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Logistics tool selection with two-phase fuzzy multi criteria decision making:A case study for personal digital assistant selection

Glin Bykzkan a,, Jbid Arsenyan b, Da Ruan c

a Department of Industrial Engineering, Galatasaray University, ragan Caddesi No: 36, Ortaky, _Istanbul 34357, Turkeyb Department of Industrial Engineering, Bahesehir University, ragan Caddesi Osmanpasa Mektebi Sokak No: 4 6, Besiktas, _Istanbul 34100, Turkeyc The Belgian Nuclear Research Centre (SCKCEN), Boeretang 200, Mol 2400, Belgium

a r t i c l e i n f o

Keywords:

Fuzzy axiomatic design

Group decision-making

Fuzzy AHP

Fuzzy TOPSIS

Logistics tool selection

Logistics industry

a b s t r a c t

Efficient logistics and supply chain management are enabled through the use of efficient information

technologies (IT). The mobile logistics tools represent the IT interface in the supply chain. This paper aims

to aiddecision makers to identify the most appropriate mobile logistics tools and to achieve this aim, sev-

eral evaluation criteria are identified to evaluate logistics tools, and a fuzzy axiomatic design (FAD) based

group decision-making method is adopted to perform the evaluation in two phases. In the first phase of

pre-assessment, alternatives that cannot meet basic requirements and the defined threshold are elimi-

nated. In the second phase of selection, the remaining alternatives are more meticulously evaluated. Cri-

teria weights are determined using fuzzy analytic hierarchy process (AHP) and another fuzzy multi-

criteria decision-making (MCDM) technique, namely fuzzy technique for order preference by similarity

to ideal solution (TOPSIS), is applied in the second phase to compare the outcome of FAD. A case study

is provided in order to demonstrate the potential of the proposed methodology. Personal digital assis-

tants (PDAs) with integrated barcode scanner that are available in the Turkish market are evaluated.

2011 Elsevier Ltd. All rights reserved.

1. Introduction

Rapid development of mobile communication technology holds

an increasing importance in supply chain and logistics industry.

Mobility generates new opportunities to improve operations in

logistics applications. As real time data is crucial in supply chain

management, mobile logistics tools are widely used and popular

in logistics activities such as warehouse automation, distribution,

inventory control and tracking. Mobile technologies have made

possible for people to work from anywhere at any time via wireless

communication network. With the emerging demand, various

models with cutting edge technology and diverse features such

as barcode scanner, RFID technology, etc. are entering the market.

Selecting the right tool among different product families with dif-

ferent specifications is an important decision problem for logistics

companies. This paper proposes a two-phase decision framework

that provides an effective evaluation of the mobile logistics tool

alternatives. The evaluation criteria for mobile logistics tool are

suggested and a two-phase fuzzy multi-criteria decision making

(MCDM) with group decision methodology is introduced. Also a

case study is proposed to evaluate the personal digital assistants

(PDAs) with integrated barcode scanner, which are hand-held

computers used for mobile data applications in different industries

to improve the effectiveness of the organizations (Hoffer, 2005;

Lee, Cheng, & Cheng, 2007; Madria, Mohania, Bhowmick, & Bharg-

ava, 2002).

MCDM is a powerful tool widely used for evaluating and rank-

ing problems containing multiple, usually conflicting criteria

(Pomerol & Romero, 2000). Given the multi-dimensional charac-

teristics of a mobile technology, MCDM provides an effective eval-

uation framework for mobile logistics tools. MCDM is one of the

rare operations research techniques where decision makers

(DMs) are involved in the solution process. This feature brings

two significant advantages: the problem can easily be structured

corresponding to DMs requests and the results are adopted more

easily by DMs, because, they are actively involved in the solution

procedure (Masud & Ravindran, 2007).

Literature offers many applications of axiomatic design (AD)

methodology to design products, systems, organizations and soft-

ware (Suh, 2001). However, AD principles also provide a powerful

tool to measure how well system capabilities respond to functional

requirements (FRs) and therefore, presents an opportunity for

MCDM. AD consists of two axioms: the independence axiom and

the information axiom. The latter proposes theselection of a proper

alternative that has minimum information content. The conven-

tional information axiom approach is employed in many applica-

tions such as decision making in design (Harutunian, Nordlund,

Tate, & Suh, 1996), design for manufacturing (Goncalves-Coelho &

0957-4174/$ - see front matter 2011 Elsevier Ltd. All rights reserved.doi:10.1016/j.eswa.2011.06.017

Corresponding author. Tel.: +90 212 227 4480.

E-mail address: [email protected] (G. Bykzkan).

Expert Systems with Applications 39 (2012) 142153

Contents lists available at ScienceDirect

Expert Systems with Applications

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e s w a
http://dx.doi.org/10.1016/j.eswa.2011.06.017mailto:[email protected]://dx.doi.org/10.1016/j.eswa.2011.06.017http://www.sciencedirect.com/science/journal/09574174http://www.elsevier.com/locate/eswahttp://www.elsevier.com/locate/eswahttp://www.sciencedirect.com/science/journal/09574174http://dx.doi.org/10.1016/j.eswa.2011.06.017mailto:[email protected]://dx.doi.org/10.1016/j.eswa.2011.06.017


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Mourao, 2007), and marine design (Jang, Yang, Song, Yeun, & Do,

2002). However, it cannot be used with incomplete information,

since the expression of decision variables by crisp numbers would

be ill defined (Kahraman & Kulak, 2005). The experts evaluate the

importance of evaluation criteria or the performance of the provid-

ers withuncertain, imprecise, or subjective judgments (Liu& Wang,

2009). Hence, the subjectivity andvagueness in the assessment pro-

cess aredealt with fuzzy logic (Zadeh,1975). Multiple DMs areoften

preferred rather than a single DM to avoid any possible bias and to

minimize the partiality in the decision process (Herrera, Herrera-

Viedma, & Chiclana, 2001). Group decision-making is thus another

important concern in this study. In order to integrate various expe-

riences, opinions, ideas, and motivations of each DM, it is preferable

to convertthe linguistic estimation intofuzzy numbers, makingfuz-

zy logic essential in resolving the problems of group decision-mak-

ing(Lin& Wu, 2008). In consequence, a fuzzy axiomatic design (FAD)

based group decision-making approach is applied to evaluate the

alternatives according to the criteria considered and rank them in

an efficient way. The methodology includes two phases. In the first

phase, which is a pre-assessment phase, the alternatives are evalu-

ated by the group of DMs with respect to the main criteria, and

FAD is applied to assessthe alternatives.The first phaseaims to con-

sider as many available tools as possible and to eliminate the ones

thatcannot meet thebasic requirements.Also, a threshold is defined

in the pre-assessment phase in order to reduce the alternative set

thatundergoesthe detailedevaluation. In the second phase of selec-

tion, the remaining alternativesare evaluated more meticulously by

thesame group of DMswith respect to the sub-criteria, andsince the

structure of the problem is hierarchical, the hierarchical FAD meth-

odology (Kahraman & Cebi, 2009) is applied to determine thescores

of the alternatives. Both phases and the pre-evaluation process

where alternatives, criteria, and criteria weightsare determined, in-

clude the same DMsin order to assure consistencyof the judgments.

Finally, a case study evaluating thePDA barcode scannersis given to

demonstrate the potential of the methodology. Another MCDM

technique, namely fuzzy technique for order preference by similar-

ity to ideal solution (TOPSIS), is applied to compare the outcome ofthe second phase given that FAD and Fuzzy TOPSIS operate on the

geometrical principles. As the study includes incomplete informa-

tion due to subjective judgments, and FAD is preferred to the con-

ventional AD, Fuzzy TOPSIS is also preferred to the conventional

TOPSIS in order to assure coherence in a fuzzy environment.

This papers contribution lays in the two-phase pre-assessment

and selection system that uses integratedly Fuzzy MCDM tech-

niques with group decision. FAD emerges as an efficient fuzzy

MCDM methodology. On the other hand, Fuzzy Delphi and fuzzy

analytic hierarchy process (AHP) are commonly used Fuzzy MCDM

techniques and these techniques are employed in the first phase to

calculate criteria weights. However, these techniques are generally

used separately and for different purposes in various decision

problems. This paper integrates these techniques in one methodol-ogy, aggregating subjective judgments of the DMs with Fuzzy Del-

phi, calculating the criteria weights with Fuzzy AHP and finally,

assessing, eliminating and selecting the alternatives with FAD.

