An evaluation approach to logistics service using fuzzy theory, quality function development and goal programming

Computers & Industrial Engineering 68 (2014) 54–64

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An evaluation approach to logistics service using fuzzy theory, quality function development and goal programming Chin-Nung Liao a,⇑, Hsing-Pei Kao b,1 a b

Department of Business Administration, China University of Science and Technology, No. 245, Sec. 3, Academia Rd., Nankang, Taipei 115, Taiwan, ROC Graduate Institute of Industrial Management, National Central University, No. 300, Jhongda Rd., Jhongli City, Taoyuan County 320, Taiwan, ROC

a r t i c l e

i n f o

Article history: Received 24 February 2012 Received in revised form 11 October 2013 Accepted 3 December 2013 Available online 11 December 2013 Keywords: Logistics customer service Logistic operations Quality function deployment Fuzzy extended analytic hierarchy process Multi-segment goal programming

a b s t r a c t Logistics customer service is an important factor in the success of supply chain management. The aim of this study is to propose a novel approach for customer service management. For the improvement of logistics service operations, the proposed method integrates quality function development (QFD), fuzzy extended analytic hierarchy process (FEAHP), and multi-segment goal programming (MSGP). The advantage of the method includes the consideration of various logistics goals and the flexibility of setting multiaspiration levels of evaluation criteria. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Almost two decades ago, some studies had highlighted the significant role of logistics and customer service in achieving competitive advantage (Bailey, 1996). Since then, customer service has become increasingly important while brand advantage and the product’s technical characteristics are no longer the exclusive factors to attract customers. While directly related to customer service, logistics management aims to increase the operational efficiency by facilitating greater collaboration and coordination with business partners (Celebi, Bayraktar, & Bingol, 2010). Furthermore, as a global trend, outsourcing of the logistics function has become increasingly important that the logistics service providers (LSPs) have been well positioned to turn into the indispensable links in the chain of commerce. Whereas businesses need to expand the breadth of logistics services (Bottani & Rizzi, 2006), its design determines if the actual operations will ensure customer satisfaction and lifetime value. Therefore, understanding customers’ requirement for providing the right solution is essential. Same as physical products, customers evaluate service by comparing their perceptions with their expectations; therefore, a gap in between can be a synthetic mea-

⇑ Corresponding author. Tel.: +886 2 27821862x214; fax: +886 2 27864984. E-mail addresses: [email protected] (C.-N. Liao), [email protected] (H.-P. Kao). 1 Tel.: +886 3 4227151x66651; fax: +886 3 4258197. 0360-8352/$ - see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.cie.2013.12.001

sure of customer satisfaction (Bottani & Rizzi, 2006; Robledo, 2001). This paper proposes a novel approach for designing the logistics customer service by integrating quality function development (QFD), fuzzy extended analytic hierarchy process (FEAHP) and multi-segment goal programming (MSGP). QFD is implemented as an analytical framework to integrate FEAHP and MSGP. FEAHP not only handles the inherent uncertainty of the human judgment, but also provides the flexibility for the decision makers to comprehend the problem-solving process. MSGP is devised to address the decision-making problem which involves multi-segment aspiration levels of evaluation criteria. The remainder of this paper is organized in the following order. Section 2 reviews the studies on logistics service for defining customer service requirements (CSRs) and logistics operation requirements (LORs) from the respective perspectives of customers and service providers. Section 3 introduces the basics of QFD, FEAHP and MSGP. Section 4 presents the procedure of the proposed method and Section 5 provides a practical case study to demonstrate its application. Section 6 provides the concluding remarks. 2. Logistics and customer service From customer perspective, Franceschini and Rafele (2000) suggested the requirements of logistics service include lead-time, regularity, reliability, flexibility, completeness, correctness, harmfulness and productivity. Bottani and Rizzi (2006) considered lead-time, flexibility, reliability, accuracy, fill rate, frequency,

C.-N. Liao, H.-P. Kao / Computers & Industrial Engineering 68 (2014) 54–64

organization accessibility and complaints management as the logistics service factors, while Spekman, Kamauff, and Myhr (1998) and Gourdin (2006) provided similar lists concerning logistics service. In contrast, from the service provider’s perspective, logistics management refers to analyzing, designing, and controlling the internal and external functions of the logistics system, including supplying materials, transforming materials and distributing finished products or services to customers, while maintaining consistency with the logistics strategy (Gourdin, 2006; Spekman et al., 1998). According to (Andersson & Norrman, 2002; Boyson, Corsi, Dresner, & Rabinovich, 1999; Dornier, Ricardo, Fender, & Kouvelis, 1998; Stank & Daugherty, 1997), the goals of logistic management include efficiency, reliability, controllability, flexibility, sustainability, and environmental friendliness. In more specific terms, the performance criteria may include short delivery times, minimum stock levels, minimum costs, damage free, vehicle and load tracking, and waste handling and transport. In recent years, the concept of just-in-time (JIT) has been borrowed from manufacturing to streamline logistics service via the efficient flows of materials and information, thus is able to consistently deliver the right product to the right place at the right time (Bottani & Rizzi, 2006). The performance of logistics service is reflected by stable service quality, forecasting accuracy, fault diagnosis capability, responsiveness, information technology, profit/risk sharing and mutual trust with business partners (Bagchi & Virum, 1998; Langley, Allen, & Tyndall, 2002; Lynch, 2000; Tam & Tummala, 2001). In addition, warehouse management, including warehouse location and layout, order picking, items storage/ retrieval operations, customer relationship management has significant impacts on logistics performance (Bottani & Rizzi, 2006). According to the above review, we summarize those performance factors into Table 1 (customer service requirements, CSRs) and Table 2 (logistics operation requirements, LORs), which represent respectively WHAT customers require and HOW the service provider should operate.

