . ϖ ϖ c −b ]< y 0 ( A) = [1 + 3 ( d + c ) − ( a + b) 2 2(c − b) ⇒ D( Aj , G) A1 = (0.1,0.2,0.4,0.5;1) and A2 = (0.1,0.3,0.5;1) used (3) Ai ∼ Aj ⇔ D( Ai , G) = D( Aj , G) in this example are adopted from Chen and Sanguansat [9]. Fig. 2 shows the graphs of the two fuzzy numbers. 3. Numerical Examples The results obtained by the proposed approach and other approaches are shown in Table 4. It is worth mentioning This section uses five numerical examples to compare that Yager’s [24] approach, Cheng’s [11] approach, Chu the ranking results of the proposed centroid-index rankand Tsao’s [12] approach, Chen and Sanguansat’s [9] ing approach with other existing ranking approaches. Example 1: Consider the data used in Chen and San- cannot differentiate A1 and A2 , that is, their rankings

D( Ai , G) = ( x Ai − xmin )2 + ( y Ai −

guansat [9], i.e., the two triangular fuzzy numbers as A1 = (0.1,0.3,0.5;0.8) and A2 = (0.1,0.3,0.5;1) shown in Fig. 1. Table 3 shows the comparison results of the proposed centroid-index ranking method with other existing centroid ranking approaches. It is clear that Yager’s [24] approach leads to an incorrect ranking order, i.e., A1 ∼ A2 , whereas Murakami et al.’s [16] approach, Cheng’s [11] approach, Chu and Tsao’s [12] approach, Chen and Chen’s [7] approach, Chen and Chen’s [8] ap-

1

2

1

2

1

2

1

2

are always the same, i.e. A1 ~ A2 . Note that the ranking

A1 ≺ A2 obtained by Murakami et al.’s [16] approach, Chen and Chen’s [7] approach, and Chen and Chen’s [8] approach, are thought of as unreasonable and not consistent with human intuition due to the fact that the center of gravity of A1 is larger than the center of gravity of A2 on the Y-axis.

Luu Quoc Dat et al.: An Improved Ranking Method for Fuzzy Numbers Based on the Centroid-Index

417

whose centroids are (0, y1 ) and (0, y2 ), ( y1 ≠ y2 ) are considered the same). However, using the proposed method, the correct result, A1 A2 , can be obtained. Thus, the proposed centroid-index ranking method overcomes the shortcomings of the inconsistency in Chu and Tsao’s [12] centroid approach. Figure 2. Fuzzy numbers A1 and A2 in example 2.

Example 3: Consider the data used in Chen and Sanguansat [9] i.e., the two triangular fuzzy numbers A1 = (−0.5, −0.3, −0.1;1) and A2 = (0.1,0.3,0.5;1) as shown in Fig. 3. Table 5 shows the comparison results of the proposed centroid-index ranking method with other existing centroid ranking approaches. The results indicate that Cheng’s [11] approach leads to an incorrect ranking order, i.e., A1 ∼ A2 , whereas Chu and Tsao’s [12] approach, Chen and Chen’s [7] approach, Chen and Chen’s [8] approach, Chen and Sanguansat’s [9] approach and the proposed approach get the same ranking order, i.e., A1 ≺ A2 .

Table 6. Comparative between fuzzy numbers A1 and A2 in example 4. Centroid points

Fuzzy number

x Ai

y

Ai

Chu and Tsao’s ranking index S = xi y i

Minimum points G

xmin

ymin

Centroid by formulae (8)

A1

0

1/3

0

-3

0.8

3.00074

A2

0

4/15

0

-3

0.8

3.00047

Table 5. Comparative results of example 3. Ranking approach Yager [24] Murakami et al. [16] Cheng [11] Chu and Tsao [12] Chen and Chen [7] Chen and Chen [8] Chen and Sanguansat [9] Proposed method

A1 NA NA 0.583 -0.15 0.446

A2 0.3 0.3 0.583 0.15 0.747

Ranking NA NA

-0.258

0.258

A1 ≺ A2

-0.3

0.3

A1 ≺ A2

0

0.6

A1 ≺ A2

A1 ∼ A2

Figure 4. Fuzzy numbers A1 and A2 in example 4.

A1 ≺ A2 A1 ≺ A2

Table 7. Comparative results of example 5. Ranking approach Wang et al. [22] Wang and Luo [20] Asady [3] Chen [5] Sign distance (p = 1) (Abbasbandy and Asady [1]) Sign distance (p = 2) (Abbasbandy and Asady [1]) Cheng [11] Abbasbandy & Hajjari [2]

Figure 3. Fuzzy numbers A1 , A2 and A3 in Example 3.

Example 4: Consider the two triangular fuzzy numbers A1 = ( −3, 0, 3;1), and A2 = (−3, 0,3;0.8) as shown in Fig. 4. Table 6 shows the results of the two triangular fuzzy numbers obtained by Chu and Tsao’s [12] centroid-index and proposed centroid-index (Eq. 7). The results reveal that the ranking order by Chu and Tsao’s centroid-index is the same, i.e. A1 ∼ A2 . Chu and Tsao’s [12] centroid-index produces the same rankings for different heights of fuzzy numbers (e.g. the fuzzy numbers

