Using Intuitionistic Fuzzy TOPSIS in Site Selection of Wind Power...

Research ArticleUsing Intuitionistic Fuzzy TOPSIS in SiteSelection of Wind Power Plants in Turkey

Babak Daneshvar Rouyendegh (B Erdebilli) Abdullah Yildizbasi and Uumlmmuumlhan Z B Arikan

Department of Industrial Engineering Ankara Yıldırım Beyazıt University (AYBU) 06010 Ankara Turkey

Correspondence should be addressed to Babak Daneshvar Rouyendegh (B Erdebilli) babekerdebilli2015gmailcom

Received 31 May 2018 Accepted 1 August 2018 Published 16 August 2018

Academic Editor Jiefeng Liu

Copyright copy 2018 Babak Daneshvar Rouyendegh (B Erdebilli) et al This is an open access article distributed under the CreativeCommons Attribution License which permits unrestricted use distribution and reproduction in any medium provided theoriginal work is properly cited

The reduction of energy resources and the increase in environmental consciousness have recently increased the interest in renewableenergy sources Wind energy is from renewable energy sources which are used in many countries Turkey has a lot alternativewind energy plants thanks to its favorable geographical location Where the wind power plant is to be established is a complex andimportant decisive factor It is very important to select the appropriate wind power plant site to take advantage of wind energy andreduce costs In this study we aimed to reach the solution of wind energy plant site selection For this purpose 4 alternative windpower plant locations have been identified To evaluate the alternatives 10 criteria in four dimensions including wind potentiallocation cost and social benefits are selected Since the Multicriterion Decision Making (MCDM) methods are often used inproblem of location selection from past to present TOPSIS method combined with intuitionistic fuzzy set (IFS) has been usedto achieve this goal The main purpose of the TOPSIS method is to rank the alternatives in the worst way The IFS are used toreflect approval rejection and hesitation of decision makers by dealing with real life uncertainty imprecision vagueness andlinguistic human decisions Finally a numerical example is applied for wind power plant site selection In order to demonstratethe effectiveness of IFS the problem is solved by the Fuzzy TOPSIS method using the same data Then the obtained results arecompared with the IFS method to show the effectiveness of the proposed method

1 Introduction

Increased living standards have also increased the need forenergy The shortage of fossil fuels and the negative effectson the environment have created a need for clean energy[1] Radioactive waste generated by nuclear power plantsand security problems especially the explosion of nuclearpower plants in Japan Fukushima triggered the tendency forrenewable energy sources [2]

The use of renewable energy sources ensures sustainabledevelopment Reducing the use of fossil fuels and energyimports achieving national targets by increasing employ-ment and competitiveness and eliminating climate changeand environmental problems are benefits of renewable energysources [8]

Wind energy which is the oldest energy source [3] iscaused by the difference in pressure that occurs when the

earthrsquos surface is exposed to different solar rays [4] Windenergy which provides high usability reliability and cleanenergy is one of the fastest growing renewable energy sourcesall over the world [9] Wind energy is abundant useful andeconomical [1] compared to limited expensive and defectivehydrocarbon based energy [5]

The increase of the urban population and the devel-opment of industry increased Turkeyrsquos energy needs [4]Thanks to its geographical location Turkey has an alternativerenewable energy especially wind energy [7] It is importantto establish where the power plant is to take advantageof this energy at the maximum level [5] Political socialenvironmental cultural and economic criteria should beconsidered in addition to the technical requirements for windpower plant site selection [10]

Multicriterion Decision Making (MCDM) methods areoften used to solve wind power plant site selection problem

HindawiAdvances in Fuzzy SystemsVolume 2018 Article ID 6703798 14 pageshttpsdoiorg10115520186703798

2 Advances in Fuzzy Systems

including complex criterion [9] MCDM methods widelyused in solution of this problem areAnalyticNetwork Process(ANP) Analytic Hierarchy Process (AHP) Elimination andChoice Translating Reality (ELECTRE) and Technique forOrder Preference by Similarity to Ideal Solution (TOPSIS)[6]

In this study the Intuitionistic Fuzzy TOPSIS (IFT)approach is proposed for the solution of the problem of windpower plant site selectionThe most commonly used methodfor ranking alternatives based on criterion is TOPSIS [11]Thealternative chosen from all alternatives is the closest to thepositive ideal solution in other words the farthest away fromthe negative ideal solution Positive ideal solution maximizesthe benefit criterion and minimizes the cost criterion It isthe inverse for the negative ideal solution [12] The IFS isused to deal with real life uncertainty imprecision vaguenessand linguistic human decisions Also it provides reflection ofapproval rejection and hesitation of decision makers [13]

In numerical example 3 alternatives were selected forthe wind power plant and the results of the literature surveywere sorted according to the 10 election decisions deter-mined These criteria are gathered in 4 dimensions Windpotential includes wind speed and wind density criterionLocation consists of surface characteristics proximity powerdistribution network and natural disaster occurrence Indimension of cost there are land purchase cost and initial andannual maintenance Finally social benefits cover culturaland environmental concerns and employment

The rest of this article is organized as follows In Section 2a literature search of the wind power site selection is givenThen the IFTOPSISmethod is given in Section 3 In Section 4this approach is illustrated with a numerical example Theconclusion and comment of the problem are given in Sec-tion 5

2 Literature Review

Increasing global warming and increased energy demandhave led countries to use renewable energy sources Thisstudy is about wind energy from renewable energy sourcesCriterion and method for wind energy site selection havebeen determined by literature survey Summaries of theliterature study are given in Tables 1 and 2

The work in [4] represents the importance of renewableenergy sources and Turkey has a high potential for windenergy The wind energy potential map has been examinedand potential places in Amasra Bandırma Bozcaada andCanakkale have been selected For site selection surfacecharacteristic proximity cost and wind characteristic werechosen as the criteria for selection Choquet integral methodwhich enables interaction between criteria and evaluatesquantitative and qualitative data at the same time is appliedCanakkale was found as the most suitable alternative

The authors of [3] have used an integrated hierarchicalData Envelopment Analysis (DEA) method for wind plantlocation optimization in their work and have implementedit in Iran A total of 125 locations in 25 cities and 5 regionsin each city were evaluated In order to determine thebest region at the first level of location optimization land

cost population and human labor and distances of powerdistribution networks indicators with social and technicalstructure were selected At the second level in order toprioritize the cities used indicators which have technical andgeographical characteristics intensity of natural disastersquantity of proper topographical areas quantity of propergeological areas and average wind blow

The authors of [6] have established a decisionmechanismfor SWHPS (solar-wind hybrid power station) site selectionusing AHP method For the 495 MW SWHPS installationin China the assessment committee chose business benefitssocioeconomic needs and performances as subtargets andbenefit accessibility environment risk and resource asevaluation features Five alternative SWHPS sites are selectedusing the Geographic Information System (GIS) and gridmap As a result alternative A site was chosen as the mostsuitable site

The authors of [2] develop strategies for the selection ofan offshore wind farm in the coastal regions of South KorearsquosJeju Island The settlement criteria are divided into fourcategories conservation areas and environmental protectionenergy resources and economy marine ecology and humanactivities and marine environment They have developed 4scenarios for alternative site selection by utilizing GIS In thescenario where 4 categories were taken into considerationfewer suitable areas were found than other scenarios Butconsideration of all categories will help to reduce the numberof social conflicts among stakeholders and environmentalissues by providing a sustainable and eco-friendly offshorewind farm

The authors of [7] have done a study on site selectionfor wind energy plants They choose Western Turkey asapplication area The application consists of two stages firststage is elimination of unfavorable stages and second stageis examination of existing ones Alternative land areas aretreated as grids of the same size each large enough to builda wind turbine GIS have been used to apply the eliminationcriterion and restrictions Besides Multiple Criterion Deci-sion Analysis (MCDA) is then used to rank and sort thegrids via the evaluation criterion specified The problem hasbeen assessed in 13 areas where several grids are assembled toevaluate larger areas of space to build wind farms instead ofindividual turbines In all MCDAmethods used areas 3 5 8and 12 are emphasized

The authors of [5] used an integrated fuzzy-DEAapproach in this study to decide onwind farm locationsTheyused the Basic Component Analysis (BCA) and NumericalTaxonomy (NT) methods to validate the results of the DEAmodel The model was tested on 25 candidate cities in Iranwith 5 regions in each city In addition 20 other cities areconsidered to be consumers of energy produced The resultsshow the importance of the proximity of consumers in theestablishment of wind farms

The authors of [1] evaluated the possibility of establishingwind farms in the province of Ardabil in northwesternIran by combining ANP and Decision Making Experimentand Evaluation Laboratories (DEMATEL) methods in a GISenvironment Using the 13 layers of information according tothree main criteria environment technical and economical

Advances in Fuzzy Systems 3

Table1Cr

iterio

nfro

mliteraturer

eview

Researchers

Metho

dology

Distance

Cost

Energy

Resources

Wind

Characteris

tics

Elevation

Slop

eProxim

ityEn

vironm

ental

Risks

Hum

anLabo

r

AzadehGhaderi

ampNasrollahi

[3]

DEA

CebiSampKa

hram

an[4]

AHPCh

oquetIntegral

AliAzadehamp

Arm

inRa

himi-G

olkh

anda

ampMoh

senM

oghadd

am[5]

DEA

AliAziziamp

Bahram

Malekmoh

ammadi

ampHam

idRe

zaJafariamp

Hossein

Nasiri

ampVa

hidA

miniP

arsa[1]

ANPDem

atel

GIS

Yunn

aWuampShuaiGeng[6]

AHP

KazimBa

risAticiamp

Ahm

etBa

hadirSim

sekamp

Aydin

UlucanampMustafa

UmurTo

sun

[7]

MCD

AG

IS

Taeyun

Kim

ampJeon

g-II

Park

ampJunh

oMaeng

[2]

AHPGIS


Table 2 Literature review on renewable energy location selection

Authors Title of the Study Article Name

Azadeh Ghaderi amp Nasrollahi [3] Location optimization of wind plants in Iran by an integratedhierarchical Data Envelopment Analysis Renewable Energy

Cebi S amp Kahraman [4] Using Multi Attribute Choquet Integral in Site Selection ofWind Energy Plants The case of Turkey

Journal of Multiple-Valued Logicamp Soft Computing

Ali Azadeh ampArminRahimi-Golkhanda amp MohsenMoghaddam (2013)

Location optimization of wind plants in Iran by an integratedhierarchical data envelopment analysis Renewable Energy

Ali Azizi amp BahramMalekmohammadi ampHamid RezaJafari amp Hossein Nasiri amp Vahid AminiParsa [1]