3. Basics of QFD, FEAHP and MSGP 3.1. Quality function development (QFD) QFD is a systematic method that provides a means of translating customer requirements into technical requirements for each stage of product development (Bhattacharya, Sarkar, & Mukherjee, 2005; Karsak, Sozer, & Alptekin, 2002; Tseng & Lin, 2011). The successful QFD application may result in greater customer focus, shorter lead times, and knowledge preservation (Liu, 2009). QFD can be applied to practically any manufacturing or service industry, including logistics service (Baki, Basfirinci, Murat, & Cilingir,

55

2009; Behara & Chase, 1993; Bottani & Rizzi, 2006; Lapidus & Schibrowsky, 1994; Stuart & Tax, 1996; Tu, Chang, Chen, & Lu, 2010). As a systematic method, QFD has a twofold implication. First, it supports product planning on the basis of the customer’s voice by a stepwise analysis and deployment of customer requirements (Akao, 1990). Second, it requires the collaboration between different business areas as a prerequisite for the design tasks (Bottani & Rizzi, 2006). As the basis of QFD, the customer requirements planning matrix (a.k.a. ‘‘House of Quality’’, HOQ) consists of seven components: (1) customer requirements (CRs), (2) importance of customer requirements, (3) engineering characteristics (ECs), (4) relationship matrix for CRs and ECs, (5) correlation among ECs, (6) benchmark analysis, and (7) prioritization of design requirements (Chan & Wu, 2002; Tseng & Lin, 2011). By developing the HOQ (Fig. 1), the design team transforms the customers’ requirements (WHATs) into the engineering characteristics (HOWs) of the product or service. 3.2. Fuzzy extended analytic hierarchy process (FEAHP) The analytic hierarchy process (AHP) (Saaty, 1980) has been widely used to address multi-criteria decision-making (MCDM) problems, which is about evaluation and ranking a set of competing alternatives according to multiple criteria involving subjective judgment of hierarchical structure (Cho & Cho, 2008; Liao & Kao, 2010; Önü & Soner, 2008). The AHP consists of six essential steps (Lee, Kang, & Chang, 2009; Murtaza, 2003): (1) define the unstructured problem, (2) develop the AHP hierarchy, (3) perform pairwise comparison among decision factors, (4) estimate the relative weights of the decision factors, (5) check the consistency property of matrices, and (6) obtain the overall rating for the alternatives. Whereas fuzzy set theory has proven advantages to approximate uncertain, imprecise and vague information, the fuzzy AHP approach is the fuzzy extension AHP (FEAHP) to handle the fuzziness of data involved in the MCDM problems (Bozbura, Beskese, & Kahraman, 2007; Cheng, 1999; Lee et al., 2009; Liao, 2011; Murtaza, 2003; Önü & Soner, 2008). A fuzzy set is characterized by a membership function that assigns each object a grade of membership ranging from 0 to 1. In this set, general terms such as ‘‘large’’, ‘‘medium’’, and small’’ are used to capture a range of numerical values. Among various representations, triangular fuzzy numbers are most popular for applications (Chan & Kumar, 2007). As shown in Fig. 2, if n1, n2 and n3 denote the smallest possible values, the most promising value and the largest possible value that describe a fuzzy event, respectively, then the triangular fuzzy number can be denoted as a triplet e (n1, n2, n3) where n1 6 n2 6 n3 . In Fig. 2, triangular fuzzy number M is represented by (n1, n2, n3), and the membership function can be defined as

Table 1 Customer service requirements. Service requirements (‘‘WHATs’’)

Description

Lead-time Flexibility Reliability Regularity Completeness Accuracy Fill rate Correctness Harmfulness Productivity Frequency Organization accessibility Complaints management

Time period passing from customer’s order until receipt Capability to modify orders in terms of due date and quantity when required by customers Capability to deliver orders within the due date The dispersion around the mean value of the delivered lead-time Capability to deliver full orders when required by customers Avoidance of mistakes and damages in orders delivered process The percentage of units available when requested by customers Avoidance of mistakes in orders delivered Avoidance of damages in orders delivered process Number of item produced in a given time period Number of deliveries accomplished in a given time period Customer’s opportunity to establish a contact with firm’s staff Process subsequent to the recognition of some errors in service provided, that allows service quality standards to be reestablished

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Table 2 Logistics operation requirements. Operation requirements (‘‘HOWs’’)

Description

Just-in-time Forecasting methods Information technology Information sharing and trust Service quality

The provision of right materials at the right price, of the right quality, in right quantity, at the right time and from the right source The process of using models to generate predictions for future demands based on known past events Generic term used to include software, hardware and networking technologies Mutual trust-based information sharing between the customer and the provider

Long-term trade relationship Order picking performance Warehouses lay-out performance Customer relationship management Risk management Customer relationship management

Various aspects including on-time delivery, accuracy of order fulfillment, frequency/cost of loss and damage, promptness in attending customers’ complaints, and commitment to continuous improvement The relationship involves shared risks/rewards and cooperation between customers and providers Performance concerning the activity by which a number of goods are retrieved from a warehousing system to satisfy a number of customer orders Performance concerning the assignment of items to storage locations, the arrangement of the functional areas of the warehouse, the number and location of docks and input/output points, the number of aisles, etc. A generic term which encompasses methodologies, software, and Internet capabilities that help the firm to manage customer relationships in an organized way The capability of the provider to address any unforeseen problem and to ensure the continuity of the services CRM is a generic term which include method software, and Internet technology that help the firm to manage customer relationships action

u M~ ( x) Correlation matrix

Relationship matrix

Benchmark analysis

Customer service requirements (CSRs) (WHATs)

Importance rating

Engineering characteristics (ECs) (HOWs )

0

n1

n3

n2

x

e = (n1, n2, n3). Fig. 2. Membership function of a triangular funny number M

Importance of ECs

M1

M2

1 Fig. 1. The house of quality (HOQ).

8 > < ðx  n1 Þ=ðn2  n1 Þ; x 2 ½n1 ; n2 ; uM~ ðxÞ ¼ ðn3  xÞ=ðn3  n2 Þ; x 2 ½n2 ; n3 ; > : 0 otherwise;

ð1Þ

where uM~ ðxÞ 2 ½0; 1 and the value uM~ ðxÞ at x represents the grade of membership of x in A and is interpreted as the membership degree to which x belongs to M. So the closer the value uM~ ðxÞ is to 1, the more x belongs to M; 1 < n1 6 n2 6 n3 < 1. In this study, the FEAHP method by Chan and Kumar (2007) is applied because it is simple and requires less computational effort (Lee et al., 2009). By applying the FEAHP method, triangular fuzzy numbers are used for the preferences of one criterion over another to calculate the synthetic extent value of the pairwise comparison. Then, the weight vectors are calculated and normalized, and thus the normalized weight vectors are determined. Therefore, based on the different weights of the criteria, the final priority weights are determined. The computational procedure is as follows. Firstly, we define two triangular fuzzy numbers: þ þ  M 1 ðm 1 ; m1 ; m1 Þ and M 2 ðm2 ; m2 ; m2 Þ as shown in Fig. 3. When þ þ  m 1 P m2 , m1 P m2 , and m1 P m2 , we define the degree of possi-

u (d)

m2−

m2

m1−

d

m2+

m1

m1+

Fig. 3. Two triangular funny numbers M1 and M2 (Lee et al., 2009).