The proposed methodology benefits from the strength of each

technique in order to solve the multi-criteria problem in hand in

the most efficient way. Also, the selection of mobile logistics tools

seems to be a neglected subject in the logistics literature. This pa-

per applies the proposed methodology to the PDA Barcode Scanner

selection problem with a case study in Turkey.

The rest of the paper is organized as follows. Section 2 describes

the details of the proposed evaluation methodology and the tech-

niques employed in the study. Section 3 introduces the evaluation

criteria forthe mobile logistics tools.A case study is given in Section

4 withall detailedsteps included to validate the model and to exam-ine its effectiveness. Section 5 is dedicated to the comparison of the

FAD outcome with another Fuzzy MCDM technique and to the sen-

sitivity analysis. Section 6 concludes the paper with some remarks.

2. Methodology

MCDM has proven to be an effective methodology for solving a

large variety of multi-criteria evaluation and ranking problems

(Hwang & Yoon, 1981). MCDM techniques are applied in many dif-

ferent areas such as strategic decisions, economic evaluation, and

technological investment. These techniques are also employed in

planning, evaluating, and selecting information technologies (IT).

In evaluating the decision alternatives in new problem settings,

the assessment data for the criteria weights and for the perfor-

mance ratings of the alternatives on qualitative criteria are often

not available and have to be assessed subjectively by the decision

makers, the stakeholders or the experts (Yeh & Chang, 2009). The

subjective assessment process involves two types of judgment.

Comparative judgments are often used for identifying the impor-

tance of criteria whereas absolute judgments are generally used

to evaluate the alternatives. On the other hand, both comparative

and absolute judgments include imprecision and subjectivity as

it involves decision makers thoughts and bias. Fuzzy sets are used

in this paper in order to deal with the imprecision and subjectivity

involved in the evaluation process (Zadeh, 1975) and manipulates

uncertain criteria of the problem (Royes, Bastos, & Royes, 2003).

Fuzzy MDCM provides means to evaluate decision alternatives

using subjective judgments. In this paper DMs opinions are gath-

ered using linguistic terms and fuzzy scales are employed to trans-

late the linguistic opinions into triangular fuzzy numbers. On the

other hand, as the evaluation criteria become more intangible

and decisions become more complex for one DM to make, the

methodology utilizes group decision making where aggregation

operations play an important role (Musilek, Guanlao, & Barreiro,

2005). Fuzzy MCDM with group decision, where individual judg-

ments of the DMs are aggregated, is increasingly employed in lit-

erature. For example, Chen and Cheng (2005) apply fuzzy MCDMwith group decision to information systems personnel selection.

Wang and Parkan (2008) considers the fuzzy preference aggrega-

tion problem in group decision and they apply it to the broadband

internet service selection. Recently, Yeh and Chang (2009) have

developed a hierarchical weighting method to assess the weights

of a large number of evaluation criteria by pairwise comparisons.

Dagdeviren, Yavuz, and Klnc (2009) use the geometric mean of

the pairwise comparisons obtained from individual evaluations

for AHP in a weapon selection problem.

A two-phase elimination and selection methodology using

MCDM is proposed in this paper. Main steps of the proposed meth-

odology are recapitulated in Fig. 1. The methodology is basically

composed of three sections. During the pre-evaluation process,

DMs are gathered in order to establish the MCDM parameters;the selection criteria are determined and categorized to establish

a hierarchy. Afterwards, Fuzzy AHP methodology is employed in

order to calculate the main criteria and sub-criteria weights. Also,

the alternatives are identified.

The first phase of the methodology, the pre-assessment phase,

includes multiple DMs determining design and system ranges in

linguistic terms. These linguistic terms are then fuzzified and

aggregated in order to undergo the FAD methodology. Also, a

threshold is defined by the group of DMs. The pre-assessment

phase eliminates the alternatives in two ways:

First elimination occurs with the design range. Alternatives that

cannot meet the limits set by the design range have unlimited

information content, and therefore, are removed from the alter-natives set.

G. Bykzkan et al./ Expert Systems with Applications 39 (2012) 142153 143


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Second elimination occurs with the threshold. DMs determine a

threshold on the number of alternatives or information content

in order to perform a more detailed and thorough evaluation in

respect to the sub-criteria with fewer alternatives.

The second phase of the methodology is basically the same with

the first phase, except that it involves fewer alternatives and more

criteria, as the evaluation in the selection phase is performed with

the sub-criteria. Again, fuzzy MCDM with group decision is em-

ployed. DMs opinions are aggregated, and then, FAD is performed.

The alternatives are ranked in increasing order of information

content.

Finally, another fuzzy MCDM technique is employed on the

DMs evaluations in order to compare the outcome of the FAD tech-

nique and underline the advantages and the drawbacks of the pro-posed methodology.

2.1. Determining and categorizing the logistics tool criteria

The first step in the methodology is to determine the main cri-

teria and sub-criteria for logistics tool performance evaluation.

These criteria are collected through literature review and verified

by the industrial experts. The criteria are then categorized in a

hierarchical structure. In some research, the customer selects the

appropriate qualitative and quantitative criteria for his/her organi-

zation given that there is no common identification of factors guid-

ing the supplier selection process and that decision criteria vary in

relation with various characteristics of the buyer organization(Chamodrakas, Batis, & Martakos, 2010). In other researches, a

team of analysts who have a rich knowledge and expertise in logis-

tics activities different, including experts from the organization

such as sales, marketing, manufacturing, finance, and logistics,

determine all possible evaluation criteria prior to provider selec-

tion (Liu & Wang, 2009). In this study the criteria is provided

beforehand and the criteria is determined by the experts of the

organization.

2.2. Determining the criteria weights

Group decision is employed in determining criteria weights, as

well as in the next steps of this study. An aggregation method is

used to combine DMs judgments and opinions. These aggregated

judgments are then employed in fuzzy MCDM techniques. FuzzyDelphi method is employed as the aggregation method. Fuzzy

aggregation enables the handling of linguistic as well as ordinary

quantitative information to deal with the multi-criteria decision

making problem (Hung, Julian, Chien, & Jin, 2010). The technique

is adapted from Buyukozkan and Ruan (2008).

On the other hand, the technique used to calculate the criteria

weights is Fuzzy AHP, given that AHP is particularly useful for eval-

uating complex multi-attribute alternatives involving subjective

criteria (Kwong & Bai, 2003). AHP solves complex decision prob-

lems that may have correlations among decision criteria based

on three principles: decomposition, comparative judgments and

synthesis of priorities (Chamodrakas et al., 2010). As independence

axiom of AD guarantees the independence of the FRs, AHP is a suit-

able technique as the independence of evaluation criteria is alsoassured.

Fig. 1. Main steps of the proposed methodology.

144 G. Bykzkan et al./ Expert Systems with Applications 39 (2012) 142153


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AHP (Saaty, 1980), which is a widely used MCDM method, offers

the opportunity to tackle the complexity of the decision problem

by means of a hierarchy of the decision layers. The model enables

DMs to divide a decision into smaller parts, starting from the level

of goal formulation into criteria, down to the level of the alterna-

tive control actions. Yet for the subjective judgments, a theory is

needed to measure the ambiguity of these concepts. Because of

the vagueness and uncertainty on judgments of the DMs, the crisp

pairwise comparison in the conventional AHP seems to be insuffi-

cient and imprecise to capture the correct judgments of DMs. Fuz-

zy logic (Zadeh, 1975) can be introduced in the pairwise

comparison of the AHP to make up for this deficiency in the con-

ventional AHP, referred to as Fuzzy AHP. Fuzzy AHP is one of the

most commonly used MCDM methods and it takes the vagueness

and uncertainty on judgments of DMs into consideration (Ayag &

Ozdemir, 2007). Therefore, fuzzy extension of the AHP methodol-

ogy is employed in this study to calculate criteria weights.

2.3. Determining the alternatives

Determining the alternatives involves merely the identification

of the basic requirements for the tool in question. A genericrequirement set is defined in order to describe the tool with all

the main characteristics. Larger the requirement set gets, smaller

becomes the alternative set. All the available tools that have the

defined characteristics are included in the alternative set.