Land suitability assessment for wind power plant siteselection using ANP-DEMATEL in a GIS environment case

study of Ardabil province IranEnviron Monit Assess

Yunna Wu amp ShuaiGeng[6]

Multi-criterion decision making on selection of solarndashwindhybrid power station location

A case of China EnergyConversion and Management

KazimBarisAtici amp AhmetBahadirSimsek amp Aydin Ulucan ampMustafa UmurTosun[7]

A GIS-based Multiple Criterion Decision Analysis approachfor wind power plant site selection Utilities Policy

Taeyun Kim amp Jeong-II Park ampJunhoMaeng [2]

Offshore wind farm site selection study around Jeju IslandSouth Korea Renewable Energy

a land suitability map was established Then it is reclassifiedaccording to the 5 equal-rate divisions from themost suitableareas that are least suitable The results show that 668 ofArdabil province is most suitable for wind turbines

3 Methodology

MCDM techniques are often used because mathematical andheuristic methods are inadequate in dealing with qualitativefactors in the solution of the problem of location selectionthat has come up to date from the past and is still valid [4]The MCDM methods are useful in reflecting the judgmentsof decision makers and in dealing with the complexity in thedecision process [7]

The TOPSIS method of the MCDM techniques is pro-posed for solving the problem of wind power plant siteselection This section briefly reviews the intuitionistic fuzzyset and TOPSIS method

31 Intuitionistic Fuzzy Set Human decisions in real lifeproblems involve uncertainty and ambiguity In 1965 thefuzzy set theory was proposed by Zadeh to deal with thisambiguity and uncertainty [14] The intuitionistic fuzzy set(A-IFS) which is generalized to the fuzzy set and char-acterized by a membership function and a nonmemberfunction was first introduced in 1986 by Atanassov IFS hasbeen applied to many different fields including logic pro-gramming medical diagnosis decision making evaluationfunction and preference relation [15]

In this section a few definitions and operations of IFS areintroduced

Let119883 be fixed IFS A in X can be defined as

X = s 120583x (s) vx (s) | s isin S (1)

where

120583x (s) 120583x (s) isin [0 1] S 997888rarr [0 1] (2)

vx (s) vx (s) isin [0 1] S 997888rarr [0 1] (3)

120583x(s) and vx(s) are degrees of membership and nonmember-ship function respectively satisfying the following equation

0 le 120583x (s) + vx (s) le 1 foralls isin S R 997888rarr [0 1] (4)

For the IFS A in X 120587x(s) is defined as the intuitionistic indexIt is the measurement of the hesitation degree

120587x (s) = 1 minus 120583x (s) minus vx (s) (5)

Let A and B be IFS of the set S then the multiplicationoperators are correspondingly

X + Y

= 120583x (s) lowast 120583y (s) vx (s) + vy (s) minus vx (s) lowast vy (s) |isin S (6)

32 Intuitionistic Fuzzy TOPSIS The TOPSIS first intro-duced by Hwang and Yoon in 1981 is the most commonmethod of ranking alternatives based on the selection cri-terion The basic idea of the TOPSIS method is to rank thealternatives from best to worst The best solution among thealternatives in the obtained order is the closest one to thepositive ideal solution at the same time as the farthest negativesolution [16]

Fuzzy numbers are used to deal with real life uncertaintyimprecision vagueness and linguistic human decisions Theintuitionistic fuzzy sets better reflect the decision makersrsquoapproval rejection and hesitation [17]


Table 3 Linguistic terms for rating DMs

Linguistic terms IFNsVery Important (080 010)Important (050 020)Medium (050 050)Bad (030 050)Very Bad (020 070)

In this section we present an Intuitionistic Fuzzy TOPSISmodel introduced by [13] for the evaluation of the alterna-tives The order of m alternatives is based on n criterion A =A1A2 Am is set of alternatives C = C1C2 Cnis set of criteria and L = l1 l2 ll represents set ofdecision makers

Now we replace the algorithm with a seven-step proce-dure as follows

Step 1 (calculate the weights of DMs) The importance of theDMs is represented as linguistic terms (Table 3)

Dl = [1205831 ]l 120587l] is the intuitionistic fuzzy number for lthDM ranking The weight of DM can be determined by thefollowing formula

120582l = [120583119897 + 120587119897 (120583119897 (120583119897 + V119897))]sum119896119897=1 [120583119897 + 120587119897 (120583119897 (120583119897 + V119897))] (7)

where

120582l isin [0 1] and119896sum119897=1

120582l = 1 (8)

Step 2 (calculate the weights of criterion) All criteria can notbe regarded as having equal qualification W represents a setof importance levels In order to achieve W all DM views onthe importance of each criterion need to be integrated

Let w119896119895 = (120583119896119895 V119896119895 120587119896119895 ) represent intuitionistic fuzzynumber about the Xj criterion of the kth decision makerThe intuitionistic fuzzy weighted averaging (IFWA) operatoris used to calculate the weights of the criterion The IFWAoperator is developed by Xu (2007)

wj = IFWAr120582 (119908(1)119895 119908(2)119895 119908(119897)119895 ) = 1205821119908(1)119895 oplus 1205822119908(2)119895oplus oplus120582k119908(119897)119895 = [1 minus 119896prod

119897=1

(1 minus 120583(119897)119894119895 )120582l 119896prod119897=1

(V(119897)119894119895 )120582l 119896prod119897=1

(1 minus 120583(119897)119894119895 )120582l minus 119896prod119897=1

(1 minus V(119897)119894119895 )120582l](9)

The importance of criterion is represented as linguistic terms(Table 4)

Step 3 (construct intuitionistic fuzzy decision matrix(IFDM)) To arrive at a precise conclusion each view from agroup of DMs must be combined into a single view to formthe aggregated intuitionistic fuzzy decision matrix (AIFDM)model

Table 4 Linguistic terms for rating criterion

Linguistic terms IFNsVery Important (090 010)Important (075 020)Medium (050 045)Unimportant (035 060)Very Unimportant (010 090)

Let R(l) = (119903(1)119894119895 )mlowastn be the IFDM of each DM120582 = 1205821 1205822 1205823 120582k is the weight of the DM

R = (rij)m1015840lowastn1015840 (10)

where

rij = IFWAr120582 (119903(1)119894119895 119903(2)119894119895 119903(119896)119894119895 ) = 1205821119903(1)119894119895 oplus 1205822119903(2)119894119895 oplus oplus 120582k119903(119896)119894119895 = [1 minus 119896prod

119897=1

(1 minus 120583(119897)119894119895 )120582l 119896prod119897=1

(V(119897)119894119895 )120582l 119896prod119897=1


(1 minus V(119897)119894119895 )120582l](11)

Step 4 (the calculation of aggregated weighted intuitionisticfuzzy decisionmatrix (AWIFDM)) The aggregatedweightedintuitionistic fuzzy decision matrix is calculated by combin-ing W and R

R oplusW = (1205831015840119894119895 V1015840119894119895)= ⟨119909 120583119894119895 lowast 120583119895 V119894119895 + V119895 minus V119894119895 lowast V119895⟩ (12)

1205871015840119894119895 = 1 minus 120583119894119895 lowast 120583119895 minus V119894119895 minus V119895 + V119894119895 lowast V119895 (13)

Step 5 (calculate intuitionistic fuzzy positive and negativeideal solution) Let J1 be benefit criterion and J2 be costcriterion Alowast represents intuitionistic fuzzy positive idealsolution and 119860minus represents intuitionistic fuzzy negative idealsolution Then Alowast and 119860minus are calculated as

119860lowast = (1199031015840lowast1 1199031015840lowast2 1199031015840lowast119899 ) 1199031015840lowast119895 = (1205831015840lowast119895 V1015840lowast119895 1205871015840lowast119895 ) 119895 = 1 2 119899 (14)

119860minus = (1199031015840minus1 1199031015840minus2 1199031015840minus119899 ) 1199031015840minus119895 = (1205831015840minus119895 V1015840minus119895 1205871015840minus119895 ) 119895 = 1 2 119899 (15)


where

1205831015840lowast119895 = (max119894

1205831015840119894119895 119895 isin 1198691) (min1198941205831015840119894119895 119895 isin 1198692) (16)

v1015840lowast

119895 = (min119894V1015840119894119895 119895 isin 1198691) (max

119894V1015840119894119895 119895 isin 1198692) (17)

1205871015840lowast119895 = (1 minusmax119894

1205831015840119894119895 minusmin119894V1015840119894119895 119895 isin 1198691)

(1 minusmin1198941205831015840119894119895 minusmax

119894V1015840119894119895 119895 isin 1198692) (18)

1205831015840minus119895 = (min1198941205831015840119894119895 119895 isin 1198691) (max

1198941205831015840119894119895 119895 isin 1198692) (19)

v1015840minus

119895 = (max119894

V1015840119894119895 119895 isin 1198691) min119894V1015840119894119895 119895 isin 1198692 (20)

1205871015840minus119895 = (1 minusmin1198941205831015840119894119895 minusmax

119894V1015840119894119895 119895 isin 1198691)

(1 minusmax119894

1205831015840119894119895 minusmin119894V1015840119894119895 119895 isin 1198692) (21)

Step 6 (obtain the separation measures between the alter-natives) In this paper the normalized Euclidean distanceproposed by Szmidt and Kacprzyk (2000) is used formeasureseparation between alternatives on intuitionistic fuzzy setSilowast and 119878119894minus the separation measures of each alternative arecalculated for intuitionistic fuzzy positive ideal and negativeideal solutions respectively

119878119894lowast= radic 12119899

119899sum119895=1

[(1205831015840119894119895 minus 1205831015840lowast119895 )2 + (V1015840119894119895 minus v1015840lowast119895 )2 + (1205871015840119894119895 minus 1205871015840lowast119895 )2] (22)

119878119894minus= radic 12119899

119899sum119895=1

[(1205831015840119894119895 minus 1205831015840minus119895 )2 + (V1015840119894119895 minus v1015840minus119895 )2 + (1205871015840119894119895 minus 1205871015840minus119895 )2] (23)

Step 7 (determine the final ranking) The relative closenesscoefficient of an alternative Ai with respect to the intuitionis-tic fuzzy positive ideal solution Alowast is defined as follows

119862119862119894lowast = 119878119894minus119878119894lowast + 119878119894minus 119908ℎ119890119903119890 0 ge 119862119894lowast le 1 (24)

After the relative closeness coefficient of each alternative isdetermined alternatives are ranked according to descendingorder of CCilowast