bility VðM 1 P M 2 Þ ¼ 1. Otherwise, according to (Chang, 1996; Lee et al., 2009; Liao, 2011), we can calculate the ordinate of the highest intersection point, u(d):

uðdÞ=uðm1 Þ ¼ ðm1  dÞ=ðm1  m1 Þ;

ð2Þ

and

uðdÞ=uðm2 Þ ¼ ðm2  dÞ=ðm2  m2 Þ;

ð3Þ

then

uðdÞ ¼ ðm1 þ mþ2 Þ=ððm1  m1 Þ  ðmþ2  m2 ÞÞ 6 1:

ð4Þ

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The value of fuzzy synthetic extent with respect to the ith criterion for m goals is defined in the following steps: Step 1. Decision makers (DMs) are required to make pairwise comparisons between decision alternatives and criteria using a point scale. The necessary matrices are constructed after all pairwise comparisons are obtained from individual DMs. Step 2. The fuzzy synthetic extent value (Fi) with respect to the ith object is defined as:

" #1 m n X m X X Fi ¼ M ij  M ij ; j¼1

However, when multiple-segment aspiration levels exist, such as ‘‘something more/higher is better’’ or ‘‘something less/lower is better’’, these problems cannot be solved using a traditional GP approach. Regarding this matter, Liao (2009, 2011) proposed a multi-segment goal programming (MSGP) method to solve the multi-segment aspiration levels problems in which decision makers can set multiple aspiration levels for each segment level. The objective function and constraints of MSGP are as follows: 3.3.1. MSGP model

i ¼ 1; 2; . . . ; n:

ð5Þ

i¼1 j¼1

Pm

To obtain j¼1 M ij , the fuzzy addition operation of extent analysis values for a particular matrix is performed as m X M ij ¼

! m m m X X X  þ M ij ; Mij ; M ij ;

j¼1

j¼1

j¼1

i ¼ 1; 2; . . . ; n:

ð6Þ

j¼1

P  Pm n To obtain i¼1 ; j¼1 ; M ij , the fuzzy addition operation of values is performed as n X m X M ij ¼

! n n n X X X M ij ; M ij ; Mþij ;

i¼1 j¼1

i¼1

i¼1

j ¼ 1; 2; . . . ; m:

ð7Þ

i¼1

And the inverse of the previous vector is computed as

" #1 n X m X M ij ¼

1

, n X m X

i¼1 j¼1

Mþij ; 1

, n X m X

i¼1 j¼1

, ! n X m X  Mij ; 1 Mij :

i¼1 j¼1

i¼1 j¼1

ð8Þ þ M 1 ðm 1 ; m1 ; m1 Þ

þ M 2 ðm 2 ; m2 ; m2 Þ

Step 3. As and are two triangular fuzzy numbers, the degree of þ þ  M 1 ðm 1 ; m1 ; m1 Þ P M 2 ðm2 ; m2 ; m2 Þ. Using Eqs. (2)–(4), the ordinate of the highest intersection point can be calculated as:

VðM 2 P M 1 Þ ¼ hgtðM1 \ M2 Þ ¼ uðdÞ m1  mþ2 : ¼ ðm2  mþ2 Þ  ðm1  m1 Þ

ð9Þ

Step 4. The degree of possibility that the convex fuzzy number (V(F)) is greater than I convex fuzzy Fi (i = 1, 2, . . . , I) can be defined as follows:

VðF P F 1 ; F 2 ; . . . ; F I Þ ¼ min VðF P F i Þ;

ð10Þ

and

dðF i Þ ¼ min VðF i P F I Þ ¼ w0i ;

I ¼ 1; 2; . . . ; n;

and i – I:

Based on the above process, we can calculate the weights vector of the criteria as: T

ð12Þ

Step 5. After normalizing, we get the normalized weight (wi) as follows:

wi ¼ ðw1 ; w2 ; . . . ; wn ÞT ;

n X þ  wi ðdi þ di Þ

ð14Þ

i¼1 þ



Subject to f i ðxÞ þ di  di ¼ g i ; i ¼ 1; 2; . . . ; n; m X f i ðxÞ ¼ sij Bij ðbÞ  xi ;

ð15Þ

j¼1

sij Bij ðbÞ 2 Ri ðxÞ; þ  di ; di

X2F

P 0;

i ¼ 1; 2; . . . ; n;

j ¼ 1; 2; . . . ; m;

ð16Þ

i ¼ 1; 2; . . . ; n;

ðF is a feasible setÞ;

where fi(x) is goal function, wi represents the weight attached to the þ  deviation; di and di are the positive and negative deviations from þ the target value gi, respectively. In addition, di ¼ maxð0; fi ðxÞ  g i Þ  and di ¼ maxð0; g i  fi ðxÞÞ denote under- and over-achievements of the ith goal, respectively. The sij is a variable coefficient that represents the multi-segment aspiration levels of the jth segment of the ith goal; Bij(b) represents a function of a binary serial number, and Ri(x) is the function of resource limitations. 4. The proposed method For determining the optimal level of a logistics operational performance, we adopt QFD as the framework to integrate FEAHP and MSGP. The proposed method consists of nine steps as follow. 4.1. Step 1. Define the scope of logistics service Firstly, it is beneficial to organize a committee of stakeholders, such as the managers, consultants, and customers. The committee members then define the customer service requirements (CSRs) and logistics operation requirements (LORs), respectively.

ð11Þ

(w0i )

w0i ¼ ðw01 ; w02 ; . . . ; w0n Þ :

Minimize

ð13Þ

where wi is a non-fuzzy number and indicates the priority weights of one alternative over another.

4.2. Step 2. Construct the HOQ of logistics service In the QFD process, the HOQ is an effective method to define necessary operations that will satisfy customer requirements (Karsak et al., 2002; Lee, Kang, Yang, & Lin, 2010). In the HOQ (shown in Fig. 4), the CSRs and LORs are listed in the HOQ first. Then the relationships (Rij) between CSRs and LORs, inner dependency (Tkj) among LORs, the priority (RIj) and targets (RIj ) of logistics operation can be defined respectively.