2.4. Pre-assessment: fuzzy axiomatic design with group decision

making

In the pre-assessment phase, PDA alternatives and FRs are eval-

uated by the group of DMs using linguistic terms. The opinions are

aggregated by the Fuzzy Delphi method and the evaluation is per-

formed by FAD considering only the main criteria, because the pre-

assessment phase involves a preliminary appraisal of alternatives.

AD, a systematic method offering a scientific base for design,was introduced by Suh (1990) and its application areas include

software design, quality system design, general system design,

manufacturing system design, e-commerce strategies, ergonomics,

engineering systems, and office cell design. AD is based on two axi-

oms. Independence axiom states that the independence of FRs

should be maintained and information axiom states that among

the designs that satisfy the FRs, the design with the minimum

information content is the best design. On the other hand, the

information content, on which MCDM technique is based, repre-

sents a function of probability of satisfying a FR. Therefore, the de-

sign with the highest probability to meet these requirements is the

best design. Information content Ii of a design with a probability of

success pi for a given FRi is defined as follows:

Ii log21pi

:

According to Suh (2001), logarithm is employed to calculate

information contents for obtaining additivity. The probability of

success is given by the design range (the requirements for the

design) and the system range (the system capacity). Fig. 2 illus-

trates the design and system ranges as well as the common area.

The intersection of the ranges offers the feasible solution.

Therefore, the probability of success can be expressed as:

pi

Zul

pFRidFRi;

where l and u represent the lower and upper limits of the design

range and where p represents the probability density function ofthe system for a given FRi.

The probability of success pi is equal to the common area Ac.

Consequently, the information content can be expressed as

follows:

Ii log21

Ac

:

Also, if the probability distribution function is uniform, the

probability of success becomes:

pi common range

system range:

Therefore, the information content can also be written as:

Ii log2system range

common range

:

However, the conventional information content approach can-

not be used with incomplete information, because under incom-

plete information, the expression of the system and design

ranges by crisp numbers would be ill defined (Kahraman & Kulak,2005). For this reason, the subjectivity and vagueness in the assess-

ment process are dealt with the fuzzy logic (Zadeh, 1975). The

information axiom of AD is utilized as a fuzzy MCDM technique

by Kulak and Kahraman (2005). FAD applications for fuzzy MCDM

can be summarized as shown in Table 1. Even though FAD litera-

ture on fuzzy MCDM shows various applications, no previous study

exists on the mobile logistics tool evaluation and there are only a

few applications of FAD in supply chain domain.

The FAD methodology is based on the conventional AD. How-

ever, the crisp ranges are replaced by fuzzy numbers that represent

linguistic terms as seen in Fig. 3.

In this study, the triangular fuzzy numbers (TFNs) are em-

ployed. Information content is calculated as in a non-fuzzy envi-

ronment. Intersection of TFNs representing the design andsystem ranges presents the common area (Kulak & Kahraman,

2005). Information content in a fuzzy environment is calculated

as follows:

Ii 1; no intersection;log2area of system range

common area

; otherwise;

n

In this study, the weighted information content calculation of

the hierarchical FAD is adapted from Kahraman and Cebi (2009).

This model requires the determination of the weights of the crite-

ria and sub-criteria. Total weighted information content for the

first level criteria is calculated as follows:

IXni1

wiIi;

where n is the number of the first level criteria andPn

i1wi 1.

Fig. 2. System-design ranges and common area in crisp environment.



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Likewise, the total weighted information content for the second

level criteria (sub-criteria for criterion i) is calculated as follows:

Ii Xmj1

wijIij:

where m is the number of sub-criteria for criterion i andPm

j1wij 1; for i 1; . . . ; n.

The lower level information contents are calculated similarly.

According to the information axiom, the alternatives are ranked

with an increasing order of information content. Alternatives that

cannot meet the FRs, which means the information content is infi-

nite, are eliminated.

2.4.1. Determining the design range for the main criteria

The design range for the main criteria determines the accept-

able interval for the DMs. Preferably, a lower limit is set for the

benefit criteria and an upper limit is set for the cost criteria. Nev-

ertheless, an interval may also be set for all criteria. With FAD, lin-

guistic terms are employed to determine the design range.

2.4.2. Determining the system range for the alternatives

Determination of the system range includes the evaluation of

each alternative by the DMs. In FAD, these evaluations are ex-

pressed as linguistic terms. Nevertheless, for the tangible criteria,the evaluations can be expressed as crisp numbers as well.

2.4.3. Determining a threshold for the elimination phase

A threshold is determined by the group of DMs for the pre-

assessment phase. The threshold may be defined using either oneof the following two ways:

A limit can be set on the number N of alternatives to undergo

the detailed evaluation. This limit is defined by consensus and

once the evaluation is made, the first N alternatives with mini-

mum information content are selected to undergo the selection

phase.

A limit on the information content can be set for the alterna-

tives to undergo the detailed evaluation. The information con-

tent is calculated as a function of the intersection of the

design range and the system range. Therefore, the gap between

the upper vertices of the triangular fuzzy numbers can be lim-

ited by defining a threshold on the information content, i.e.,

the maximum gap between the desired performance and theactual performance of the alternative can be defined.

The threshold should be defined according to the level of tech-

nology and the number of alternatives available. For example, if the

investment in question seizes a high-level cutting-edge technol-

ogy, the alternatives would be relatively fewer and the threshold

cannot be highly severe in the elimination phase. However, if the

technology to acquire is ordinary and well-spread, then the numer-

ous alternatives available in the market can be eliminated by a

strict threshold. Hence, a threshold on the number of alternatives

is suggested when a common technology is in question, whereas

a threshold on the information content is preferable for the high-

level technology.

2.4.4. Evaluation of the alternatives with the main criteria and

elimination of the unsuitable ones

Once the information contents are calculated, alternatives are

ranked in decreasing order of the information content. First, the

alternatives with infinite information content are eliminated auto-

matically, by the methodology itself. Then, the threshold is em-

ployed to eliminate more unsuitable alternatives according to the

opinion of the group of DMs.

2.5. Selection: hierarchical fuzzy axiomatic design with group decision

making

In this phase, the remaining alternatives are evaluated consid-ering the sub-criteria, therefore hierarchical FAD is applied and

Table 1

FAD literature.

Authors Application area

Kulak and Kahraman (2005) Selection among transportation companies

Kulak (2005) Choice of material handling equipments

Kulak, Durmusoglu, and Kahraman (2005) Multi-attribute equipment selection

Eraslan, Akay, and Kurt (2006) Ranking of intercity bus passenger seats

Cebi and Celik (2007) Measuring customer satisfaction at ports

Ozel and Ozyoruk (2007) Supplier decisionCebi and Celik (2008) Ship machinery installation

Cebi, Celik, Er, and Kahraman (2008) Ship design project approval

Ycel and Aktas (2008) Evaluation for ergonomic design of electronic consumer products

Celik and Er (2009) Model selection paradigm

Celik (2009) Integrated environmental management system (IEMS) design

Celik, Kahraman, Cebi, and Er (2009) Shipyards docking performance evaluation model

Celik, Cebi, Kahraman, and Er (2009) Strategy making towards container port development

Cevikcan, Cebi, and Kaya (2009) Comparison of fuzzy VIKOR and fuzzy axiomatic design versus to fuzzy TOPSIS and application to candidate assessment

Cicek and Celik (2009) Selection of porous materials in marine system design

Kahraman and Cebi (2009) Teaching assistant selection problem

Cebi and Kahraman (2010a) Discussions on the adaptation of the current AD principles into fuzzy sets theory

Cebi and Kahraman (2010b) Design of indicator panel for passenger cars based on the AD principles under fuzzy environment

Fig. 3. System-design ranges and common area in a fuzzy environment.



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the functional requirements are defined as functions of sub-crite-

ria. The opinions are again aggregated by Fuzzy Delphi.

2.5.1. Determining the design range for the sub-criteria

Similar to the determination of the design range for the main

criteria, the DMs are involved in defining an interval, a lower limit

or an upper limit for each sub-criterion determined for each

requirement.

2.5.2. Determining the system range for the remaining alternatives

Similar to the determination of the system range for the alter-

natives, the DMs evaluate the alternatives with linguistic terms

according to the sub-criteria. The linguistic terms are then trans-

lated into fuzzy numbers.