4 Case Study

In this section the IFTOPSIS method for selection ofappropriate wind power plant site is applied in a numericalexample It is shown in Figure 1 A decision maker groupconsisting of three experts was formed for this reasonDecision maker 1 is a geographer decision maker 2 is a

map engineer and decision maker 3 is an energy engineerCanakkale Izmir Samsun and Mersin are selected as thealternative for wind power plant location Three decisionmakers evaluate these alternatives and for selection of anappropriate alternative we will use selection criterion givenin Table 6 Ten criteria are considered as follows

Wind speed (C1) this includes a high average wind speed andthe ability to provide the maximum benefit from the wind

Wind density (C2) it is the kinetic energy measure producedby wind unit square meter and time

Surface characteristic (C3) this includes geological conditionssuch as soil structure and infrastructure conditions and topo-graphic conditions such as geographical direction elevationof the plant and slope which are properties to be taken intoconsideration in the site selection

Proximity (C4) due to some factors such as noise pollutionhealthcare aesthetics and safety the wind power plantshould be away from areas such as urban areas waterresource main roads protected areas and airports

Power distribution network (C5) to prevent energy losses theplant must be installed in areas close to the consumers

Natural disaster occurrence (C6) this criterion is aimedat establishing the plant in places where safe and naturaldisasters are less experienced

Land purchase cost (C7) this criterion refers to the purchasecost of the place where the plant will be established

Initial and annual maintenance cost (C8) installation andannual maintenance costs are the criteria that should not beignored

Cultural end environmental concerns (C9) the plant to beinstalled must not be allowed to harm the environment andthe historical heritage

Employment (C10)being close to the available human labor ofthe plant is important in terms of employee accommodationand salaries

41 Application of Intuitionistic FuzzyTOPSISMethod In thissection Intuitionistic Fuzzy TOPSIS model is applied for theevaluation of the alternatives

In Step 1 the linguistic terms in Table 3 and (7) are usedto calculate the weight of the decision makers and the resultsin Table 7 were obtained

In the second step the opinions of the decision makersabout the criterion weight are calculated as shown in Table 10using Table 4 and (9)

Decision makers were asked to evaluate each alternativeusing linguistic terms in Table 5 for each criterion andTable 8 was generated Using (11) Table 11 is calculatedso that the decisions of DMs are collected into a singledecision


POWER PLANT SITE SELECTION

Wind Potential

(C1)

Location(C2)

Cost(C3)

SocialBenefits

(C4)

Wind DensityWind SpeedSurface

Characteristic ProximityNatural Disaster

Occurrence

PowerDistribution

Network

Land Purchase

Cost

Initial and annual

maintenance

Cultural and environmental

concernsEmployment

(A1) (A2) (A3) (A4)

Figure 1 Alternative assessment based on criterion

Table 5 Linguistic terms for rating the alternatives

Linguistic terms IFNsVery Good (VG) [100 000 000]Good (G) [085 005 010]Moderately Good (MG) [070 020 010]Fair (F) [050 050 000]Moderately Poor (Mp) [040 050 010]Poor (P) [025 060 015]Very Poor (VP) [000 090 010]

Table 6 The dimension and criterion of wind power plant siteselection

Target Dimension Criterion

Wind powerplant siteselection

Wind Potential Wind speedWind density

Location

Surface characteristicProximityPower distribution networkNatural disaster occurrence

Cost Land purchase costInitial and annual maintenancecost

Social BenefitsCultural end environmentalconcernsEmployment

By combining the criterion weights and the aggregatedintuitionistic fuzzy decision matrix with the help of (12)and (13) an aggregated weighted intuitionistic fuzzy decisionmatrix is obtained in Table 12

Table 7 The importance and weights of decision makersrsquo opinions

DM1 DM2 DM3Linguistic terms Very Important Important Medium120582 042 034 024

In Step 5 fromAIFMDA+ is calculated by using (16) (17)and (18) and Aminus is calculated by using (19) (20) and (21) A+andAminus are shown that in Table 13 and inTable 14 respectively

To calculate Slowast (22) is used to calculate 119878minus (23) is usedand to calculate 119862119862lowast119894 (24) is used Slowast and 119878minus represent sepa-ration measurement and 119862119862lowast119894 shows ranking of alternatives119878lowast119878minusand 119862119862lowast119894 values for the alternatives are determined andpresented in Table 15

42 Application of Fuzzy TOPSIS Method In this sectionthe data used in the solution phase of the IntuitionisticFuzzy TOPSISmodel (decisionmakers opinions importanceweight of the criterion) were also used in Fuzzy TOPSISapplication As a result of this evaluation the results obtainedby the two different approaches under the same data willbe compared and the effectiveness and solution results ofthe proposed Intuitionistic Fuzzy TOPSIS model will beevaluated

After obtaining fuzzy ratings from Tables 9 and 10normalized fuzzy decision matrix determined is shown inTable 16

By combining the criterion weights and the aggregatedIntuitionistic fuzzy decision matrix aggregated weightedintuitionistic fuzzy decision matrix is obtained After thatfuzzy positive ideal solution (119860+) and negative ideal solution(119860minus) obtained according to the aggregated weighted intu-itionistic fuzzy decisionmatrix are presented in Tables 17 and18 respectively


Table 8 Importance of alternative based on opinion of DMs

Decision-Makers (DMs) Alternative CriterionC1 C2 C3 C4 C5 C6 C7 C8 C9 C10

DM1

Mersin MG G MP MG MG MG G MP F PCanakkale VG VG G VG G MP MG MG MP MP

Izmir VG VG F G VG F F G MG MGSamsun G F MG MP F G VG F P F

DM2

Mersin MG G F F F G VG F MP PCanakkale VG VG MG MG VG F MG G G MG

Izmir G G G G G MP F MG MG FSamsun MG MG MP MP MG MG G MP F MP

DM3

Mersin F MG MP MP F F G F MP PCanakkale VG VG MG G G MP MG G MG F

Izmir G VG G MG VG P MP MG F GSamsun MG F F F MG MG F VG P MG

Table 9 The importance weight of the criterion

Criterion DM1 DM2 DM3C1 I VI IC2 VI VI VIC3 I M MC4 M U MC5 M I MC6 I I MC7 VI I VIC8 U M IC9 VU U UC10 M U U

Table 10 Weight of criterion

C1 (082016003)C2 (090010000)C3 (063032005)C4 (045050005)C5 (060034005)C6 (070024005)C7 (086013001)C8 (053042005)C9 (025071003)C10 (042053005)

The calculation of distance (119878lowast 119886119899119889 119878minus) of each alternativefrom119860+ and119860minus is determinedThen by using the (119878lowast 119886119899119889 119878minus)values closeness coefficient (119862119862lowast119894 ) of each alternative iscalculated and shown in Table 19 According to the obtainedresults Canakkale is the best alternative and Samsun is theworst alternative

43 Comparison of Intuitionistic Fuzzy TOPSIS with FuzzyTOPSIS Method In this section the results obtained bytwo different methods will be compared As Table 20 and

001020304050607

CCi

Val

ues

Alternatives

Comparison of Proposed Methods

Intuitionistic Fuzzy TOPSISFuzzy TOPSIS

A1

- Mer

sin

A2

ndash Ccedil

anak

kale

A4

- Sam

sun

3minus

İＴＧＣＬ

Figure 2 Comparison of two different methods results

Figure 2 show the results obtained by Intuitionistic FuzzyTOPSIS method and Fuzzy TOPSIS method according tothe Intuitionistic Fuzzy TOPSIS method the most importantalternative is Izmir Later CanakkaleMersin and Samsun arelisted respectively According to the results obtained by FuzzyTOPSIS method Canakkale is the most important alterna-tive followed by Mersin Izmir and Samsun respectively

Given the results obtained it is clear how effectivethe weighting of decision makersrsquo opinions is on the out-come which demonstrates the effectiveness of the proposedmethod

5 Conclusion

Many reasons such as reduced resources increased envi-ronmental problems and energy need have led the worldto trend to renewable energy The wind energy is fromrenewable energy that develops rapidly all around the worldTo get the maximum benefit from wind energy where the


Table11R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(066025009)

(082007011)

(044050006)

(058034008)

(060034006)

(073016011)

(100000000)

(046050004)

(044050006)

(025060015

)A

2(100000000)

(100000000)

(078011011)

(100000000)

(100000000)

(044050006)

(070020010

)(080009011)

(068018013

)(055037009)

A3

(100000000)

(100000000)

(075013012

)(082007011)

(100000000)

(0410

52006)

(048050002)

(078011011)

(066025009)

(070020011)

A4

(078011011)

(058037005)

(0570

34009)

(043050007)

(063029008)

(078011011)

(100000000)

(100000000)

(035056009)

(053

040007)


Table12A

ggregatedweightedintuition

isticfuzzydecisio

nmatrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1(0540

03680092)

(074

1016

30097)

(027306600067)

(026206680070)

(036105660073)

(05160361012

3)(0863012

70010)

(024307090048)

(011308560031)

(010

408130083)

A2(0817015

80025)

(0900010

0000

0)(0486039

60118)

(045304960050)

(060503420053)

(030706210071)

(0604

03010094)

(04210470010

8)(017407640062)

(022807030069)

A3(0817015

80025)

(0900010

0000

0)(04710410012

0)(037

305310

096)

(060503420053)

(029206380070)

(041205630024)

(04090483010

8)(016

80783004

8)(029206230085)

A4(063402520114)

(052

204300049)

(035705510091)

(019

307480059)

(0380053

50085)

(0547032

80126)

(0863012

70010)

(052

704180055)

(008808740038)

(022107200059)


Table 13 A+

C1 (081701580025)C2 (090001000000)C3 (048603960118)C4 (045304960050)C5 (060503420053)C6 (054703280126)C7 (041205630024)C8 (024307090048)C9 (017407640062)C10 (029206230085)

Table 14 Aminus

C1 (054003680092)C2 (052204300049)C3 (027306600067)C4 (019307480059)C5 (036105660073)C6 (029206380070)C7 (086301270010)C8 (052704180055)C9 (008808740038)C10 (010408130083)

Table 15 Separation measures and the relative closeness coefficient of each alternative

Alternatives Slowast 119878minus

119862119862lowast119894

Mersin 02166 01466 04036

Canakkale 01342 02131 06135

Izmir 01136 02425 06811

Samsun 02409 01075 03086

plant is installed is a very important decision that needs to betaken In addition to cost many factors such as sustainabilityand the environmentmust be consideredTherefore thewindpower station site selection problem should be considered asMCDM problem