3.3. Multi-segment goal programming (MSGP)

4.3. Step 3. Formulate the questionnaire and construct the pairwise comparison matrix

Goal Programming (GP) has been widely applied due to its flexibility in handling multi-objective problems, when the decision maker aims to minimize the deviation between the achievement of goals and their aspiration levels (Chang, 2007; Romero, 2001). The key element of a GP model is the achievement function that represents a mathematical expression of the unwanted deviation variables.

A questionnaire is formulated to compare CSRs pairwise in their contribution toward achieving the best logistic operations. A fuzzy number is used to represent the pairwise comparison value of the overall objective. The committee members’ opinions are then collected and combined into a fuzzy pairwise comparison matrix in ~ 3; ~ 5; ~ are defined ~ 7; ~ and 9 which the triangular fuzzy numbers 1; in Table 3.

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C.-N. Liao, H.-P. Kao / Computers & Industrial Engineering 68 (2014) 54–64

Logistics operation requirements (LORs)

Inner dependence among the LORs

(Tkj )

Customer service requirements (CSRs)

HOWs

WHATs

Relative importance of the CSRs

Inner dependence among the CSRs

Relationship between WHATs and HOWs

(Rij )

(RIj) (RI*j )

Overall priorities of the LORs

Normalized crisp values (e.g., FEAHP-QFD)

Additional goals Fig. 4. HOQ-logistics applications.

4.4. Step 4. Calculate the priority weights of customer service requirements

Table 3 Characteristic function of the fuzzy numbers (Lee et al., 2009).

The committee members’ opinions are aggregated and pairwise comparison matrices of CSRs are built. If there are k members, a total of k sets of pairwise comparison matrices should be available. Let A represent a n  n pairwise comparison matrix that can be expressed as

2

1 6 1=a 6 ~12 e ¼ ½a ~ij  ¼ 6 A 6 .. 4 . ~1n 1=a

~12 a 1 .. . ~2n 1=a

~1n 3  a ~2n 7  a 7 7 .. 7; . 5  

i; j ¼ 1; 2; . . . ; n;

ð17Þ

1

~ij ¼ ðx ; x; xþ Þ. where n is the number of CSRs and a Fuzzy matrices are combined into an integrated matrix and checked for consistency. Let B represent a n  n pairwise comparison matrix of committee members that can be expressed as

2

1 6 ~ 6 1=b12 ~ ¼6 e ¼ ½b B 6 . ij 6 . 4 . ~1n 1=b

~12 b 1 .. . ~2n 1=b

3

~1n  b ~2n 7 7  b 7 ; .. 7 7 . 5   1

i; j ¼ 1; 2; . . . ; n;

ð18Þ

~ij can be obtained by combining the then, triangular fuzzy number b committee members’ opinions,

~ ¼ ðm ; m ; mþ Þ; b ij ij ij ij

ð19Þ

where

rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi YK k ¼ x ; k¼1 ijk rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi YK k mij ¼ x ; k¼1 ijk rffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi YK k mþij ¼ xþ ; k¼1 ijk

mij

Fuzzy number

Characteristic function

~ 1 ~ x ~ 9

(1, 1, 3) (x  2, x, x + 2) for x = 3, 5, 7 (7, 9, 9)

The crisp relative importance weights (priority vectors) for CSRs can be obtained using FEAHP. Using Eqs. (2)–(13), we calculate the weights, w0i , of the factors. After normalizing w0i , the normalized weight vectors of CSRs are wi. 4.5. Step 5. Determine the relationships, Rij, between CSRs and LORs and the correlation, Tkj, between LORs In the HOQ, the degrees of the relationship, Rij, between CSRs and LORs are stated as the triangular fuzzy numbers (TFNs), which are defined in Table 4. The degrees of correlation, Tkj, among LORs are also stated as TFNs, which are defined in Table 5. The TFNs can be denoted as a triplet (n1, n2, n3), as shown in Fig. 2. 4.6. Step 6. Calculate the relative importance (RIj) and priority weights of LORs (RIj ) The purpose of computing these two parameters is to determine the relative effect (priority weights) of individual LORs. RIj is computed by fuzzy multiplication of wi and Rij.

RIj ¼

n X

wi  Rij ;

j ¼ 1; . . . ; m;

ð23Þ

i¼1

ð20Þ ð21Þ

where Rij in the matrix represents the relationship between the jth LORs and the ith CSRs. Again, RIj is the degree of importance for the jth LORs (j = 1, . . . , m).

ð22Þ

RIj ¼ RIj 

þ and ðx ijk ; xijk ; xijk Þ is the importance weight from expert k, and i, j = 1, 2, . . . , n.

X T kj  RIk ;

j ¼ 1; . . . ; m;

ð24Þ

k¼j

where Tkj, j; k ¼ 1; . . . ; m; k–j, in the matrix represents the correlation between the kth and the jth LORs.

C.-N. Liao, H.-P. Kao / Computers & Industrial Engineering 68 (2014) 54–64 Table 4 Degree of relationship between CSRs and LORs, symbols and corresponding fuzzy numbers. Degree of relationship (Rij)

Symbol

Fuzzy number (n1, n2, n3)

Strong Medium Weak

d  N

(0.7, 1, 1) (0.3, 0.5, 0.7) (0, 0, 0.3)

Table 5 Degree of correlation among LORs, symbols and corresponding fuzzy numbers. Degree of correlation (Tkj)

Symbol

Fuzzy number (n1, n2, n3)

Strong positive Positive

d

(0.7, 1, 1) (0.5, 0.7, 1)

Negative Strong negative

h N

(0, 0.3, 0.5) (0, 0, 0.3)

4.7. Step 7. Calculate and normalize crisp relative importance weights To rank the LORs, the crisp values are obtained by defuzzification. Suppose N = (n1, n2, n3) is a TFN; then the defuzzified value is computed by

ðn1 þ 4n2 þ n3 Þ=6;