2.5.3. Re-evaluation of the alternatives with sub-criteria

Information contents for each alternative according to each sub-

criterion are calculated and hierarchical FAD is applied to deter-

mine the weighted information contents.

2.5.4. Ranking of the alternatives

The last step of FAD methodology is to rank the alternatives indecreasing order of information content and to select the best tool.

Information contents display how well the alternative is respond-

ing to all the requirements. The alternative with the minimum

information content is therefore the best alternative.

3. Evaluation criteria for mobile logistics tools

A typical supply chain is a complex mixture of actors who need

coordination, collaboration, and information exchanges among

them in order to increase productivity and efficiency (Martinez-

Sala, Egea-Lopez, Garcia-Sanchez, & Garcia-Haro, 2009). The use

of technology to drive business innovation, enhance customer ser-

vice, and utilize available resources has become a strategic neces-

sity in this respect (Chen, Yen, & Chen, 2009). Mobile logistics toolsprovide the flexibility and the functionality required in supply

chains. Mobile stands for positioning a mobile device as any ter-

minal that can be used on the move like a PDA (Wamba, Lefebvre,

Bendavid, & Lefebvre, 2008).

Factors affecting successful technology adoption include tech-

nological factors, market-related factors, socio-economic factors,

regulatory factors, and factors related to internal organization

(Jeong, Yoo, & Heo, 2009). On the other hand, according to innova-

tion diffusion theory, five significant innovation characteristics

(relative advantage, compatibility, complexity, trial ability, and

observables) are used to explain the user adoption and decision

making process (Wu & Wang, 2005). Jeong et al. (2009) state that,

as the market becomes more competitive and the customers

expectations regarding services or products increase, the success-ful adoption of a new technology becomes more challenging. In to-

days market, there exist hundreds of IT applications for

maintenance management purposes, and choosing the most

appropriate one is a challenge unless the requirements are defined

(Kans, 2008).

In consequence, in order to define the expectations from mobile

logistics tools, five main criteria and 14 sub-criteria are determined

based on the literature review and the DMs suggestions. These cri-

teria are displayed in Table 2. Product characteristics, functionality,

cost, after sales services, and brand reliability are identified as the

main criteria.

Product characteristics involve physical characteristics includ-

ing size, screen size, weight, design, etc., technical characteristics

such as memory, operating system, etc., and safety standardsincluding certification and warranty. On the other hand,

functionality represents the intangible product characteristics that

cannot be measured but perceived, such as ease of use and flexibil-

ity. Adaptability refers to the tool capability to adapt to various

systems and functionality implicates the usefulness of the func-

tions provided. According to the behavioral decision theory, the

cost-benefit pattern is significant to both perceived usefulness

and ease of use (Wu & Wang, 2005). However this study considers

functionality and cost as completely independent categories.

For PDA barcode scanners, the operating cost and product price

are very low compared to major investments. However, as the tool

in question represents a corporate investment, the cost is never-

theless considered as an important factor. According to Wu and

Wang (2005), consumers must deal with non-negligible costs in

switching between different brands of products or relative services

in various markets.

After Sales Service and Brand Reliability are the two remaining

categories, comparable yet dissimilar. In general, the vendor and

the brand are two separate companies and therefore, two different

considerations have to be made in order to evaluate both the man-

ufacturer and the vendor. After Sales Services category includes

technical support of the vendor, vendor reputation and vendor

capacity to provide service. Provision of after-sales services is

one way that firms can differentiate themselves from competitors

and these services are important in terms of expectations, profit-

ability, and customer loyalty (Zackariasson & Wilson, 2002). On

the other hand, Brand Reliability includes market share of the man-

ufacturer and brand reputation, which is different from vendor

reputation as it represents the reputation engendered by the man-

ufacturing firm.

4. Case study

Logistics and supply chain is a rapidly growing industry in Tur-

key. Several enterprises are integrating automation systems into

their activities. The integration of wireless technologies to support

business processes within a supply chain could have a significantimpact on overall business operations, leading to competitive

advantages in terms of cost reduction, supply chain responsive-

ness, and performance of supply chain functions, and therefore im-

pact the strategic management of all firms involved in the supply

chain (Wamba et al., 2008). Moreover, investments in IT have a po-

sitive correlation to company profitability and competitiveness,

thus indicating that IT is of strategic importance and poor invest-

ment decisions could result in failure, leading to costly conse-

quences (Kans, 2008). This paper considers a PDA Barcode

Scanner selection problem for a logistics company ABC to be em-

ployed in their regional warehouses and headquarters.

Founded in 1990 as a transportation company, ABC is a leading

logistics firm with 26 branches and 1500 corporate customers.

They offer warehouse and distribution management solutions aswell as freight solutions. ABC aims to improve its logistics perfor-

mance by using mobile technologies. The company thus demands

a purchase of 24 PDAs, which results in a large amount that can

be considered as a strategic investment. Moreover, the company

intends to increase eventually the number of its warehouses, con-

sequently more PDAs are to be purchased potentially.

The problem presents similarities to the supplier selection

problem in supply chain system, which is in fact a group decision

making combination of several and different criteria with different

forms of uncertainty, and hence, requires MCDM methods for an

effective problem-solving (Sanayei, Mousavi, & Yazdankhah,

2010). As mentioned in Fig. 1 of Section 2, two-phased pre-assess-

ment and selection methodology is proposed to effectively decide

on the best PDA Barcode Scanner alternative for the firm. First,pairwise comparisons are obtained from three DMs (IT manager,



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warehouse manager and logistics manager) having importance of

0.4, 0.3, 0.3, respectively. The judgments are aggregated through

Fuzzy Delphi, and the criteria weights are determined. In the first

phase of the study, the group of DM gathers information about

all available PDA Barcode Scanners in the Turkish market and eval-

uates these alternatives with respect to five first level criteria as

well as the FRs defined by the group. The alternatives that cannot

meet the basic requirements are eliminated in this phase. Also, the

threshold defined by the DMs is taken into account at this stage inorder to eliminate the unsuitable alternatives. The second phase is

performed with the sub-criteria to apply a more detailed evalua-

tion. In this phase, the alternatives are ranked and the most appro-

priate tool is selected. Selection phase involves the same group of

DMs, which determines the FRs on the sub-criteria and evaluates

the alternatives. The alternatives are ranked in an increasing order

of the information content. Also, Fuzzy TOPSIS is applied to com-

pare the outcome of FAD with another MCDM technique.

ABC Companys mobile logistics tool evaluation process first

starts by the identification of PDA Barcode Scanner alternatives.

The PDA alternatives are selected from popular items of Turkish

information system and logistics tool providers. Products are

determined from different brands and they are all available in Tur-

key. Common features are sought for the selection of PDAs to eval-uate the same product family. Table 3 displays the product name

and the vendors website.

Once the DMs decide on the PDA Barcode Scanner alternatives,

a primary evaluation is made with the first-level criteria and re-

lated FRs for the pre-assessment phase.

The linguistic terms employed in evaluating the PDAs need to

be translated into fuzzy numbers in order to quantify the judg-

ments. In this study, a 11-level fuzzy scale is used to assess the

alternatives and determine the criteria weights, and another 11-le-

vel fuzzy scale is used to assess the FRs, as a bare minimum is re-

quired to be achieved for FRs. Table 4 and Fig. 4 describe the

linguistic terms, their abbreviations and fuzzy membership

functions.

The DMs pairwise comparisons on main criteria and sub-crite-ria are gathered in linguistic terms and then fuzzified, and Fuzzy

Delphi is employed to aggregate the pairwise comparisons. Criteria

weights determined by Fuzzy AHP are displayed in Fig. 5.

Table 5 displays Expert 1s judgment on alternatives as well as

on the FRs. The scales described in Table 4 are used for the evalu-

ations. The DMs judgments on FRs and alternatives are gathered

and translated into fuzzy numbers. Fuzzy Delphi is then applied

to aggregate the evaluations. Table 6 demonstrates the aggregated

opinions of DMs based on the main (first level) criteria. The aggre-

gated evaluations undergo FAD methodology in order to calculatethe information contents presented in Table 7. The information

contents and the main criteria weights are used to compute the

weighted information contents displayed in Table 8.