In this paper to solve problem of wind power plant siteselection the TOPSISmethod is used and combined with theintuitionistic fuzzy number which is reflecting the judgmentsof decision makers and dealing with the complexity inthe decision process so that more accurate results can beachieved Wind potential location cost and social benefitswere defined as dimension of criterion and the ten selectedcriteria were collected under these dimensions The weightsof the criterion importancewere decided in the establishmentof the wind power plant and the selection was made in thisdirection The alternatives evaluated based on these criteriaare Mersin Canakkale Izmir and Samsun The results haveshown that Izmir is the best alternative for wind power site

Also Canakkale Mersin and Samsun follow it respectivelyThus which alternative is most appropriate is determined

For the future research the other MCDMmethodologieslike ELECTRE PROMETHEE etc can be used for windpower site selection and the obtained results can be comparedwith ours Furthermore a comprehensive study can becarried out by identifying other main criteria and separatingthem with subcriterion

Data Availability

The data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

The authors declare that they have no conflicts of interest


Table16R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(461218049)

(724090090)

(284327044)

(349262044)

(538379032

)(42515

6041)

(697026052

)(313

335022)

(255294039

)(132

317079)

A2(728000000)

(905000000)

(491098065)

(556055044)

(854032063)

(270311041)

(54215

5077)

(537

067067)

(38214

7059)

(282211035

)A

3(655024049)

(859015030)

(48013

1044

)(524065065)

(901016032

)(238332052

)(361387026)

(50310

1067)

(37317

7039)

(36113

2035)

A4(54610

9073)

(513

362030)

(349262044)

(284327044)

(601285063)

(467093062)

(60714

2026)

(425224022)

(196333059)

(282211035)


Table 17 A+ positive distance of alternatives

ALTERNATIVES C1 C2 C3 C4 C5 C6 C7 C8 C9 C10A1 0726 0550 0433 0331 0733 0173 0153 0393 0143 0319A2 0115 0121 0244 0451 0407 0682 0457 0374 0442 0217A3 0223 0178 0211 0436 0414 0570 0966 0332 0372 0328A4 0499 1176 0304 0412 0615 0872 0280 0251 0158 0217

Table 18 Aminus negative distance of alternatives



Alternatives 119878lowast

119878minus

119862119862lowast119894

RankingA1 - Mersin 39545 42258 0517 2A2 ndash Canakkale 35102 49020 0583 1A3 ndash Izmir 40305 42441 0513 3A4 - Samsun 47853 35938 0429 4

Table 20 Comparison of the relative closeness coefficient of two methods

Alternatives Intuitionistic Fuzzy TOPSIS CCi Ranking Fuzzy TOPSIS CCi RankingA1 - Mersin 04036 3 0517 2A2 ndash Canakkale 06135 2 0583 1A3 ndash Izmir 06811 1 0513 3A4 - Samsun 03086 4 0429 4

References

[1] A Azizi B Malekmohammadi H R Jafari H Nasiri and VAmini Parsa ldquoLand suitability assessment for wind power plantsite selection usingANP-DEMATEL in aGIS environment casestudy of Ardabil province Iranrdquo Environmental Modeling ampAssessment vol 186 no 10 pp 6695ndash6709 2014

[2] T Kim J-I Park and J Maeng ldquoOffshore wind farm siteselection study around Jeju Island South Koreardquo RenewableEnergy vol 94 pp 619ndash628 2016

[3] A Azadeh S F Ghaderi and M R Nasrollahi ldquoLocationoptimization ofwind plants in Iran by an integrated hierarchicalData Envelopment Analysisrdquo Renewable Energy vol 36 no 5pp 1621ndash1631 2011

[4] S Cebi and C Kahraman ldquoUsing multi attribute choquetintegral in site selection of wind energy plants The case ofturkeyrdquo Journal of Multiple-Valued Logic and Soft Computingvol 20 no 5-6 pp 423ndash443 2013

[5] A Azadeh A Rahimi-Golkhandan and M MoghaddamldquoLocation optimization of wind power generation-transmissionsystems under uncertainty using hierarchical fuzzyDEAA case

studyrdquo Renewable and Sustainable Energy Reviews vol 30 pp877ndash885 2014

[6] W Yunna and S Geng ldquoMulti-criterion decision making onselection of solar-wind hybrid power station location A caseof Chinardquo Energy Conversion andManagement vol 81 pp 527ndash533 2014

[7] K B Atici A B Simsek A Ulucan and M U Tosun ldquoA GIS-based Multiple Criterion Decision Analysis approach for windpower plant site selectionrdquo Utilities Policy vol 37 pp 86ndash962015

[8] T Tsoutsos I Tsitoura D Kokologos and K KalaitzakisldquoSustainable siting process in large wind farms case study inCreterdquo Renewable Energy vol 75 pp 474ndash480 2015

[9] D Latinopoulos and K Kechagia ldquoA GIS-based multi-criterionevaluation for wind farm site selection A regional scale appli-cation in Greecerdquo Renewable Energy vol 78 pp 550ndash560 2015

[10] Z Aljicevic A Kostic N Dautbasic and G Karli ldquoModelof Fuzzy Logic for Selection Infrastructural InvestmentProject of Wind Farm Locationsrdquo in Proceedings of the 2016httpsdoiorg10250727thdaaamproceedings107

[11] H Gupta andM K Barua ldquoSupplier selection among SMEs onthe basis of their green innovation ability using BWMand fuzzy


TOPSISrdquo Journal of Cleaner Production vol 152 pp 242ndash2582017

[12] A Zouggari and L Benyoucef ldquoSimulation based fuzzy TOPSISapproach for group multi-criteria supplier selection problemrdquoEngineering Applications of Artificial Intelligence vol 25 no 3pp 507ndash519 2012

[13] B D Rouyendegh ldquoDeveloping an Integrated ANP and Intu-itionistic Fuzzy TOPSIS Model for Supplier Selectionrdquo Journalof Testing and Evaluation vol 43 no 3 p 20130114 2015

[14] K Devi and S P Yadav ldquoA multicriterion intuitionistic fuzzygroup decision making for plant location selection with ELEC-TRE methodrdquo nternational Journal of Advanced ManufacturingTechnology vol 66 no 9-12 pp 1219ndash1229

[15] M-C Wu and T-Y Chen ldquoThe ELECTRE multicriterionanalysis approach based onAtanassovs intuitionistic fuzzy setsrdquoExpert Systemswith Applications vol 38 no 10 pp 12318ndash123272011

[16] H Gupta andM K Barua ldquoSupplier selection among SMEs onthe basis of their green innovation ability using BWMand fuzzyTOPSISrdquo Journal of Cleaner Production vol 152 no 0 pp 242ndash258 2017

[17] Y Wu J Zhang J Yuan S Geng and H Zhang ldquoStudy of deci-sion framework of offshore wind power station site selectionbased on ELECTRE-III under intuitionistic fuzzy environmentA case of Chinardquo Energy Conversion and Management vol 113pp 66ndash81 2016

Computer Games Technology

International Journal of

Hindawiwwwhindawicom Volume 2018

Hindawiwwwhindawicom

Journal ofEngineeringVolume 2018

Advances in

FuzzySystems


Volume 2018


ReconfigurableComputing



Applied Computational Intelligence and Soft Computing

thinspAdvancesthinspinthinsp

thinspArtificial Intelligence

Hindawiwwwhindawicom Volumethinsp2018


Civil EngineeringAdvances in


Electrical and Computer Engineering

Journal of

Journal of

Computer Networks and Communications


Hindawi

wwwhindawicom Volume 2018

Advances in

Multimedia


Biomedical Imaging



Engineering Mathematics


RoboticsJournal of



Computational Intelligence and Neuroscience


Mathematical Problems in Engineering

Modelling ampSimulationin EngineeringHindawiwwwhindawicom Volume 2018

Hindawi Publishing Corporation httpwwwhindawicom Volume 2013Hindawiwwwhindawicom

The Scientific World Journal

Volume 2018


Human-ComputerInteraction

Advances in


Scientic Programming

Submit your manuscripts atwwwhindawicom


including complex criterion [9] MCDM methods widelyused in solution of this problem areAnalyticNetwork Process(ANP) Analytic Hierarchy Process (AHP) Elimination andChoice Translating Reality (ELECTRE) and Technique forOrder Preference by Similarity to Ideal Solution (TOPSIS)[6]

In this study the Intuitionistic Fuzzy TOPSIS (IFT)approach is proposed for the solution of the problem of windpower plant site selectionThe most commonly used methodfor ranking alternatives based on criterion is TOPSIS [11]Thealternative chosen from all alternatives is the closest to thepositive ideal solution in other words the farthest away fromthe negative ideal solution Positive ideal solution maximizesthe benefit criterion and minimizes the cost criterion It isthe inverse for the negative ideal solution [12] The IFS isused to deal with real life uncertainty imprecision vaguenessand linguistic human decisions Also it provides reflection ofapproval rejection and hesitation of decision makers [13]

In numerical example 3 alternatives were selected forthe wind power plant and the results of the literature surveywere sorted according to the 10 election decisions deter-mined These criteria are gathered in 4 dimensions Windpotential includes wind speed and wind density criterionLocation consists of surface characteristics proximity powerdistribution network and natural disaster occurrence Indimension of cost there are land purchase cost and initial andannual maintenance Finally social benefits cover culturaland environmental concerns and employment

The rest of this article is organized as follows In Section 2a literature search of the wind power site selection is givenThen the IFTOPSISmethod is given in Section 3 In Section 4this approach is illustrated with a numerical example Theconclusion and comment of the problem are given in Sec-tion 5

2 Literature Review

Increasing global warming and increased energy demandhave led countries to use renewable energy sources Thisstudy is about wind energy from renewable energy sourcesCriterion and method for wind energy site selection havebeen determined by literature survey Summaries of theliterature study are given in Tables 1 and 2

The work in [4] represents the importance of renewableenergy sources and Turkey has a high potential for windenergy The wind energy potential map has been examinedand potential places in Amasra Bandırma Bozcaada andCanakkale have been selected For site selection surfacecharacteristic proximity cost and wind characteristic werechosen as the criteria for selection Choquet integral methodwhich enables interaction between criteria and evaluatesquantitative and qualitative data at the same time is appliedCanakkale was found as the most suitable alternative

The authors of [3] have used an integrated hierarchicalData Envelopment Analysis (DEA) method for wind plantlocation optimization in their work and have implementedit in Iran A total of 125 locations in 25 cities and 5 regionsin each city were evaluated In order to determine thebest region at the first level of location optimization land

cost population and human labor and distances of powerdistribution networks indicators with social and technicalstructure were selected At the second level in order toprioritize the cities used indicators which have technical andgeographical characteristics intensity of natural disastersquantity of proper topographical areas quantity of propergeological areas and average wind blow