ð25Þ

furthermore, normalization is performed by dividing each LOR by the total scores of LORs. Therefore, we can obtain the normalized crisp values (e.g., Fig. 4); it denoted the weight of FEAHP-QFD goals and it will be use in Fig. 5 in this case. 4.8. Step 8. Obtain the priorities of additional goals In this step, MSGP can be incorporated to consider other logistic goals, such as cost, ability, time, and extendibility of logistics service. These goals help in determining priorities and directions for improvement. Since the committee members’ opinions in the questionnaire have been aggregated, the priority weights (xi) with respect to each goal can be calculated using AHP. 4.9. Step 9. Formulate and solve the MSGP model In this step, we apply MSGP because it can handle multiple objectives and minimizes the total deviation from the desired goals. To implement MSGP in this study, the MSGP can be reformulating as below (see Liao, 2011):

Minimize

Z¼

n X

xi ðdþi þ di þ eþi þ ei Þ

ð26Þ

i¼1

Subject to

m X þ  Lw ij ðsij Bij ðbÞÞ  X i  di þ di ¼ g i ;

i ¼ 1; 2; . . . ; n; ð27Þ

j¼1

 1  max bi sij þ ð1  bi Þsmin  eþi þ ei ij Si ðsmax or smin ij ij Þ ¼ þ 1; i ¼ 1; 2; . . . ; n; Si X i ; bi 2 f0; 1g; i ¼ 1; 2; . . . ; n; þ



di ; di ; eþi ; ei P 0 i ¼ 1; 2; . . . ; n;

ð28Þ ð29Þ

59

example. Since established in 1987, this LSP has played a pivotal role serving wholesale and retail clients. Before 2010, the average service level was 92% to meet the promised delivery date. However, most clients demand better service with shorter delivery time, greater order accuracy and lower service charge. In brief, this LSP needed to transition from traditional operations to JIT service, thus was able to retain clients and beat the competition. Owing to the general manager’s background in quality management, he accepted the professional advice from the first author of this paper to use QFD to improve service performance. Following a prudent process, the LSP organized a committee which includes two top managers of the LSP, two representatives of the major clients and one consultant. Base on the literature reviews, data analysis and using Delphi technique the committee members then define six CSRs and eight LORs criteria. A sample list of both types of requirements in this work is shown in Table 6. The descriptions of six CSRs criteria are defined as follows: Lead-time: The time period passing from customer’s order until receipt. Accuracy: Avoidance of mistakes and damages in orders delivered process for customer. Fill rate: The percentage of units available for customer’s requirement. Flexibility: Capability to modify orders in terms of due date and quantity for customer’s requirement. Reliability: Capability to deliver orders within the due date for customer. Frequency: Number of deliveries accomplished in a given time period for customer. In addition, the descriptions of eight LORs are defined as follows: Just-in time (x1): The provision of right materials at the right price, quality, quantity, right time and from the right source. Information technology (x2): Generic term used to include software, hardware and technologies such as services, computer networks and expert systems. Order picking optimization (x3): Performance about the activity by which various goods are retrieved from a warehousing system to satisfy various customer orders. Demand forecasting methods (x4): The process of using models to generate predictions for future demands. Quality of services (x5): Including on-time delivery, accuracy of order fulfillment, frequency/cost of loss and damage, promptness in attending customers’ complaints, and commitment to continuous improvement. Customer relationship management (x6): A generic term which use software, information and Internet technology that help the firm to manage customer relationships action. Warehouses lay-out optimization (x7): Performance about storage locations, areas of the warehouse, number and input/output points, and aisles lay-out, etc. Information sharing and mutual trust (x8): The mutual trustbased information sharing between the customer and the provider.

ð30Þ

where Si ¼ smax  smin ij ij . All other variables are defined as in the original MSGP model, which is shown in Eqs. (14)–(16). 5. Application of proposed method To demonstrate the application of the proposed method, a midsized logistics service provider (LSP) in Taiwan is presented as an

They were asked to rate individual CSRs and LORs and their degree of relationship. The team was also asked to determine the degrees of interdependence among LORs in a four-degree scale. Documented in three tables (Tables 3–5), all the linguistic terms were presented with graphic symbols and corresponding fuzzy numbers. In addition, each committee member independently filled up a questionnaire that compared the relative importance between CSRs in pairs.

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Fig. 5. FEAHP-QFD of the case study.

Table 6 Customer service requirements and logistics operation requirements. CSRs

LORs

Lead-time Accuracy Fill rate Flexibility Reliability Frequency

Just-in time Information technology Order picking optimization Demand forecasting methods Quality of services Customer relationship management Warehouses lay-out optimization Information sharing and mutual trust

With the obtained data from the committee, the authors of this study went further to apply the proposed method. Firstly, a HOQ was constructed as shown in Fig. 5. Then, in the following steps, we applied FEAHP to calculate the relative weights of the criteria (wi) for individual CSRs. (1) Transform the committee members’ opinions into triangular fuzzy numbers, and obtain the pairwise comparison matrix shown in Table 7. (2) Use geometric means to calculate the fuzzy numbers by using Eqs. (19)–(22).

(3) Perform the consistency test to confirm the integrated fuzzy matrix is consistent. Pm j Pn Pm j In this step, we calculated and j¼1 M s1 ; i¼1 j¼1 M si , hP P i1 n m j respectively by using the fuzzy numbers in i¼1 j¼1 M si Table 7. P j The m j¼1 M s1 values can be calculated by using Eq. (6) and add individual rows values from Table 7 as follows: m X Mjs1 ¼ ð1; 1; 1Þ  ð0:25; 1:72; 3:94Þ      ð1:25; 2:67; 4:83Þ j¼1

¼ ð6:49; 10:42; 21:20Þ: Following the same procedure, we obtained m X Mjs2 ¼ ð5:75; 9:61; 18:07Þ;

m X

j¼1

j¼1

Mjs3 ¼ ð4:52; 6:89; 12:69Þ;

m X Mjs4 ¼ ð3:88; 5:29; 10:00Þ;

m X M js5 ¼ ð3:05; 4:75; 8:13Þ;

j¼1

j¼1

and

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C.-N. Liao, H.-P. Kao / Computers & Industrial Engineering 68 (2014) 54–64 m X M js6 ¼ ð2:88; 4:51; 5:67Þ: j¼1

Using Eq. (7) and add total rows and columns values from Table 7, we obtained n X m X M jsi ¼ ð1; 1; 1Þ  ð0:25; 1:72; 3:94Þ      ð0:37; 0:83; 1Þ i¼1 j¼1