As can be seen in Table 8, FAD methodology itself eliminates

two alternatives, A1 and A6, given that these alternatives cannot

meet the design range for criteria C2. Hence, the bare minimum de-

fined on FR cannot be satisfied and these alternatives are elimi-

nated. On the other hand, the pre-assessment phase aims to

eliminate as many unfit alternatives as possible in order to evalu-

ate and select from a limited set of suitable candidates. Therefore,

DMs set a threshold on information content. Alternatives that re-

sult in weighted information content WICTOTP 1 are eliminated

in order to have the most suitable PDAs to analyze in detail and se-

lect from.Four alternatives (A3, A2, A7, and A5) are proven to be satisfac-

tory enough to undergo the selection phase. Alternatives A4, A8,

and A9 have insufficient information contents and hence, are elim-

inated. On the other hand, alternatives A1 and A6 are eliminated

from solution set by the FAD methodology itself.

The selection phase represents a more detailed evaluation with

14 sub-criteria. The same group of DM evaluates the remaining

four alternatives with the same 11-level scale, as well as the FRs

in respect to each sub-criterion. Table 9 displays Expert 1s opin-

ions on the performance of the alternatives according to the sub-

criteria as well as his judgment on the design ranges.

As in the pre-assessment phase, the DMs judgments are aggre-

gated as seen in Table 10. These aggregated judgments are em-

ployed to compute the information contents for each criterion,displayed in Table 11. Then the hierarchical FAD is applied to cal-

culate the weighted information contents in order to obtain the to-

tal weighted information contents shown in Table 12. The selection

phase does not seek to eliminate the alternatives, unless the meth-

odology itself eliminates them based on the minimum require-

ments. Therefore, once the total weighted information contents

are calculated, the alternatives are ranked in increasing order of

information content. The ranking of the four remaining alterna-

tives are shown in Table 13.

5. Comparative validation

This section of the paper presents the comparison of the FAD

outcome with another MCDM technique, namely Fuzzy TOPSIS,as well as a sensitivity analysis conducted on variations of FRs.

Table 2

Mobile logistic tool evaluation criteria.

Criteria Definition Sources

C1 Product

characteristics

Physical (C11), technical (C12), safety

standards (C13)

Tam and Tummala (2001), Lee et al. (2007), Isklar and Bykzkan (2007), Lin, Wang, Chen, and

Chang (2008)

C2 Functionality Ease of use (C21), function diversity (C22),

adaptability (C23), flexibility (C24)

Lehmann and Oshaughnessy (1982), Isklar and Bykzkan (2007), Lee et al. (2007), Lin et al.

(2008), Chen et al. (2009), Jeong et al. (2009), Qi, Li, Li, and Shu (2009), Lee (2009), Sanayei et al.

(2010)

C3 Cost Operating cost (C31), product price (C32) Lehmann and Oshaughnessy (1982), Tam and Tummala (2001), Bei, Wang, and Hu (2006), Isklarand Bykzkan (2007), Liu and Wang (2009), Lee (2009), Sanayei et al. (2010)

C4 After sales

services

Technical support (C41), vendor reputation

(C42), capacity (C43)

Tam and Tummala (2001), Zackariasson and Wilson (2002), Hoffer (2005), Bei et al. (2006), Liu

and Wang (2009), Lee (2009)

C5 Brand

reliability

Market share (C51), brand reputation (C52) Lehmann and Oshaughnessy (1982), Tam and Tummala (2001), Hoffer (2005), Bei et al. (2006),

Lee et al. (2007), Liu and Wang (2009)

Table 3

PDA barcode scanner alternatives.

Product name Vendor website

A1 Zebex PDL 20-16 www.bilkur.com.tr

A2 Opticon H19 www.mlshop.biz

A3 Mobile Compia M3 www.baykod.com

A4 Pidion BIP-5000 www.sempa.com.tr

A5 Symbol MC50 www.hepsibilgisayar.com

A6 SCC SC600 www.sistembilisim.com

A7 Casio IT600 www.mobit.com.tr

A8 Bitatek IT7000 www.teknoshop.com.tr

A9 CipherLab 9570CE www.mobilish.com

http://www.bilkur.com.tr/http://www.mlshop.biz/http://www.baykod.com/http://www.sempa.com.tr/http://www.hepsibilgisayar.com/http://www.sistembilisim.com/http://www.mobit.com.tr/http://www.teknoshop.com.tr/http://www.mobilish.com/http://www.mobilish.com/http://www.teknoshop.com.tr/http://www.mobit.com.tr/http://www.sistembilisim.com/http://www.hepsibilgisayar.com/http://www.sempa.com.tr/http://www.baykod.com/http://www.mlshop.biz/http://www.bilkur.com.tr/


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5.1. Comparison of the obtained results with Fuzzy TOPSIS

methodology

In this study, another MCDM method is applied in order to com-

pare the performance of the presented methodology. TOPSIS was

initially proposed by Chen and Hwang (1992) and the basic princi-

ple is that the optimal solution should have the shortest distance

from the positive ideal solution and the farthest from the negative

ideal solution. In order to assure the consistency of the study, TOP-

SIS is adapted to the fuzzy environment. Fuzzy TOPSIS is chosen as

the additional technique due to its simplicity and wide applica-tions (Qureshi, Kumar, & Kumar, 2008; Shih, 2008) as well as due

Table 4

Linguistic terms and membership functions for system and design ranges.

Term Abbreviations Membership function Term Abbreviations Membership function

None N (0.00, 0.00, 1) At least none LN (0.00, 1.00, 1.00)

Very low VL (0.00, 0.10, 0.20) At least very low LVL (0.05, 1.00, 1.00)

Low L (0.10, 0.20, 0.30) At least low LL (0.10, 1.00, 1.00)

Fairly low FL (0.20, 0.30, 0.40) At least fairly low LFL (0.20, 1.00, 1.00)

More or less low ML (0.30, 0.40, 0.50) At least more or less low LML (0.30, 1.00, 1.00)

Medium M (0.40, 0.50, 0.60) At least medium LM (0.40, 1.00, 1.00)More or less good MG (0.50, 0.60, 0.70) At least more or less good LMG (0.50, 1.00, 1.00)

Fairly good FG (0.60, 0.70, 0.80) At least fairly good LFG (0.60, 1.00, 1.00)

Good G (0.70, 0.80, 0.90) At least good LG (0.70, 1.00, 1.00)

Very good VG (0.80, 0.90, 1.00) At least very good LVG (0.80, 1.00, 1.00)

Excellent E (0.90, 1.00, 1.00) At least excellent LE (0.90, 1.00, 1.00)

Fig. 4. Membership functions for system and design range.

Fig. 5. Evaluation criteria weights.

Table 5

Expert 1 first phase evaluation on alternatives and design range.

FR A1 A2 A3 A4 A5 A6 A7 A8 A9

C1 LL FL M ML MG FL VL FL FL M

C2 LM VL FG FG ML M VL M MG FL

C3 LVL VG M G MG FL G MG M VL

C4 LL ML G M MG FG M G ML ML

C5 LL VL FG MG ML G FL VG VL ML



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to its compatibility with group decision (Wu, Lin, Kung, & Lin,

2007). Moreover, Fuzzy TOPSIS is based on geometrical principles,

similar to FAD which also operates on a geometrical level. The

technique is adapted from Chen (2000).

Tables 14 and 15 exhibit the outcome of the experts opinions

with the TOPSIS methodology, as well as the outcome of the FAD

methodology.

The comparison of the outcomes clearly suggests that the two

methodologies differ considerably in evaluating the identified

alternatives. Although the FAD and Fuzzy TOPSIS are similar inevaluating the alternatives with a reference to ideal point (FRs

for FAD, FPIRP and FNIRP for Fuzzy TOPSIS), the description of

the ideal reference point differs for the two methodologies. In TOP-

SIS the chosen alternative shouldhave the shortest distance from

the ideal solution and the farthest distance from the

negative-ideal (Sanayei et al., 2010). A requirement set is deter-

mined by the DMs for FAD, whereas FPIRP and FNIRP are generic;

which explains the variations in the outcomes. The results ob-

tained in Fuzzy TOPSIS represent a generic ranking, where the

requirements for all criteria are the same; whereas the ranking of

FAD methodology presents the performance of the alternatives

according to the defined requirements. The criteria weights are

the same for both methodologies; however the requirements de-

fined on criteria change. On the other hand, as seen in Table 14,

when the ranking is divided in three groups, it is observed that

each group contains the same alternatives with FAD and Fuzzy

TOPSIS, which clearly indicates that alternatives in each group em-

brace the same level of performance.