The authors of [6] have established a decisionmechanismfor SWHPS (solar-wind hybrid power station) site selectionusing AHP method For the 495 MW SWHPS installationin China the assessment committee chose business benefitssocioeconomic needs and performances as subtargets andbenefit accessibility environment risk and resource asevaluation features Five alternative SWHPS sites are selectedusing the Geographic Information System (GIS) and gridmap As a result alternative A site was chosen as the mostsuitable site

The authors of [2] develop strategies for the selection ofan offshore wind farm in the coastal regions of South KorearsquosJeju Island The settlement criteria are divided into fourcategories conservation areas and environmental protectionenergy resources and economy marine ecology and humanactivities and marine environment They have developed 4scenarios for alternative site selection by utilizing GIS In thescenario where 4 categories were taken into considerationfewer suitable areas were found than other scenarios Butconsideration of all categories will help to reduce the numberof social conflicts among stakeholders and environmentalissues by providing a sustainable and eco-friendly offshorewind farm

The authors of [7] have done a study on site selectionfor wind energy plants They choose Western Turkey asapplication area The application consists of two stages firststage is elimination of unfavorable stages and second stageis examination of existing ones Alternative land areas aretreated as grids of the same size each large enough to builda wind turbine GIS have been used to apply the eliminationcriterion and restrictions Besides Multiple Criterion Deci-sion Analysis (MCDA) is then used to rank and sort thegrids via the evaluation criterion specified The problem hasbeen assessed in 13 areas where several grids are assembled toevaluate larger areas of space to build wind farms instead ofindividual turbines In all MCDAmethods used areas 3 5 8and 12 are emphasized

The authors of [5] used an integrated fuzzy-DEAapproach in this study to decide onwind farm locationsTheyused the Basic Component Analysis (BCA) and NumericalTaxonomy (NT) methods to validate the results of the DEAmodel The model was tested on 25 candidate cities in Iranwith 5 regions in each city In addition 20 other cities areconsidered to be consumers of energy produced The resultsshow the importance of the proximity of consumers in theestablishment of wind farms

The authors of [1] evaluated the possibility of establishingwind farms in the province of Ardabil in northwesternIran by combining ANP and Decision Making Experimentand Evaluation Laboratories (DEMATEL) methods in a GISenvironment Using the 13 layers of information according tothree main criteria environment technical and economical


Table1Cr

iterio

nfro

mliteraturer

eview

Researchers

Metho

dology

Distance

Cost

Energy

Resources

Wind

Characteris

tics

Elevation

Slop

eProxim

ityEn

vironm

ental

Risks

Hum

anLabo

r

AzadehGhaderi

ampNasrollahi

[3]

DEA

CebiSampKa

hram

an[4]

AHPCh

oquetIntegral

AliAzadehamp

Arm

inRa

himi-G

olkh

anda

ampMoh

senM

oghadd

am[5]

DEA

AliAziziamp

Bahram

Malekmoh

ammadi

ampHam

idRe

zaJafariamp

Hossein

Nasiri

ampVa

hidA

miniP

arsa[1]

ANPDem

atel

GIS

Yunn

aWuampShuaiGeng[6]

AHP

KazimBa

risAticiamp

Ahm

etBa

hadirSim

sekamp

Aydin

UlucanampMustafa

UmurTo

sun

[7]

MCD

AG

IS

Taeyun

Kim

ampJeon

g-II

Park

ampJunh

oMaeng

[2]

AHPGIS




















3 Methodology






X = s 120583x (s) vx (s) | s isin S (1)

where

120583x (s) 120583x (s) isin [0 1] S 997888rarr [0 1] (2)







X + Y











120582l = [120583119897 + 120587119897 (120583119897 (120583119897 + V119897))]sum119896119897=1 [120583119897 + 120587119897 (120583119897 (120583119897 + V119897))] (7)

where

120582l isin [0 1] and119896sum119897=1

120582l = 1 (8)




119897=1

(1 minus 120583(119897)119894119895 )120582l 119896prod119897=1

(V(119897)119894119895 )120582l 119896prod119897=1


(1 minus V(119897)119894119895 )120582l](9)






R = (rij)m1015840lowastn1015840 (10)

where


119897=1

(1 minus 120583(119897)119894119895 )120582l 119896prod119897=1

(V(119897)119894119895 )120582l 119896prod119897=1


(1 minus V(119897)119894119895 )120582l](11)








where

1205831015840lowast119895 = (max119894

1205831015840119894119895 119895 isin 1198691) (min1198941205831015840119894119895 119895 isin 1198692) (16)

v1015840lowast

119895 = (min119894V1015840119894119895 119895 isin 1198691) (max

119894V1015840119894119895 119895 isin 1198692) (17)

1205871015840lowast119895 = (1 minusmax119894

1205831015840119894119895 minusmin119894V1015840119894119895 119895 isin 1198691)


119894V1015840119894119895 119895 isin 1198692) (18)


1198941205831015840119894119895 119895 isin 1198692) (19)

v1015840minus

119895 = (max119894

V1015840119894119895 119895 isin 1198691) min119894V1015840119894119895 119895 isin 1198692 (20)


119894V1015840119894119895 119895 isin 1198691)

(1 minusmax119894

1205831015840119894119895 minusmin119894V1015840119894119895 119895 isin 1198692) (21)


119878119894lowast= radic 12119899

119899sum119895=1


119878119894minus= radic 12119899

119899sum119895=1





4 Case Study



















Wind Potential

(C1)

Location(C2)

Cost(C3)

SocialBenefits

(C4)



Occurrence

PowerDistribution

Network

Land Purchase

Cost

Initial and annual

maintenance


concernsEmployment

(A1) (A2) (A3) (A4)








Location















DM1



DM2



DM3






C1 (082016003)C2 (090010000)C3 (063032005)C4 (045050005)C5 (060034005)C6 (070024005)C7 (086013001)C8 (053042005)C9 (025071003)C10 (042053005)



001020304050607

CCi

Val

ues

Alternatives



A1

- Mer

sin

A2

ndash Ccedil

anak

kale

A4

- Sam

sun

3minus

İＴＧＣＬ




5 Conclusion



Table11R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(066025009)

(082007011)

(044050006)

(058034008)

(060034006)

(073016011)

(100000000)

(046050004)

(044050006)

(025060015

)A

2(100000000)

(100000000)

(078011011)

(100000000)

(100000000)

(044050006)

(070020010

)(080009011)

(068018013

)(055037009)

A3

(100000000)

(100000000)

(075013012

)(082007011)

(100000000)

(0410

52006)

(048050002)

(078011011)

(066025009)

(070020011)

A4

(078011011)

(058037005)

(0570

34009)

(043050007)

(063029008)

(078011011)

(100000000)

(100000000)

(035056009)

(053

040007)


Table12A


isticfuzzydecisio

nmatrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1(0540

03680092)

(074

1016

30097)

(027306600067)

(026206680070)

(036105660073)

(05160361012

3)(0863012

70010)

(024307090048)

(011308560031)

(010

408130083)

A2(0817015

80025)

(0900010

0000

0)(0486039

60118)

(045304960050)

(060503420053)

(030706210071)

(0604

03010094)

(04210470010

8)(017407640062)

(022807030069)

A3(0817015

80025)

(0900010

0000

0)(04710410012

0)(037

305310

096)

(060503420053)

(029206380070)

(041205630024)

(04090483010

8)(016

80783004

8)(029206230085)

A4(063402520114)

(052

204300049)

(035705510091)

(019

307480059)

(0380053

50085)

(0547032

80126)

(0863012

70010)

(052

704180055)

(008808740038)

(022107200059)


Table 13 A+

C1 (081701580025)C2 (090001000000)C3 (048603960118)C4 (045304960050)C5 (060503420053)C6 (054703280126)C7 (041205630024)C8 (024307090048)C9 (017407640062)C10 (029206230085)

Table 14 Aminus

C1 (054003680092)C2 (052204300049)C3 (027306600067)C4 (019307480059)C5 (036105660073)C6 (029206380070)C7 (086301270010)C8 (052704180055)C9 (008808740038)C10 (010408130083)



119862119862lowast119894

Mersin 02166 01466 04036

Canakkale 01342 02131 06135

Izmir 01136 02425 06811

Samsun 02409 01075 03086





Data Availability





Table16R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(461218049)

(724090090)

(284327044)

(349262044)

(538379032

)(42515

6041)

(697026052

)(313

335022)

(255294039

)(132

317079)

A2(728000000)

(905000000)

(491098065)

(556055044)

(854032063)

(270311041)

(54215

5077)

(537

067067)

(38214

7059)

(282211035

)A

3(655024049)

(859015030)

(48013

1044

)(524065065)

(901016032

)(238332052

)(361387026)

(50310

1067)

(37317

7039)

(36113

2035)

A4(54610

9073)

(513

362030)

(349262044)

(284327044)

(601285063)

(467093062)

(60714

2026)

(425224022)

(196333059)

(282211035)








119878minus

119862119862lowast119894




References


























Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in





Table1Cr

iterio

nfro

mliteraturer

eview

Researchers

Metho

dology

Distance

Cost

Energy

Resources

Wind

Characteris

tics

Elevation

Slop

eProxim

ityEn

vironm

ental

Risks

Hum

anLabo

r

AzadehGhaderi

ampNasrollahi

[3]

DEA

CebiSampKa

hram

an[4]

AHPCh

oquetIntegral

AliAzadehamp

Arm

inRa

himi-G

olkh

anda

ampMoh

senM

oghadd

am[5]

DEA

AliAziziamp

Bahram

Malekmoh

ammadi

ampHam

idRe

zaJafariamp

Hossein

Nasiri

ampVa

hidA

miniP

arsa[1]

ANPDem

atel

GIS

Yunn

aWuampShuaiGeng[6]

AHP

KazimBa

risAticiamp

Ahm

etBa

hadirSim

sekamp

Aydin

UlucanampMustafa

UmurTo

sun

[7]

MCD

AG

IS

Taeyun

Kim

ampJeon

g-II

Park

ampJunh

oMaeng

[2]

AHPGIS




















3 Methodology






X = s 120583x (s) vx (s) | s isin S (1)

where

120583x (s) 120583x (s) isin [0 1] S 997888rarr [0 1] (2)







X + Y











120582l = [120583119897 + 120587119897 (120583119897 (120583119897 + V119897))]sum119896119897=1 [120583119897 + 120587119897 (120583119897 (120583119897 + V119897))] (7)

where

120582l isin [0 1] and119896sum119897=1

120582l = 1 (8)