Proposed approach

A1 0.25

A2 0.5339

A3 0.5625 0.583

Ranking A1 ≺ A2 ≺ A3 A1 ≺ A2 ≺ A3

0.5

0.571

0.66667

0.81818

1

0.5

0.5714

0.5833

A1 ≺ A2 ≺ A3 A1 ≺ A2 ≺ A3

6.12

12.45

12.5

A1 ≺ A2 ≺ A3

8.52

8.82

8.85

A1 ≺ A2 ≺ A3

6.021

6.349

6.7519

A3 ≺ A2 ≺ A1

6

6.075

6.0834

A1 ≺ A2 ≺ A3

0.222

0.373

0.401

A1 ≺ A2 ≺ A3

Example 5: Consider the data used in Asady and Zendehanm [4], i.e., the three normal fuzzy numbers A1 = (5, 6, 7;1), A2 = (5.9, 6, 7;1) and A3 = (6,6,7;1) as shown in Fig. 5. Table 7 shows the ranking results of the three triangular fuzzy numbers by using the proposed method and other approaches. It is observed that the ranking order of the three fuzzy numbers obtained by the proposed approach is consistent with the ranking order obtained by other approaches (Abbasbandy and Asady

International Journal of Fuzzy Systems, Vol. 14, No. 3, September 2012

418

[1], Abbasbandy and Hajjari [2], Asady [3], Chen [5], Wang and Luo [20], Wang et al. [22]). Note that the ranking A1 A2 A3 obtained by the CV index of Cheng [11] is thought of as unreasonable and not consistent with human intuition. This example shows the strong discrimination power of the proposed ranking approach and its advantages.

[5]

[6]

[7] A1 A2

A3

[8] 5

5.9 6

7

x

Figure 5. Fuzzy numbers A1 , A2 and A3 in Example 5.

[9]

4. Conclusion This paper proposes a new centroid-index method for ranking fuzzy numbers. The proposed formulae are simple and have consistent expressions on the horizontal axis and vertical axis. Because the new centroid-index ranking method is based on Wang’s and Shieh’s centroid formulae, it can be used to rank both invertible and non-invertible fuzzy numbers. The paper herein presents several comparative examples to illustrate the validity and advantages of the proposed centroid-index ranking method. It shows that the ranking order obtained by the proposed centroid-index ranking method is more consistent with human intuitions than existing methods. Furthermore, the proposed ranking method can effectively rank a mix of various types of fuzzy numbers (invertible and non-invertible, normal, non-normal, triangular, and trapezoidal), which is another advantage of the proposed method over other existing ranking approaches.

[10]

[11]

[12]

[13]

[14]

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[2]

[3]

[4]

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Luu Quoc Dat et al.: An Improved Ranking Method for Fuzzy Numbers Based on the Centroid-Index

[18] B. S. Shieh, “An approach to centroids of fuzzy numbers,” International Journal of Fuzzy Systems, vol. 9, no. 1, pp. 51-54, 2007. [19] Y. J. Wang and H. S. Lee, “The revised method of ranking fuzzy numbers with an area between the centroid and original points,” Computers and Mathematics with Applications, vol. 55, no. 9, pp. 2033-2042, 2008. [20] Y. M. Wang and Y. Luo, “Area ranking of fuzzy numbers based on positive and negative ideal points,” Computers and Mathematics with Applications, vol. 58, pp. 1769-1779, 2009. [21] Z. X. Wang, J. Li, and S. L. Gao, “The method for ranking fuzzy numbers based on the centroid index and the fuzziness degree,” Fuzzy Information and Engineering, vol. 2, pp. 1335-1342, 2009. [22] Z. X. Wang, Y. J. Liu, Z. P. Fan, and B. Feng, “Ranking L-R fuzzy number based on deviation degree,” Information Sciences, vol. 179, no. 13, pp. 2070-2077, 2009. [23] Y. M. Wang, J. B. Yang, D. L, Xu, and K. S. Chin, “On centroids of fuzzy numbers,” Fuzzy Sets and Systems, vol. 157, pp. 919-926, 2006. [24] R. R. Yager, “On a general class of fuzzy connectives,” Fuzzy Sets and Systems, vol. 4, no. 6, pp. 235-242, 1980. Luu Quoc Dat received his B.S. degree in Political Economics in 2007 from Vietnam National University, Hanoi, and his M.S. degree in Business of Administration in 2009 from Southern Taiwan University. He is currently Ph. D candidate in Industrial Management department at National Taiwan University of Science and Technology. His recent research interests include fuzzy multi-criteria decision making, ranking fuzzy numbers, and fuzzy quality function deployment. Vincent F. Yu is an associate professor of Industrial Management at the National Taiwan University of Science and Technology. He received his Ph.D. in Industrial and Operations Engineering from the University of Michigan, Ann Arbor. His current research interests include information management, operations research, logistics/supply chain management, and soft computing. He had published articles in Computers & Industrial Engineering, Computers & Operations Research, European Journal of Operational Research, Management Decision, and Service Industries Journal.

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Shuo-Yan Chou is a distinguished professor of industrial management and the director of the Center for Internet of Things Innovation (CITI) at National Taiwan University of Science and Technology and also holds appointments at Graduate Institute of Automation and Control and Graduate Institute of Technology Management in the same university. He is currently a visiting scholar at Nagoya University in Japan. His research interests include Internet of Things, technology-enabled services, intelligent system modeling and application, RFID, supply chain management, and geometric algorithms. Dr. Chou was the dean of international affairs, the national coordinator of the European Union Framework Programmer National Contact Point Taiwan Office and the editor-in-chief of the Journal of Chinese Institute of Industrial Engineers published by Taylor and Francis. He was a visiting scholar at University of Washington and at Hong Kong University of Science and Technology. Dr. Chou has been very active in international cooperation, having served as the general chair for CE2009, 2010 INFORMS Service Science Conference and MCP AP2010 as well as the organizer of many international events. He received his BBA in industrial management from National Cheng-Kung University, Taiwan in 1983, his MS and PhD in industrial and operations engineering from the University of Michigan in 1987 and 1992 respectively.