5.2. Sensitivity analysis

Considering the difference in the outcome of the two methodol-

ogies, a sensitivity analysis is conducted in order to identify the

cause of this difference. Given that FR set on the C2 (functionality)

is the eliminating requirement, the sensitivity analysis is con-

ducted on the variations of the FR2. Table 16 displays the outcome

resulted from different values of FR2.

As the minimum requirement on FR2 decreases, the weighted

information contents ofA1 and A6 decreases as well. It can be con-

cluded that FAD methodology results in the same outcome as Fuz-

zy TOPSIS if the requirement on functionality lessens. With the

design range on FR2 being (0.25, 1, 1), the FAD ranking is identical

to Fuzzy TOPSIS except for the last three alternatives.

As the FR set on C2 decreases, it approaches to fuzzy negative

ideal reference point (FNIRP), which is (0; 0; 0). Therefore, the sim-

ilarity between FAD and Fuzzy TOPSIS outcome increases. On the

other hand, once the alternatives that are eliminated with the pre-

vious FRs are capable to meet the requirements for C2 with the last

FR, the ranking of the alternatives changes considerably and ap-

proach the Fuzzy TOPSIS ranking. As seen in Table 16, as long as

the criteria weights remain the same, FAD methodology results

are similar to the outcome of Fuzzy TOPSIS technique as long asthe FRs are close to FNIRP. This can be easily explained by the fact

that FAD methodology considers the requirements as well as the

criteria weights and evaluates the alternatives according to these

requirements. On the other hand, Fuzzy TOPSIS evaluates the alter-

natives according to the ideal reference points, and therefore, the

Table 6

Aggregation on expert evaluations.

C1 C2 C3 C4 C5

FR 0.07 1.00 1.00 0.37 1.00 1.00 0.11 1.00 1.00 0.16 1.00 1.00 0.11 1.00 1.00

A1 0.26 0.36 0.46 0.06 0.16 0.26 0.80 0.90 1.00 0.30 0.40 0.50 0.09 0.19 0.29

A2 0.40 0.50 0.60 0.54 0.64 0.74 0.34 0.44 0.54 0.67 0.77 0.87 0.60 0.70 0.80

A3 0.39 0.49 0.59 0.60 0.70 0.80 0.70 0.80 0.90 0.34 0.44 0.54 0.47 0.57 0.67

A4 0.47 0.57 0.67 0.27 0.37 0.47 0.47 0.57 0.67 0.53 0.63 0.73 0.36 0.46 0.56

A5 0.20 0.30 0.40 0.40 0.50 0.60 0.23 0.33 0.43 0.63 0.73 0.83 0.70 0.80 0.90A6 0.00 0.10 0.20 0.06 0.16 0.26 0.67 0.77 0.87 0.40 0.50 0.60 0.26 0.36 0.46

A7 0.20 0.30 0.40 0.40 0.50 0.60 0.50 0.60 0.70 0.73 0.83 0.93 0.86 0.96 1.00

A8 0.20 0.30 0.40 0.47 0.57 0.67 0.40 0.50 0.60 0.36 0.46 0.56 0.03 0.13 0.23

A9 0.40 0.50 0.60 0.26 0.36 0.46 0.06 0.16 0.26 0.27 0.37 0.47 0.30 0.40 0.50

Table 7

Information contents and total information contents for pre-assessment phase.

IC1 IC2 IC3 IC4 IC5 ITOT

A1 0.94 1 0.02 1.05 2.69 1

A2 0.50 0.59 0.74 0.11 0.18 2.12

A3 0.52 0.38 0.08 0.87 0.39 2.24

A4 0.35 3.87 0.39 0.32 0.68 5.61

A5 1.23 1.50 1.23 0.16 0.08 4.19

A6 3.61 1 0.10 0.64 1.09 1A7 1.23 1.50 0.33 0.06 0.00 3.12

A8 1.23 0.94 0.56 0.78 3.90 7.40

A9 0.50 4.17 3.14 1.22 0.90 9.93

Table 8

Weighted information contents and total weighted information contents for pre-

assessment phase.

WIC1 WIC2 WIC3 WIC4 WIC5 WITOT Ranking

A1 0.24 1 0.00 0.18 0.35 1

A2 0.13 0.15 0.15 0.02 0.02 0.46 2

A3 0.13 0.10 0.02 0.15 0.05 0.44 1

A4 0.09 0.97 0.08 0.05 0.09 1.28 5

A5 0.31 0.38 0.25 0.03 0.01 0.97 4

A6 0.90 1 0.02 0.11 0.14 1

A7 0.31 0.38 0.07 0.01 0.00 0.76 3

A8 0.31 0.23 0.11 0.13 0.51 1.29 6

A9 0.13 1.04 0.63 0.21 0.12 2.12 7

Table 9

Expert 1 first phase evaluation on alternatives and design range.

C11 C12 C13 C21 C22 C23 C24 C31 C32 C41 C42 C43 C51 C52

FR LL LM LMG LM LL LFL LMG LML LML LMG LML LML LM LML

A2 FG FL G M G MG FG MG ML MG FG G FG M

A3 G ML FG G FG M M FL G M MG FG MG MG

A5 G MG FG FG MG M MG ML ML M G FG FG MG

A7 FG M FG ML G MG MG G MG M MG FG G M



10/12

alternatives are ranked in reference to their performance againsteach other. Therefore, FAD methodology is a more appropriate

technique as a Fuzzy MCDM method for cases where defining the

requirements is necessary. Fuzzy TOPSIS can be employed when

merely a comparison of the alternatives is required.

6. Conclusions

This study proposes a fuzzy group decision-making framework

for the mobile logistics tool evaluation problem, as well as a case

study for PDA barcode scanner selection. Applications of FAD in re-

cent studies proved this technique to be an appropriate supporttool in decision-making. For this reason, both phases of this study

Table 10

Aggregation on expert evaluations.

C11 C12 C13 C21 C22

FR 0.09 1.00 1.00 0.37 1.00 1.00 0.41 1.00 1.00 0.49 1.00 1.00 0.19 1.00 1.00

A2 0.54 0.64 0.74 0.32 0.42 0.52 0.70 0.80 0.90 0.34 0.44 0.54 0.61 0.71 0.81

A3 0.58 0.68 0.78 0.30 0.40 0.50 0.57 0.67 0.77 0.61 0.71 0.81 0.54 0.64 0.74

A5 0.61 0.71 0.81 0.44 0.54 0.64 0.57 0.67 0.77 0.57 0.67 0.77 0.41 0.51 0.61

A7 0.51 0.61 0.71 0.28 0.38 0.48 0.54 0.64 0.74 0.36 0.46 0.56 0.61 0.71 0.81

C23 C24 C31 C32 C41

FR 0.29 1.00 1.00 0.5 1.00 1.00 0.45 1.00 1.00 0.27 1.00 1.00 0.41 1.00 1.00

A2 0.44 0.54 0.64 0.57 0.67 0.77 0.44 0.54 0.64 0.33 0.43 0.53 0.47 0.57 0.67

A3 0.37 0.47 0.57 0.37 0.47 0.57 0.32 0.42 0.52 0.70 0.80 0.90 0.37 0.47 0.57

A5 0.37 0.47 0.57 0.41 0.51 0.61 0.27 0.37 0.47 0.30 0.40 0.50 0.52 0.62 0.72

A7 0.38 0.48 0.58 0.41 0.51 0.61 0.64 0.74 0.84 0.50 0.60 0.70 0.46 0.56 0.66

C42 C43 C51 C52

FR 0.27 1.00 1.00 0.39 1.00 1.00 0.49 1.00 1.00 0.3 1.00 1.00

A2 0.57 0.67 0.77 0.64 0.74 0.84 0.66 0.76 0.86 0.49 0.59 0.69

A3 0.50 0.60 0.70 0.54 0.64 0.74 0.5 0.60 0.70 0.53 0.63 0.73

A5 0.61 0.71 0.81 0.57 0.67 0.77 0.63 0.73 0.83 0.62 0.72 0.82

A7 0.50 0.60 0.70 0.60 0.70 0.80 0.58 0.68 0.78 0.55 0.65 0.75

Table 11

Information contents for selection phase.