119897=1

(1 minus 120583(119897)119894119895 )120582l 119896prod119897=1

(V(119897)119894119895 )120582l 119896prod119897=1


(1 minus V(119897)119894119895 )120582l](9)






R = (rij)m1015840lowastn1015840 (10)

where


119897=1

(1 minus 120583(119897)119894119895 )120582l 119896prod119897=1

(V(119897)119894119895 )120582l 119896prod119897=1


(1 minus V(119897)119894119895 )120582l](11)








where

1205831015840lowast119895 = (max119894

1205831015840119894119895 119895 isin 1198691) (min1198941205831015840119894119895 119895 isin 1198692) (16)

v1015840lowast

119895 = (min119894V1015840119894119895 119895 isin 1198691) (max

119894V1015840119894119895 119895 isin 1198692) (17)

1205871015840lowast119895 = (1 minusmax119894

1205831015840119894119895 minusmin119894V1015840119894119895 119895 isin 1198691)


119894V1015840119894119895 119895 isin 1198692) (18)


1198941205831015840119894119895 119895 isin 1198692) (19)

v1015840minus

119895 = (max119894

V1015840119894119895 119895 isin 1198691) min119894V1015840119894119895 119895 isin 1198692 (20)


119894V1015840119894119895 119895 isin 1198691)

(1 minusmax119894

1205831015840119894119895 minusmin119894V1015840119894119895 119895 isin 1198692) (21)


119878119894lowast= radic 12119899

119899sum119895=1


119878119894minus= radic 12119899

119899sum119895=1





4 Case Study



















Wind Potential

(C1)

Location(C2)

Cost(C3)

SocialBenefits

(C4)



Occurrence

PowerDistribution

Network

Land Purchase

Cost

Initial and annual

maintenance


concernsEmployment

(A1) (A2) (A3) (A4)








Location















DM1



DM2



DM3






C1 (082016003)C2 (090010000)C3 (063032005)C4 (045050005)C5 (060034005)C6 (070024005)C7 (086013001)C8 (053042005)C9 (025071003)C10 (042053005)



001020304050607

CCi

Val

ues

Alternatives



A1

- Mer

sin

A2

ndash Ccedil

anak

kale

A4

- Sam

sun

3minus

İＴＧＣＬ




5 Conclusion



Table11R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(066025009)

(082007011)

(044050006)

(058034008)

(060034006)

(073016011)

(100000000)

(046050004)

(044050006)

(025060015

)A

2(100000000)

(100000000)

(078011011)

(100000000)

(100000000)

(044050006)

(070020010

)(080009011)

(068018013

)(055037009)

A3

(100000000)

(100000000)

(075013012

)(082007011)

(100000000)

(0410

52006)

(048050002)

(078011011)

(066025009)

(070020011)

A4

(078011011)

(058037005)

(0570

34009)

(043050007)

(063029008)

(078011011)

(100000000)

(100000000)

(035056009)

(053

040007)


Table12A


isticfuzzydecisio

nmatrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1(0540

03680092)

(074

1016

30097)

(027306600067)

(026206680070)

(036105660073)

(05160361012

3)(0863012

70010)

(024307090048)

(011308560031)

(010

408130083)

A2(0817015

80025)

(0900010

0000

0)(0486039

60118)

(045304960050)

(060503420053)

(030706210071)

(0604

03010094)

(04210470010

8)(017407640062)

(022807030069)

A3(0817015

80025)

(0900010

0000

0)(04710410012

0)(037

305310

096)

(060503420053)

(029206380070)

(041205630024)

(04090483010

8)(016

80783004

8)(029206230085)

A4(063402520114)

(052

204300049)

(035705510091)

(019

307480059)

(0380053

50085)

(0547032

80126)

(0863012

70010)

(052

704180055)

(008808740038)

(022107200059)


Table 13 A+

C1 (081701580025)C2 (090001000000)C3 (048603960118)C4 (045304960050)C5 (060503420053)C6 (054703280126)C7 (041205630024)C8 (024307090048)C9 (017407640062)C10 (029206230085)

Table 14 Aminus

C1 (054003680092)C2 (052204300049)C3 (027306600067)C4 (019307480059)C5 (036105660073)C6 (029206380070)C7 (086301270010)C8 (052704180055)C9 (008808740038)C10 (010408130083)



119862119862lowast119894

Mersin 02166 01466 04036

Canakkale 01342 02131 06135

Izmir 01136 02425 06811

Samsun 02409 01075 03086





Data Availability





Table16R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(461218049)

(724090090)

(284327044)

(349262044)

(538379032

)(42515

6041)

(697026052

)(313

335022)

(255294039

)(132

317079)

A2(728000000)

(905000000)

(491098065)

(556055044)

(854032063)

(270311041)

(54215

5077)

(537

067067)

(38214

7059)

(282211035

)A

3(655024049)

(859015030)

(48013

1044

)(524065065)

(901016032

)(238332052

)(361387026)

(50310

1067)

(37317

7039)

(36113

2035)

A4(54610

9073)

(513

362030)

(349262044)

(284327044)

(601285063)

(467093062)

(60714

2026)

(425224022)

(196333059)

(282211035)








119878minus

119862119862lowast119894




References


























Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in























3 Methodology






X = s 120583x (s) vx (s) | s isin S (1)

where

120583x (s) 120583x (s) isin [0 1] S 997888rarr [0 1] (2)







X + Y











120582l = [120583119897 + 120587119897 (120583119897 (120583119897 + V119897))]sum119896119897=1 [120583119897 + 120587119897 (120583119897 (120583119897 + V119897))] (7)

where

120582l isin [0 1] and119896sum119897=1

120582l = 1 (8)




119897=1

(1 minus 120583(119897)119894119895 )120582l 119896prod119897=1

(V(119897)119894119895 )120582l 119896prod119897=1


(1 minus V(119897)119894119895 )120582l](9)






R = (rij)m1015840lowastn1015840 (10)

where


119897=1

(1 minus 120583(119897)119894119895 )120582l 119896prod119897=1

(V(119897)119894119895 )120582l 119896prod119897=1


(1 minus V(119897)119894119895 )120582l](11)








where

1205831015840lowast119895 = (max119894

1205831015840119894119895 119895 isin 1198691) (min1198941205831015840119894119895 119895 isin 1198692) (16)

v1015840lowast

119895 = (min119894V1015840119894119895 119895 isin 1198691) (max

119894V1015840119894119895 119895 isin 1198692) (17)

1205871015840lowast119895 = (1 minusmax119894

1205831015840119894119895 minusmin119894V1015840119894119895 119895 isin 1198691)


119894V1015840119894119895 119895 isin 1198692) (18)


1198941205831015840119894119895 119895 isin 1198692) (19)

v1015840minus

119895 = (max119894

V1015840119894119895 119895 isin 1198691) min119894V1015840119894119895 119895 isin 1198692 (20)


119894V1015840119894119895 119895 isin 1198691)

(1 minusmax119894

1205831015840119894119895 minusmin119894V1015840119894119895 119895 isin 1198692) (21)


119878119894lowast= radic 12119899

119899sum119895=1


119878119894minus= radic 12119899

119899sum119895=1





4 Case Study



















Wind Potential

(C1)

Location(C2)

Cost(C3)

SocialBenefits

(C4)



Occurrence

PowerDistribution

Network

Land Purchase

Cost

Initial and annual

maintenance


concernsEmployment

(A1) (A2) (A3) (A4)








Location















DM1



DM2



DM3






C1 (082016003)C2 (090010000)C3 (063032005)C4 (045050005)C5 (060034005)C6 (070024005)C7 (086013001)C8 (053042005)C9 (025071003)C10 (042053005)



001020304050607

CCi

Val

ues

Alternatives



A1

- Mer

sin

A2

ndash Ccedil

anak

kale

A4

- Sam

sun

3minus

İＴＧＣＬ




5 Conclusion



Table11R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(066025009)

(082007011)

(044050006)

(058034008)

(060034006)

(073016011)

(100000000)

(046050004)

(044050006)

(025060015

)A

2(100000000)

(100000000)

(078011011)

(100000000)

(100000000)

(044050006)

(070020010

)(080009011)

(068018013

)(055037009)

A3

(100000000)

(100000000)

(075013012

)(082007011)

(100000000)

(0410

52006)

(048050002)

(078011011)

(066025009)

(070020011)

A4

(078011011)

(058037005)

(0570

34009)

(043050007)

(063029008)

(078011011)

(100000000)

(100000000)

(035056009)

(053

040007)


Table12A


isticfuzzydecisio

nmatrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1(0540

03680092)

(074

1016

30097)

(027306600067)

(026206680070)

(036105660073)

(05160361012

3)(0863012

70010)

(024307090048)

(011308560031)

(010

408130083)

A2(0817015

80025)

(0900010

0000

0)(0486039

60118)

(045304960050)

(060503420053)

(030706210071)

(0604

03010094)

(04210470010

8)(017407640062)

(022807030069)

A3(0817015

80025)

(0900010

0000

0)(04710410012

0)(037

305310

096)

(060503420053)

(029206380070)

(041205630024)

(04090483010

8)(016

80783004

8)(029206230085)

A4(063402520114)

(052

204300049)

(035705510091)

(019

307480059)

(0380053

50085)

(0547032

80126)

(0863012

70010)

(052

704180055)

(008808740038)

(022107200059)


Table 13 A+

C1 (081701580025)C2 (090001000000)C3 (048603960118)C4 (045304960050)C5 (060503420053)C6 (054703280126)C7 (041205630024)C8 (024307090048)C9 (017407640062)C10 (029206230085)

Table 14 Aminus

C1 (054003680092)C2 (052204300049)C3 (027306600067)C4 (019307480059)C5 (036105660073)C6 (029206380070)C7 (086301270010)C8 (052704180055)C9 (008808740038)C10 (010408130083)



119862119862lowast119894

Mersin 02166 01466 04036

Canakkale 01342 02131 06135

Izmir 01136 02425 06811

Samsun 02409 01075 03086





Data Availability





Table16R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(461218049)

(724090090)

(284327044)

(349262044)

(538379032

)(42515

6041)

(697026052

)(313

335022)

(255294039

)(132

317079)

A2(728000000)

(905000000)

(491098065)

(556055044)

(854032063)

(270311041)

(54215

5077)

(537

067067)

(38214

7059)

(282211035

)A

3(655024049)

(859015030)

(48013

1044

)(524065065)

(901016032

)(238332052

)(361387026)

(50310

1067)

(37317

7039)

(36113

2035)

A4(54610

9073)