VðF 4 P F 1 Þ ¼ 0:701;

VðF 4 P F 2 Þ ¼ 0:743

VðF 4 P F 3 Þ ¼ 0:891;

VðF 4 P F 5 Þ ¼ 1;

VðF 4 P F 6 Þ ¼ 1; VðF 5 P F 1 Þ ¼ 0:617;

VðF 5 P F 2 Þ ¼ 0:662;

VðF 5 P F 3 Þ ¼ 0:826;

VðF 5 P F 4 Þ ¼ 0:951;

VðF 5 P F 6 Þ ¼ 1;  ð1; 1; 1Þ

and

¼ ð26:57; 41:47; 75:75Þ: In addition, the inverse of fuzzy numbers, (26.57, 41.47, 75.75) can be calculated by using Eq. (8) as follows:

¼ ð0:013; 0:024; 0:038Þ: Therefore, the fuzzy synthetic degree of CSRs can be calculated by using Eq. (5), as follows:

" #1 m n X m X X j j F1 ¼ M s1  M si i¼1 j¼1

¼ ð0:086; 0:251; 0:798Þ: Following the same procedure of calculation, other five fuzzy synthetic degrees were obtained as:

F 2 ¼ ð0:076; 0:232; 0:680Þ; F 3 ¼ ð0:060; 0:166; 0:478Þ; F 4 ¼ ð0:051; 0:128; 0:376Þ; F 5 ¼ ð0:040; 0:114; 0:306Þ; F 6 ¼ ð0:038; 0:109; 0:213Þ: For the comparison of F1 and F2 both the values of VðF 1 P F 2 Þ and VðF 2 P F 1 Þ are as below:

VðF 1 P F 3 Þ ¼ 1; VðF 1 P F 5 Þ ¼ 1;

VðF 1 P F 6 Þ ¼ 1; and by using Eq. (9) the ordinate of the highest intersection point can be calculated as

VðF 2 P F 1 Þ ¼ ðn1  nþ2 Þ=ððn1  n1 Þ  ðnþ2  n2 ÞÞ; ¼ 0:086  0:680=ðð0:086  0:251Þ  ð0:680  0:232ÞÞ ¼ 0:968; VðF 2 P F 3 Þ ¼ 1;

VðF 2 P F 4 Þ ¼ 1;

VðF 2 P F 5 Þ ¼ 1;

VðF 2 P F 6 Þ ¼ 1:

Following the same procedure, we obtained

VðF 3 P F 1 Þ ¼ 0:822; VðF 3 P F 4 Þ ¼ 1;

mðF 1 Þ ¼ min VðF 1 P F 2 ; F 3 ; F 4 ; F 5 ; F 6 Þ ¼ minð1; 1; 1; 1; 1Þ ¼ 1; Similarly, m(F2) = 0.968, m(F3) = 0.822, m(F4) = 0.701, m(F5) = 0.617 and m(F6) = 0.472. Therefore, the weight vector, w0i , is given as

w0i ¼ ð1; 0:968; 0:822; 0:701; 0:617; 0:472ÞT ;

¼ ð6:49  0:013; 10:42  0:024; 21:20  0:038Þ

VðF 1 P F 4 Þ ¼ 1;

VðF 6 P F 4 Þ ¼ 0:896;

Using Eqs. (10) and (11), the minimum degree of possibility can be calculated as follows:

i¼1 j¼1

VðF 1 P F 2 Þ ¼ 1;

VðF 6 P F 2 Þ ¼ 0:528;

VðF 6 P F 3 Þ ¼ 0:728; VðF 6 P F 5 Þ ¼ 0:968:

" #1 n X m X j M si ¼ ð1=75:75; 1=41:47; 1=26:57Þ

j¼1

VðF 6 P F 1 Þ ¼ 0:472;

VðF 3 P F 2 Þ ¼ 0:860;

VðF 3 P F 5 Þ ¼ 1;

VðF 3 P F 6 Þ ¼ 1;

i ¼ 1; 2; . . . ; 6;

and after the normalization process, the relative weight with respect to CSRs (e.g., lead-time, accuracy, fill rate, flexibility, reliability and frequency, respectively) were obtained as

wi ¼ ð0:218; 0:211; 0:179; 0:153; 0:135; 0:103ÞT ;

i ¼ 1; 2; . . . ; 6;

and as show in Table 7. Since the objective is to maximize the satisfaction levels regarding logistics operations between CSRs and LORs, the major goal is to maximize FEAHP-QFD (G1), along with several additional goals, such as logistics cost (G2), logistic ability (G3), logistics time (G4), and extendibility (G5). Logistics cost refers to the total service cost of logistics actions that may be incurred while developing a specific LORs and that cost should be minimized. Logistics time is the lead-time that may be incurred in developing specific LORs, and this cost should also be minimized. Logistic ability – which should be maximized – is the expected operation ability of the logistics industry with regard to specific LORs. Extendibility, which also should be maximized, indicates the flexibility benefits that can be obtained from developing specific LORs. By using Eqs. (17)–(25), we can calculate the normalized crisp values; it denoted the weight of FEAHP-QFD goals (G1) of LORs are shown in Fig. 5 (e.g., 0.292, 0.305, 0.016, 0.130, 0.015, 0.089, 0.062 and 0.090). Moreover, By applying AHP method, we can obtained the priority weights of LORs with respect to logistics operational goals (G2 to G5). For example, the relative importance of the LORs with respect to logistics cost (G2) are shown in Table 8. The same procedure is carried out for calculating the relative importance of other LORs, and the results are shown in the bottom three rows of Fig. 5. By following the same procedure, we can calculate the importance weights of the logistics operational goals (G1 to G5). The pair-

Table 7 Fuzzy pairwise comparison of logistic operation requirements from five DMs.