IC11 IC12 IC13 IC21 IC22 IC23 IC24

A2 0.25 1.93 0.18 5.61 0.20 0.81 0.25

A3 0.19 2.35 0.56 0.59 0.32 1.21 0.19

A5 0.16 0.43 0.56 0.83 0.67 1.21 0.16

A7 0.29 2.83 0.70 4.64 0.20 1.14 0.29

IC31 IC32 IC41 IC42 IC43 IC51 IC31

A2 1.85 1.40 1.14 0.34 0.30 0.38 1.85

A3 4.73 0.12 2.43 0.53 0.64 1.47 4.73

A5 8.34 1.68 0.80 0.25 0.52 0.50 8.34

A7 0.38 0.53 1.23 0.53 0.41 0.76 0.38

Table 12

Weighted information contents for sub-criteria.

WIC11 WIC12 WIC13 WIC21 WIC22 WIC23 WIC24

A2 0.05 0.39 0.04 1.40 0.05 0.20 0.05

A3 0.04 0.47 0.11 0.15 0.08 0.30 0.04

A5 0.03 0.09 0.11 0.21 0.17 0.30 0.03

A7 0.06 0.57 0.14 1.16 0.05 0.29 0.06

WIC31 WIC32 WIC41 WIC42 WIC43 WIC51 WIC31

A2 0.76 0.57 0.63 0.19 0.16 0.19 0.76

A3 1.94 0.05 1.34 0.29 0.35 0.74 1.94

A5 3.42 0.69 0.44 0.14 0.28 0.25 3.42

A7 0.16 0.22 0.68 0.29 0.23 0.38 0.16

Table 13

Weighted information contents and total weighted information contents for selection

phase.

WIC1 WIC2 WIC3 WIC4 WIC5 WITOT Ranking

A2 0.12 0.47 0.27 0.17 0.07 1.08 1

A3 0.16 0.42 0.40 0.34 0.13 1.44 3

A5 0.06 0.38 0.82 0.15 0.05 1.45 4

A7 0.19 0.58 0.07 0.20 0.08 1.13 2

Table 14

Performance indices for pre-assessment phase.

Alternatives IC Alternatives PI

A3 0.44 A3 0.14

A2 0.46 A2 0.14

A7 0.76 A7 0.13

A5 0.97 A4 0.12

A4 1.28 A5 0.12

A8 1.29 A8 0.10

A9 2.12 A1 0.09

A1 1 A9 0.09

A6 1 A6 0.08

Table 15

Performance indices for selection phase.

Alternatives IC Alternatives PI

A2 1.08 A7 0.05

A7 1.13 A2 0.05

A3 1.44 A5 0.05

A5 1.45 A3 0.05

Table 16

Sensitivity analysis on FR2.

Alternatives WICTOT WICTOT WICTOT WICTOT

FR2 (0.37, 1, 1) (0.35, 1 , 1 ) (0.3, 1, 1 ) (0.25, 1, 1 )

A1 1 1 1 3.45

A2 0.46 0.45 0.43 0.41A3 0.44 0.43 0.42 0.41

A4 1.28 1.15 0.93 0.77

A5 0.97 0.93 0.86 0.81

A6 1 1 1 3.86

A7 0.76 0.72 0.65 0.60

A8 1.29 1.27 1.23 1.21

A9 2.12 1.99 1.74 1.56



11/12

employed FAD, which can be used to eliminate alternatives that

cannot meet basic requirements in the pre-assessment phase,

and also to rank the remaining alternatives according to their per-

formance in the selection phase. An industrial case study is used to

illustrate the approach. The outcome of FAD application is then

compared to the outcome of Fuzzy TOPSIS, given that it is widely

used MCDM technique. The main contribution of this paper is that

the proposed methodology is a two-phase approach employing

FAD and integrating various MCDM techniques.

On the other hand, the industrial applications of FAD cover

many areas such as shipbuilding industry, transportation and var-

ious selection problems; however it does not include IT evaluation,

neither mobile/electronic tool selection such as PDAs and barcode

scanners. To our knowledge, no previous work investigated such

problem. Another contribution of this paper was to establish crite-

ria and methodology for evaluating mobile logistics tools. Imple-

mentation of a case study in Turkish logistics industry verified

the potential of the proposed methodology. For further research,

another industrial case will be executed and an evaluation of RFID

systems will be performed for the mentioned company employing

the methodology of this paper.

Acknowledgements

The authors acknowledge the contribution of the industrial ex-

perts without which this study could not be accomplished.

References

Ayag, Z., & Ozdemir, R. (2007). A combined fuzzy AHP-goal programming approach

to assembly-line selection.Journal of Intelligent & Fuzzy Systems, 18(4), 345362.Bei, W., Wang, S., & Hu, J. (2006). An analysis of supplier selection in manufacturing

supply chain management. Service Systems and Service Management, 2, 439444.Buyukozkan, G., & Ruan, D. (2008). Evaluation of software development projects

using a fuzzy multi-criteria decision approach. Mathematics and Computers inSimulation, 77(56), 464475.

Cebi, S., & Celik, M. (2007). Fuzzy axiomatic design approach for measuring of

customer satisfaction level on Turkish container ports. In Proceeding of theinternational 11th IFAC symposium on computational economics and financial andindustrial systems (pp. 221226). Istanbul, Turkey.

Cebi, M., & Celik, M. (2008). Ship machinery installation based on fuzzy information

axiom: The case of compressed air system. In Proceedings of the 8th internationalFLINS conference on computational intelligence in decision and control. Madrid,Spain.

Cebi, M., Celik, M., Er, I., & Kahraman, C. (2008). Structuring ship design project

approval mechanism towards operator-system interfaces via fuzzy axiomatic

design principles. In Proceedings of the 8th international FLINS conference oncomputational intelligence in decision and control (pp. 11111116). Madrid,Spain.

Cebi, S., & Kahraman, C. (2010a). Extension of axiomatic design principles under

fuzzy environment. Expert Systems with Applications, 37(3), 26822689.Cebi, S., & Kahraman, C. (2010b). Indicator design for passenger car using fuzzy

axiomatic design principles. Expert Systems with Applications, 37(9), 64706481.Celik, M. (2009). A hybrid design methodology for structuring an integrated

environmental management system (IE MS) for shipping business. Journal ofEnvironmental Management, 90(3), 14691475.

Celik, M., Cebi, S., Kahraman, C., & Er, I. (2009). Application of axiomatic design andTOPSIS methodologies under fuzzy environment for proposing competitive

strategies on Turkish container ports in maritime transportation network.

Expert Systems with Applications, 36(3), 45414557.Celik, M., & Er, I. (2009). Fuzzy axiomatic design extension for managing model

selection paradigm in decision science. Expert Systems with Applications, 36(3),64776484.

Celik, M., Kahraman, C., Cebi, S., & Er, I. (2009). Fuzzy axiomatic design-based

performance evaluation model for docking facilities in shipbuilding industry:

The case of Turkish shipyards. Expert Systems with Applications, 36(1), 599615.Cevikcan, E., Cebi, S., & Kaya, I. (2009). Fuzzy VIKOR and fuzzy axiomatic design

versus to fuzzy TOPSIS: An application of candidate assessment. Journal ofMultiple-Valued Logic and Soft Computing, 15, 181208.

Chamodrakas, I., Batis, D., & Martakos, D. (2010). Supplier selection in electronic

marketplaces using satisfying and fuzzy AHP. Expert Systems with Applications,37, 490498.

Chen, C.-T. (2000). Extensions of the TOPSIS for group decision-making under fuzzy

environment. Fuzzy Sets and Systems, 114, 19.Chen, L.-S., & Cheng, C.-H. (2005). Selecting IS personnel use fuzzy GDSS based on

metric distance method. European Journal of Operational Research, 160, 803820.

Chen, S., & Hwang, C. (1992). Fuzzy multiple attribute decision making: Methods andapplications. Berlin: Springer-Verlag.

Chen, J., Yen, D., & Chen, K. (2009). The acceptance and diffusion of the innovative

smart phone use: A case study of a delivery service company in logistics.