(513

362030)

(349262044)

(284327044)

(601285063)

(467093062)

(60714

2026)

(425224022)

(196333059)

(282211035)








119878minus

119862119862lowast119894




References


























Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in











120582l = [120583119897 + 120587119897 (120583119897 (120583119897 + V119897))]sum119896119897=1 [120583119897 + 120587119897 (120583119897 (120583119897 + V119897))] (7)

where

120582l isin [0 1] and119896sum119897=1

120582l = 1 (8)




119897=1

(1 minus 120583(119897)119894119895 )120582l 119896prod119897=1

(V(119897)119894119895 )120582l 119896prod119897=1


(1 minus V(119897)119894119895 )120582l](9)






R = (rij)m1015840lowastn1015840 (10)

where


119897=1

(1 minus 120583(119897)119894119895 )120582l 119896prod119897=1

(V(119897)119894119895 )120582l 119896prod119897=1


(1 minus V(119897)119894119895 )120582l](11)








where

1205831015840lowast119895 = (max119894

1205831015840119894119895 119895 isin 1198691) (min1198941205831015840119894119895 119895 isin 1198692) (16)

v1015840lowast

119895 = (min119894V1015840119894119895 119895 isin 1198691) (max

119894V1015840119894119895 119895 isin 1198692) (17)

1205871015840lowast119895 = (1 minusmax119894

1205831015840119894119895 minusmin119894V1015840119894119895 119895 isin 1198691)


119894V1015840119894119895 119895 isin 1198692) (18)


1198941205831015840119894119895 119895 isin 1198692) (19)

v1015840minus

119895 = (max119894

V1015840119894119895 119895 isin 1198691) min119894V1015840119894119895 119895 isin 1198692 (20)


119894V1015840119894119895 119895 isin 1198691)

(1 minusmax119894

1205831015840119894119895 minusmin119894V1015840119894119895 119895 isin 1198692) (21)


119878119894lowast= radic 12119899

119899sum119895=1


119878119894minus= radic 12119899

119899sum119895=1





4 Case Study



















Wind Potential

(C1)

Location(C2)

Cost(C3)

SocialBenefits

(C4)



Occurrence

PowerDistribution

Network

Land Purchase

Cost

Initial and annual

maintenance


concernsEmployment

(A1) (A2) (A3) (A4)








Location















DM1



DM2



DM3






C1 (082016003)C2 (090010000)C3 (063032005)C4 (045050005)C5 (060034005)C6 (070024005)C7 (086013001)C8 (053042005)C9 (025071003)C10 (042053005)



001020304050607

CCi

Val

ues

Alternatives



A1

- Mer

sin

A2

ndash Ccedil

anak

kale

A4

- Sam

sun

3minus

İＴＧＣＬ




5 Conclusion



Table11R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(066025009)

(082007011)

(044050006)

(058034008)

(060034006)

(073016011)

(100000000)

(046050004)

(044050006)

(025060015

)A

2(100000000)

(100000000)

(078011011)

(100000000)

(100000000)

(044050006)

(070020010

)(080009011)

(068018013

)(055037009)

A3

(100000000)

(100000000)

(075013012

)(082007011)

(100000000)

(0410

52006)

(048050002)

(078011011)

(066025009)

(070020011)

A4

(078011011)

(058037005)

(0570

34009)

(043050007)

(063029008)

(078011011)

(100000000)

(100000000)

(035056009)

(053

040007)


Table12A


isticfuzzydecisio

nmatrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1(0540

03680092)

(074

1016

30097)

(027306600067)

(026206680070)

(036105660073)

(05160361012

3)(0863012

70010)

(024307090048)

(011308560031)

(010

408130083)

A2(0817015

80025)

(0900010

0000

0)(0486039

60118)

(045304960050)

(060503420053)

(030706210071)

(0604

03010094)

(04210470010

8)(017407640062)

(022807030069)

A3(0817015

80025)

(0900010

0000

0)(04710410012

0)(037

305310

096)

(060503420053)

(029206380070)

(041205630024)

(04090483010

8)(016

80783004

8)(029206230085)

A4(063402520114)

(052

204300049)

(035705510091)

(019

307480059)

(0380053

50085)

(0547032

80126)

(0863012

70010)

(052

704180055)

(008808740038)

(022107200059)


Table 13 A+

C1 (081701580025)C2 (090001000000)C3 (048603960118)C4 (045304960050)C5 (060503420053)C6 (054703280126)C7 (041205630024)C8 (024307090048)C9 (017407640062)C10 (029206230085)

Table 14 Aminus

C1 (054003680092)C2 (052204300049)C3 (027306600067)C4 (019307480059)C5 (036105660073)C6 (029206380070)C7 (086301270010)C8 (052704180055)C9 (008808740038)C10 (010408130083)



119862119862lowast119894

Mersin 02166 01466 04036

Canakkale 01342 02131 06135

Izmir 01136 02425 06811

Samsun 02409 01075 03086





Data Availability





Table16R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(461218049)

(724090090)

(284327044)

(349262044)

(538379032

)(42515

6041)

(697026052

)(313

335022)

(255294039

)(132

317079)

A2(728000000)

(905000000)

(491098065)

(556055044)

(854032063)

(270311041)

(54215

5077)

(537

067067)

(38214

7059)

(282211035

)A

3(655024049)

(859015030)

(48013

1044

)(524065065)

(901016032

)(238332052

)(361387026)

(50310

1067)

(37317

7039)

(36113

2035)

A4(54610

9073)

(513

362030)

(349262044)

(284327044)

(601285063)

(467093062)

(60714

2026)

(425224022)

(196333059)

(282211035)








119878minus

119862119862lowast119894




References


























Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in





where

1205831015840lowast119895 = (max119894

1205831015840119894119895 119895 isin 1198691) (min1198941205831015840119894119895 119895 isin 1198692) (16)

v1015840lowast

119895 = (min119894V1015840119894119895 119895 isin 1198691) (max

119894V1015840119894119895 119895 isin 1198692) (17)

1205871015840lowast119895 = (1 minusmax119894

1205831015840119894119895 minusmin119894V1015840119894119895 119895 isin 1198691)


119894V1015840119894119895 119895 isin 1198692) (18)


1198941205831015840119894119895 119895 isin 1198692) (19)

v1015840minus

119895 = (max119894

V1015840119894119895 119895 isin 1198691) min119894V1015840119894119895 119895 isin 1198692 (20)


119894V1015840119894119895 119895 isin 1198691)

(1 minusmax119894

1205831015840119894119895 minusmin119894V1015840119894119895 119895 isin 1198692) (21)


119878119894lowast= radic 12119899

119899sum119895=1


119878119894minus= radic 12119899

119899sum119895=1





4 Case Study



















Wind Potential

(C1)

Location(C2)

Cost(C3)

SocialBenefits

(C4)



Occurrence

PowerDistribution

Network

Land Purchase

Cost

Initial and annual

maintenance


concernsEmployment

(A1) (A2) (A3) (A4)








Location















DM1



DM2



DM3






C1 (082016003)C2 (090010000)C3 (063032005)C4 (045050005)C5 (060034005)C6 (070024005)C7 (086013001)C8 (053042005)C9 (025071003)C10 (042053005)



001020304050607

CCi

Val

ues

Alternatives



A1

- Mer

sin

A2

ndash Ccedil

anak

kale

A4

- Sam

sun

3minus

İＴＧＣＬ




5 Conclusion



Table11R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(066025009)

(082007011)

(044050006)

(058034008)

(060034006)

(073016011)

(100000000)

(046050004)

(044050006)

(025060015

)A

2(100000000)

(100000000)

(078011011)

(100000000)

(100000000)

(044050006)

(070020010

)(080009011)

(068018013

)(055037009)

A3

(100000000)

(100000000)

(075013012

)(082007011)

(100000000)

(0410

52006)

(048050002)

(078011011)

(066025009)

(070020011)

A4

(078011011)

(058037005)

(0570

34009)

(043050007)

(063029008)

(078011011)

(100000000)

(100000000)

(035056009)

(053

040007)


Table12A


isticfuzzydecisio

nmatrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1(0540

03680092)

(074

1016

30097)

(027306600067)

(026206680070)

(036105660073)

(05160361012

3)(0863012

70010)

(024307090048)

(011308560031)

(010

408130083)

A2(0817015

80025)

(0900010

0000

0)(0486039

60118)

(045304960050)

(060503420053)

(030706210071)

(0604

03010094)

(04210470010

8)(017407640062)

(022807030069)

A3(0817015

80025)

(0900010

0000

0)(04710410012

0)(037

305310

096)

(060503420053)

(029206380070)

(041205630024)

(04090483010

8)(016

80783004

8)(029206230085)

A4(063402520114)

(052

204300049)

(035705510091)

(019

307480059)

(0380053

50085)

(0547032

80126)

(0863012

70010)

(052

704180055)

(008808740038)

(022107200059)


Table 13 A+

C1 (081701580025)C2 (090001000000)C3 (048603960118)C4 (045304960050)C5 (060503420053)C6 (054703280126)C7 (041205630024)C8 (024307090048)C9 (017407640062)C10 (029206230085)

Table 14 Aminus

C1 (054003680092)C2 (052204300049)C3 (027306600067)C4 (019307480059)C5 (036105660073)C6 (029206380070)C7 (086301270010)C8 (052704180055)C9 (008808740038)C10 (010408130083)



119862119862lowast119894

Mersin 02166 01466 04036

Canakkale 01342 02131 06135

Izmir 01136 02425 06811

Samsun 02409 01075 03086





Data Availability





Table16R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(461218049)

(724090090)

(284327044)

(349262044)

(538379032

)(42515

6041)

(697026052

)(313

335022)

(255294039

)(132

317079)

A2(728000000)

(905000000)

(491098065)

(556055044)

(854032063)

(270311041)

(54215

5077)

(537

067067)

(38214

7059)

(282211035

)A

3(655024049)

(859015030)

(48013

1044

)(524065065)

(901016032

)(238332052

)(361387026)

(50310

1067)

(37317

7039)

(36113

2035)

A4(54610

9073)

(513

362030)

(349262044)

(284327044)

(601285063)

(467093062)

(60714

2026)

(425224022)

(196333059)

(282211035)








119878minus

119862119862lowast119894




References


























Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in






Wind Potential

(C1)

Location(C2)

Cost(C3)

SocialBenefits

(C4)



Occurrence

PowerDistribution

Network

Land Purchase

Cost

Initial and annual

maintenance


concernsEmployment

(A1) (A2) (A3) (A4)