Lead-time Accuracy Fill rate Flexibility Reliability Frequency

Lead-time

Accuracy

Fill rate

Flexibility

Reliability

Frequency

wi

(1, 1, 1) (0.25, 0.58, 0.80) (0.27, 0.64, 1) (0.25, 0.25, 1) (0.25, 0.52, 1) (0.27, 0.44, 0.83)

(0.25, 1.72, 3.94) (1, 1, 1) (0.25, 0.52, 1) (0.33, 1, 1) (0.23, 0.47, 0.80, ) (0.27, 0.41, 0.83, )

(1, 1.55, 3.68) (1, 1.93, 4.08) (1, 1, 1) (0.27, 0.64, 1) (0.25, 0.52, 1) (0.58, 0.83, 1)

(1, 1.55, 3.68) (1, 1, 3) (1, 1.55, 3.68) (1, 1, 1) (0.33, 1, 1) (0.4, 1, 1)

(1, 1.93, 4.08) (1.25, 2.14, 4.36) (1, 1.93, 1.08) (1, 1, 3) (1, 1, 1) (0.37, 0.83, 1)

(1.25, 2.67, 4.83) (1.25, 2.95, 4.83) (1, 1.25, 1.93) (1, 1, 3) (1, 1.25, 3.32) (1, 1, 1)

0.218 0.211 0.179 0.153 0.135 0.103

62

C.-N. Liao, H.-P. Kao / Computers & Industrial Engineering 68 (2014) 54–64

wise comparison matrix and the resulting weight vectors (xi) are given in Table 9. Hitherto the committee had determined the essential measures from the FEAHP-QFD study. However, considering business strategy the CEO and top managers of LSP establish a cost goal. According to the logistic cost record in the last five years of the LSP, the monthly budget of logistic cost is set between US$ 35,000 and US$ 50,000. According to their request, we formulated the below MSGP model, in which the five goals in logistics service selection are defined as follows: G1: G2: G3: G4: G5:

f1(x) = 1, f2(x) = 0, f3(x) = 1, f4(x) = 0, f5(x) = 1,

and and and and and

is is is is is

to to to to to

þ



Minimize Z ¼ 0:129ðd1 þ d1 Þþ þ 0:269ðd2 þ 0:060ðd3 þ 0:348ðd4 þ 0:195ðd5

þ þ þ þ

 d2 Þ þ  d3 Þþ  d4 Þþ  d5 Þ

ð31aÞ ðeþ1

þ

e1 Þþ

ð31bÞ ð31cÞ ð31dÞ ð31eÞ

Subject to 0:292x1 þ 0:305x2 þ 0:016x3 þ 0:130x4 þ 0:015x5 þ



ð32Þ



ð33Þ

þ 0:089x6 þ 0:062x7 þ 0:090x8  d1 þ d1 ¼ 1; 0:078x1 þ 0:062x2 þ 0:031x3 þ 0:217x4

maximize the FEAHP-QFD; minimizes the logistics cost; maximize the logistic ability; minimizes the logistic time; maximize the extendibility.

þ 0:204ð50; 000b1 þ 35; 000ð1  b1 ÞÞx5 þ

þ 0:131x6 þ 0:041x7 þ 0:237x8  d2 þ d2 ¼ 0; ð1=15; 000Þð50; 000b1 þ 35; 000ð1  b1 ÞÞ  eþ1 þ e1 ¼ 3:333 ;

ð34Þ

0:065x1 þ 0:088x2 þ 0:028x3 þ 0:083x4 þ 0:138x5 þ

In addition, this work uses the following define.

ð35Þ

0:197x1 þ 0:211x2 þ 0:152x3 þ 0:031x4 þ 0:045x5

Objective functions (31a) Satisfy FEAHP-QFD goal (G1) (31b) Satisfy logistics cost goal (G2) (31c) Satisfy logistic ability goal (G3) (31d) Satisfy logistic time goal (G4) (31e) Satisfy extendibility goal (G5)

þ



þ 0:059x6 þ 0:278x7 þ 0:027x8  d4 þ d4 ¼ 0;

ð36Þ

0:074x1 þ 0:131x2 þ 0:034x3 þ 0:151x4 þ 0:285x5 þ

FEAHP-QFD goal, and the more the better logistics cost goal, and the less the better minimize logistics cost goal of x5 logistic ability goal, and the more the better logistic time goal, and the less the better extendibility goal, and the more the better

Deviation variables þ  d1 ; d1 The positive and negative deviations from value of 1 þ  d2 ; d2 The positive and negative deviations from value of 0 þ  d3 ; d3 The positive and negative deviations from value of 1 þ  d4 ; d4 The positive and negative deviations from value of 0 þ  d5 ; d5 The positive and negative deviations from value of 1  The positive and negative deviations from eþ 1 ; e1 value of 3.333* (*Please see Appendix A)

the target the target the target the target the target the target

ð37Þ

b1 2 f0; 1g;

ð38Þ

G2

JIT

IT

OPO

DFM

QS

CRM

WLO

IST

wG2

JIT IT OPO DFM QS CRM WOL IST

1 0.5 0.333 3 3 3 0.333 4

2 1 0.5 4 2 3 0.333 5

3 2 1 5 6 3 3 6

0.333 0.25 0.2 1 2 0.5 0.2 0.5

0.333 0.5 0.167 0.5 1 0.5 0.2 2

0.333 0.333 0.333 2 2 1 0.333 2

3 3 0.333 5 5 3 1 5

0.25 0.2 0.167 2 0.5 0.5 0.2 1

0.078 0.062 0.031 0.217 0.204 0.131 0.041 0.237

JIT = Just-in time, IT = information technology, OPO = order picking optimization, DFM = demand forecasting methods, QS = quality of services, CRM = customer relationship management, WOL = warehouses lay-out optimization and IST = information sharing and mutual trust.

i ¼ 1; 2; . . . ; 5;

ð39Þ

eþi ; ei

i ¼ 1;

ð40Þ

P 0;

x1 ¼ x2 ¼ x5 ¼ x6 ¼ x8 ¼ 1; þ



d1 ¼ 0;

d1 ¼ 1;

þ d4

 d4

þ

Table 8 The pairwise assessment for the alternatives with respect to logistics cost.



di ; di P 0;

whereas other variables are defined in the MSGP model, which is introduced in Section 3.3. (*Please see Appendix A.) Specifically, the objective functions including Eqs. (31a)–(31e) are the objective function which minimizes the total weighted deviation from all the goals, including FEAHP-QFD, logistics cost, logistic ability, logistics time and extendibility; the weights coefficients are taken from the last column (e.g., xi) in Table 9. Eqs. (32)– (37) are the constraints associated with individual goals, whereas the coefficients are taken from Fig. 5, i.e., FEAHP-QFD. Eqs. (38)– (40) define the variables of the model. The MSGP model given is solved by using the software package LINGO (Schrage, 2002). The results summarized as follows:

¼ 0;

d5 ¼ 0; Using the parameters was taken from the previous steps that the final MSGP model which integrates FEAHP can be shown as:



þ 0:199x6 þ 0:059x7 þ 0:068x8  d5 þ d5 ¼ 1; þ

Constraints (32) For (33) For (34) For (35) For (36) For (37) For



þ 0:173x6 þ 0:116x7 þ 0:224x8  d3 þ d3 ¼ 1;



þ

x3 ¼ x4 ¼ x7 ¼ 0; 

þ

d2 ¼ 0;

d2 ¼ 0;

d3 ¼ 0;

eþ1 ¼ 0;

e1 ¼ 0;

b1 ¼ 1:



d3 ¼ 0;

¼ 0;

d5 ¼ 0;

Therefore, in the best interest of the LSP, it should select just-intime (x1), information technology (x2), quality of services (x5), customer relationship management (x6), and information sharing and þ mutual trust (x8). Moreover, form above results such as d2 ¼ 0,  þ  d2 ¼ 0 e1 ¼ 0;e1 ¼ 0 and b1 = 1, the monthly budget of logistics cost $50,000 were be decided in this case. Table 10 illustrated the preference ranking of LORs with respect to different goals and the final selection using MSGP method. Under the FEAHP-QFD goal (G1), as state before, information technology (x2), just-in-time (x1) and demand forecasting methods (x4) are the top three import LORs. The ranking by logistics cost (G2) shows that information sharing and trust (x8) with rank 1 is expected to incur the lowest logistics cost while order picking optimization (x3) with rank 8 the highest logistics cost. Under the logistic ability (G3), information sharing and trust (x8) ranks the first, followed by customer relationship management (x6) and quality of services (x5). The ranking by logistics times (G4) shows that warehouses lay-out optimization (x7) ranks number one and this implies that

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C.-N. Liao, H.-P. Kao / Computers & Industrial Engineering 68 (2014) 54–64 Table 9 Relative importance weights of the logistics operational goals.

FEAHP-QFD Logistics cost Logistic ability Logistic time Extendibility

FEAHP-QFD

Logistics cost

Logistic ability

Logistics time

Extendibility

xi

1 1 0.333 3 4

1 1 0.2 2 0.25

3 5 1 3 4

0.333 0.5 0.333 1 0.5

0.25 4 0.25 2 1

0.129 0.269 0.060 0.348 0.195

Table 10 Rank of logistic operational requirements (LORs) by MSGP and FEAHP-QFD, etc. Logistic operational requirements (LORs)

MSGP selection

Rank by FEAHPQFD (G1)

Rank by logistics cost (G2)

Rank by logistic ability (G3)

Rank by logistics time (G4)

Rank by extendibility (G5)

Just-in-time (x1) Information technology (x2) Order picking optimization (x3) Demand forecasting methods (x4) Quality of services (x5) Customer relationship management (x6) Warehouses lay-out optimization (x7) Information sharing and mutual trust (x8)

Selected Selected

2 1 7 3 8 5

5 6 8 2 3 4

7 5 8 6 3 2

3 2 4 7 6 5

5 4 8 3 1 2

6

7

4

1

7

Selected

4

1

1

8

6

Yes

Yes

No

Yes

No

Yes

Yes

No

Yes

No

Yes

No

Yes

No

No

No

No

No

Considering selection criteria – Qualitative Considering selection criteria – Quantitative Multiple choice aspiration levels

Selected Selected

it is expected to be warehouses lay-out the most easily. The ranking by satisfy extendibility (G5) shows that quality of services (x5), customer relationship management (x6) and demand forecasting methods (x4) are the top three LORs. Through MSGP, the five goals can be considered simultaneously, and five LORs, namely, just-intime (x1), information technology (x2), quality of services (x5), customer relationship management (x6), and information sharing and mutual trust (x8), are be selected. In brief, those five LORs are the most essential (or priority) operational requirements than others three LORs (e.g., order picking optimization, demand forecasting methods and warehouses lay-out optimization) of the logistics service provider in this case. Note that, in Table 10 the optimal solution is not obtained simply based on the G1, G2, G3, G4 or G5 results alone, but also needs to consider other addition goals. For instance, even though warehouses lay-out optimization (x7) rank number one and has a first high priority of 0.036 in logistics times analysis, it is not selected in the end due to the fact that it has a rather low ranking under the goals of logistics cost (ranked 7), logistic ability (ranked 4) and extendibility (ranked 7). The proposed advantage of this method is that it allow for the decision makers to set multiple choice aspiration levels (e.g., qualitative and quantitative criteria) for logistic services strategy selection in which ‘‘the more/higher is better’’ (e.g., benefit criteria) or ‘‘the less/lower is better’’ (e.g., cost criteria) (Liao, 2013). In consequence, MSGP model can indeed solve the logistic services design problems by simultaneously considering customer demands on logistic operational requirements characteristics and also other important goals.

service for characterizing customer service requirements and logistics operation requirements. By considering both customer’s expectations and service provider’s perceptions, we take a novel approach for designing the logistics service system. We adopt quality function development (QFD) as the analytical framework to integrate the fuzzy extended analytic hierarchy process (FEAHP) and multi-segment goal programming (MSGP). Whereas FEAHP handles the inherent uncertainty of the human judgment and provides the flexibility to comprehend the problem-solving process, MSGP is able to address the multi-segment aspiration levels problems, which involves multiple objectives. To demonstrate the proposed model, we present a practical case of a logistics service company. The importance weights of evaluation criteria, resource limitations, and other design metrics can be incorporated into FEAHP and MSGP models, and the results of computation are aggregated into the QFD framework. In this way, various analytical methods can be conveniently integrated into the design process for real-world applications. Appendix A For constraint (34), by using the right side of Eq. (28): ðsmax or smin Þ ij ij

and

Si smax ij

35;000 þ 1 ¼ 50;00035;000 þ 1 ¼ 3:333, where Si ¼ smax  smin ij ij ,

¼ 50; 000 and smin ¼ 35; 000. Because the logistic cost beij

longs to cost criterion (e.g., the less the better), the element of smin ij will be selected in this case (see Liao, 2011). References

6. Conclusions In an increasingly competitive business environment, a business needs to be innovative its operational system to satisfy customers. In this study, we review existing studies of logistics

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