Information & Management, 46, 241248.Cicek, K., & Celik, M. (2009). Selection of porous materials in marine system design:

The case of heat exchanger aboard ships. Materials and Design, 30, 42604266.Dagdeviren, M., Yavuz, S., & Klnc, N. (2009). Weapon selection using the AHP and

TOPSIS methods under fuzzy environment. Expert Systems with Applications, 36,81438151.

Eraslan, E., Akay, D., & Kurt, M. (2006). Usability ranking of intercity bus passengerseats using fuzzy axiomatic design theory. Lecture Notes in Computer Science,410, 141148.

Goncalves-Coelho, A., & Mourao, A. (2007). Axiomatic design as support for

decision-making in a design for manufacturing context: A case study.

International Journal of Production Economics, 109, 8189.Harutunian, V., Nordlund, M., Tate, D., & Suh, N. (1996). Decision making and

software tools for product development based on axiomatic design theory. In

The 1996 CIRP general assembly. Como, Italy.Herrera, F., Herrera-Viedma, E., & Chiclana, F. (2001). Multiperson decision-making

based on multiplicative preference relations. European Journal of OperationalResearch, 129, 372385.

Hoffer, R. (2005). Auditing PDA wireless devices in financial services. EDPACS TheEDP audit, control, and security newsletter, 33, 19.

Hung, K.-C., Julian, P., Chien, T., & Jin, W.-H. (2010). A decision support system for

engineering design based on an enhanced fuzzy MCDM approach. ExpertSystems with Applications, 37, 202213.

Hwang, C., & Yoon, K. (1981). Multiple attribute decision making: Methods and

applications. New York: Springer-Verlag.

Isklar, G., & Bykzkan, G. (2007). Using a multi-criteria decision making

approach to evaluate mobile phone alternatives. Computer Standards &Interfaces, 29, 265274.

Jang, B.-S., Yang, Y.-S., Song, Y.-S., Yeun, Y.-S., & Do, S.-H. (2002). Axiomatic design

approach for marine design problems. Marine Structures, 15, 3556.Jeong, N., Yoo, Y., & Heo, T.-Y. (2009). Moderating effect of personal innovativeness

on mobile-RFID services: Based on Warshaws purchase intention model.

Technological Forecasting and Social Change, 76, 154164.Kahraman, C., & Cebi, S. (2009). A new multi-attribute decision making method:

Hierarchical fuzzy axiomatic design. Expert Systems with Applications, 36(3),48484861.

Kahraman, C., & Kulak, O. (2005). Fuzzy multi-attribute decision making using an

information axiom based approach. Fuzzy multi-criteria decision making. NewYork, USA: Springer, pp. 265278.

Kans, M. (2008). An approach for determining the requirements of computerised

maintenance management systems. Computers in Industry, 3240, 59.Kulak, O. (2005). A decision support system for fuzzy multi-attribute selection of

material handling equipments. Expert System with Application, 29(2), 310319.

Kulak, O., Durmusoglu, M., & Kahraman, C. (2005). Fuzzy multi-attribute equipmentselection based on information axiom.Journal of Materials Processing Technology,169, 337345.

Kulak, O., & Kahraman, C. (2005). Fuzzy multi-attribute selection among

transportation companies using axiomatic design and analytic hierarchy

process. Information Sciences, 170, 191210.Kwong, C., & Bai, H. (2003). Determining the importance weights for the customer

requirements in QFD using a fuzzy AHP with an extent analysis approach. IIETransactions, 35, 619626.

Lee, C.-C., Cheng, H., & Cheng, H.-H. (2007). An empirical study of mobile commerce

in insurance industry: Task-technology fit and individual differences. DecisionSupport Systems, 43, 95110.

Lee, A. H. I. (2009). A fuzzy supplier selection model with the consideration of

benefits, opportunities, costs and risks. Expert Systems with Applications, 36,28792893.

Lehmann, D., & Oshaughnessy, J. (1982). Decision criteria used in buying different

categories of products. Journal of Purchasing and Materials Management, 18,914.

Lin, M., Wang, C., Chen, M., & Chang, C. (2008). Using AHPand TOPSIS approaches in

customer-driven product design process. Computers in Industry, 59, 1731.Lin, C.-J., & Wu, W.-W. (2008). A causal analytical method for group decision-

making under fuzzy environment. Expert Systems with Applications, 34, 205213.Liu, H.-T., & Wang, W.-K. (2009). An integrated fuzzy approach for provider

evaluation and selection in third-party logistics. Expert Systems withApplications, 36, 43874398.

Madria, S. K., Mohania, M., Bhowmick, S., & Bhargava, B. (2002). Mobile data and

transaction management. Information Sciences, 141, 279309.Martinez-Sala, A., Egea-Lopez, E., Garcia-Sanchez, F., & Garcia-Haro, J. (2009).

Tracking of returnable packaging and transport units with active RFID in the

grocery supply chain. Computers in Industry, 60, 161171.Masud, A., & Ravindran, A. (2007). Multiple criteria decision making. Operations

research and management science handbook. Boca Raton, FL: CRC Press.Musilek, P., Guanlao, R., & Barreiro, G. (2005). Genetic programming of fuzzy

aggregation operations. Journal of Intelligent & Fuzzy Systems, 16(2), 107118.Ozel, B., & Ozyoruk, B. (2007). Supplier selection with fuzzy axiomatic design.

Journal of the Faculty of Engineering and Architecture of Gazi University, 22(3),415423.

Pomerol, J.-C., & Romero, S. (2000). Multicriterion decision in management: Principlesand practice. Norwell: Kluwer Academic Publishers.



12/12

Qi, J., Li, L., Li, Y., & Shu, H. (2009). An extension of technology acceptance model:

Analysis of the adoption of mobile data services in China. Systems Research andBehavioral Science, 26, 391507.

Qureshi, M., Kumar, P., & Kumar, D. (2008). 3PL evaluation and selection under a

fuzzy environment: A case study. The ICFAI Journal of Supply Chain Management,5, 3853.

Royes, G., Bastos, R., & Royes, G. (2003). Applicants selection applying a fuzzy

multicriteria CBR methodology. Journal of Intelligent & Fuzzy Systems, 14(4),167180.

Saaty, T. (1980). The analytic hierarchy process. New York: McGraw Hill.

Sanayei, A., Mousavi, S., & Yazdankhah, A. (2010). Group decision making processfor supplier selection with VIKOR under fuzzy environment. Expert Systems with

Applications, 37, 2430.Shih, H.-S. (2008). Incremental analysis for MCDM with an application to group

TOPSIS. European Journal of Operational Research, 186, 720734.Suh, N. (1990). The principles of design. New York: Oxford University Press.Suh, N. (2001). Axiomatic design advances applications. New York: Oxford

University Press.

Tam, M., & Tummala, V. (2001). An application of the AHP in vendor selection of a

telecommunications system. Omega, 29, 171182.Wamba, S., Lefebvre, L., Bendavid, Y., & Lefebvre, E. (2008). Exploring the impact of

RFID technology and theEPC network on mobile B2B e-commerce: Acase study

in the retail industry. International Journal of Production Economics, 112,614629.

Wang, Y.-M., & Parkan, C. (2008). Optimal aggregation of fuzzy preference relations

with an application to broadband internet service selection. European Journal ofOperational Research, 187, 14761486.

Wu, W.-Y., Lin, C., Kung, J.-Y., & Lin, C.-T. (2007). A new fuzzy TOPSIS for fuzzy

MADM problems under group decisions. Journal of Intelligent & Fuzzy Systems,18(2), 109115.

Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce? An empirical

evaluation of the revised technology acceptance model. Information &

Management, 42, 719729.Yeh, C.-H., & Chang, Y.-H. (2009). Modeling subjective evaluation for fuzzy group

multicriteria decision making. European Journal of Operational Research, 194(2),464473.

Ycel, G., & Aktas, E. (2008). An evaluation methodology for ergonomic design of

electronic consumer products based on fuzzy axiomatic design. Journal ofMultiple-Valued Logic and Soft Computing, 14(3-5), 475494.

Zackariasson, P., & Wilson, T. (2002). Technology, after sales service and

organizations. In Association of marketing theory and practice (AMTP). Georgia,USA.

Zadeh, L. (1975). The concept of a linguistic variable and its applications to

approximate reasoning. Information Sciences. 199249 (I), 301357 (II).


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