Location















DM1



DM2



DM3






C1 (082016003)C2 (090010000)C3 (063032005)C4 (045050005)C5 (060034005)C6 (070024005)C7 (086013001)C8 (053042005)C9 (025071003)C10 (042053005)



001020304050607

CCi

Val

ues

Alternatives



A1

- Mer

sin

A2

ndash Ccedil

anak

kale

A4

- Sam

sun

3minus

İＴＧＣＬ




5 Conclusion



Table11R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(066025009)

(082007011)

(044050006)

(058034008)

(060034006)

(073016011)

(100000000)

(046050004)

(044050006)

(025060015

)A

2(100000000)

(100000000)

(078011011)

(100000000)

(100000000)

(044050006)

(070020010

)(080009011)

(068018013

)(055037009)

A3

(100000000)

(100000000)

(075013012

)(082007011)

(100000000)

(0410

52006)

(048050002)

(078011011)

(066025009)

(070020011)

A4

(078011011)

(058037005)

(0570

34009)

(043050007)

(063029008)

(078011011)

(100000000)

(100000000)

(035056009)

(053

040007)


Table12A


isticfuzzydecisio

nmatrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1(0540

03680092)

(074

1016

30097)

(027306600067)

(026206680070)

(036105660073)

(05160361012

3)(0863012

70010)

(024307090048)

(011308560031)

(010

408130083)

A2(0817015

80025)

(0900010

0000

0)(0486039

60118)

(045304960050)

(060503420053)

(030706210071)

(0604

03010094)

(04210470010

8)(017407640062)

(022807030069)

A3(0817015

80025)

(0900010

0000

0)(04710410012

0)(037

305310

096)

(060503420053)

(029206380070)

(041205630024)

(04090483010

8)(016

80783004

8)(029206230085)

A4(063402520114)

(052

204300049)

(035705510091)

(019

307480059)

(0380053

50085)

(0547032

80126)

(0863012

70010)

(052

704180055)

(008808740038)

(022107200059)


Table 13 A+

C1 (081701580025)C2 (090001000000)C3 (048603960118)C4 (045304960050)C5 (060503420053)C6 (054703280126)C7 (041205630024)C8 (024307090048)C9 (017407640062)C10 (029206230085)

Table 14 Aminus

C1 (054003680092)C2 (052204300049)C3 (027306600067)C4 (019307480059)C5 (036105660073)C6 (029206380070)C7 (086301270010)C8 (052704180055)C9 (008808740038)C10 (010408130083)



119862119862lowast119894

Mersin 02166 01466 04036

Canakkale 01342 02131 06135

Izmir 01136 02425 06811

Samsun 02409 01075 03086





Data Availability





Table16R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(461218049)

(724090090)

(284327044)

(349262044)

(538379032

)(42515

6041)

(697026052

)(313

335022)

(255294039

)(132

317079)

A2(728000000)

(905000000)

(491098065)

(556055044)

(854032063)

(270311041)

(54215

5077)

(537

067067)

(38214

7059)

(282211035

)A

3(655024049)

(859015030)

(48013

1044

)(524065065)

(901016032

)(238332052

)(361387026)

(50310

1067)

(37317

7039)

(36113

2035)

A4(54610

9073)

(513

362030)

(349262044)

(284327044)

(601285063)

(467093062)

(60714

2026)

(425224022)

(196333059)

(282211035)








119878minus

119862119862lowast119894




References


























Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in







DM1



DM2



DM3






C1 (082016003)C2 (090010000)C3 (063032005)C4 (045050005)C5 (060034005)C6 (070024005)C7 (086013001)C8 (053042005)C9 (025071003)C10 (042053005)



001020304050607

CCi

Val

ues

Alternatives



A1

- Mer

sin

A2

ndash Ccedil

anak

kale

A4

- Sam

sun

3minus

İＴＧＣＬ




5 Conclusion



Table11R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(066025009)

(082007011)

(044050006)

(058034008)

(060034006)

(073016011)

(100000000)

(046050004)

(044050006)

(025060015

)A

2(100000000)

(100000000)

(078011011)

(100000000)

(100000000)

(044050006)

(070020010

)(080009011)

(068018013

)(055037009)

A3

(100000000)

(100000000)

(075013012

)(082007011)

(100000000)

(0410

52006)

(048050002)

(078011011)

(066025009)

(070020011)

A4

(078011011)

(058037005)

(0570

34009)

(043050007)

(063029008)

(078011011)

(100000000)

(100000000)

(035056009)

(053

040007)


Table12A


isticfuzzydecisio

nmatrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1(0540

03680092)

(074

1016

30097)

(027306600067)

(026206680070)

(036105660073)

(05160361012

3)(0863012

70010)

(024307090048)

(011308560031)

(010

408130083)

A2(0817015

80025)

(0900010

0000

0)(0486039

60118)

(045304960050)

(060503420053)

(030706210071)

(0604

03010094)

(04210470010

8)(017407640062)

(022807030069)

A3(0817015

80025)

(0900010

0000

0)(04710410012

0)(037

305310

096)

(060503420053)

(029206380070)

(041205630024)

(04090483010

8)(016

80783004

8)(029206230085)

A4(063402520114)

(052

204300049)

(035705510091)

(019

307480059)

(0380053

50085)

(0547032

80126)

(0863012

70010)

(052

704180055)

(008808740038)

(022107200059)


Table 13 A+

C1 (081701580025)C2 (090001000000)C3 (048603960118)C4 (045304960050)C5 (060503420053)C6 (054703280126)C7 (041205630024)C8 (024307090048)C9 (017407640062)C10 (029206230085)

Table 14 Aminus

C1 (054003680092)C2 (052204300049)C3 (027306600067)C4 (019307480059)C5 (036105660073)C6 (029206380070)C7 (086301270010)C8 (052704180055)C9 (008808740038)C10 (010408130083)



119862119862lowast119894

Mersin 02166 01466 04036

Canakkale 01342 02131 06135

Izmir 01136 02425 06811

Samsun 02409 01075 03086





Data Availability





Table16R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(461218049)

(724090090)

(284327044)

(349262044)

(538379032

)(42515

6041)

(697026052

)(313

335022)

(255294039

)(132

317079)

A2(728000000)

(905000000)

(491098065)

(556055044)

(854032063)

(270311041)

(54215

5077)

(537

067067)

(38214

7059)

(282211035

)A

3(655024049)

(859015030)

(48013

1044

)(524065065)

(901016032

)(238332052

)(361387026)

(50310

1067)

(37317

7039)

(36113

2035)

A4(54610

9073)

(513

362030)

(349262044)

(284327044)

(601285063)

(467093062)

(60714

2026)

(425224022)

(196333059)

(282211035)








119878minus

119862119862lowast119894




References


























Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in





Table11R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(066025009)

(082007011)

(044050006)

(058034008)

(060034006)

(073016011)

(100000000)

(046050004)

(044050006)

(025060015

)A

2(100000000)

(100000000)

(078011011)

(100000000)

(100000000)

(044050006)

(070020010

)(080009011)

(068018013

)(055037009)

A3

(100000000)

(100000000)

(075013012

)(082007011)

(100000000)

(0410

52006)

(048050002)

(078011011)

(066025009)

(070020011)

A4

(078011011)

(058037005)

(0570

34009)

(043050007)

(063029008)

(078011011)

(100000000)

(100000000)

(035056009)

(053

040007)


Table12A


isticfuzzydecisio

nmatrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1(0540

03680092)

(074

1016

30097)

(027306600067)

(026206680070)

(036105660073)

(05160361012

3)(0863012

70010)

(024307090048)

(011308560031)

(010

408130083)

A2(0817015

80025)

(0900010

0000

0)(0486039

60118)

(045304960050)

(060503420053)

(030706210071)

(0604

03010094)

(04210470010

8)(017407640062)

(022807030069)

A3(0817015

80025)

(0900010

0000

0)(04710410012

0)(037

305310

096)

(060503420053)

(029206380070)

(041205630024)

(04090483010

8)(016

80783004

8)(029206230085)

A4(063402520114)

(052

204300049)

(035705510091)

(019

307480059)

(0380053

50085)

(0547032

80126)

(0863012

70010)

(052

704180055)

(008808740038)

(022107200059)


Table 13 A+

C1 (081701580025)C2 (090001000000)C3 (048603960118)C4 (045304960050)C5 (060503420053)C6 (054703280126)C7 (041205630024)C8 (024307090048)C9 (017407640062)C10 (029206230085)

Table 14 Aminus

C1 (054003680092)C2 (052204300049)C3 (027306600067)C4 (019307480059)C5 (036105660073)C6 (029206380070)C7 (086301270010)C8 (052704180055)C9 (008808740038)C10 (010408130083)



119862119862lowast119894

Mersin 02166 01466 04036

Canakkale 01342 02131 06135

Izmir 01136 02425 06811

Samsun 02409 01075 03086





Data Availability





Table16R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(461218049)

(724090090)

(284327044)

(349262044)

(538379032

)(42515

6041)

(697026052

)(313

335022)

(255294039

)(132

317079)

A2(728000000)

(905000000)

(491098065)

(556055044)

(854032063)

(270311041)

(54215

5077)

(537

067067)

(38214

7059)

(282211035

)A

3(655024049)

(859015030)

(48013

1044

)(524065065)

(901016032

)(238332052

)(361387026)

(50310

1067)

(37317

7039)

(36113

2035)

A4(54610

9073)

(513

362030)

(349262044)

(284327044)

(601285063)

(467093062)

(60714

2026)

(425224022)

(196333059)

(282211035)








119878minus

119862119862lowast119894




References


























Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in





Table12A


isticfuzzydecisio

nmatrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1(0540

03680092)

(074

1016

30097)

(027306600067)

(026206680070)

(036105660073)

(05160361012

3)(0863012

70010)

(024307090048)

(011308560031)

(010

408130083)

A2(0817015

80025)

(0900010

0000

0)(0486039

60118)

(045304960050)

(060503420053)

(030706210071)

(0604

03010094)

(04210470010

8)(017407640062)

(022807030069)

A3(0817015

80025)

(0900010

0000

0)(04710410012

0)(037

305310

096)

(060503420053)

(029206380070)

(041205630024)

(04090483010

8)(016

80783004

8)(029206230085)

A4(063402520114)

(052

204300049)

(035705510091)

(019

307480059)

(0380053

50085)

(0547032

80126)

(0863012

70010)

(052

704180055)

(008808740038)

(022107200059)


Table 13 A+

C1 (081701580025)C2 (090001000000)C3 (048603960118)C4 (045304960050)C5 (060503420053)C6 (054703280126)C7 (041205630024)C8 (024307090048)C9 (017407640062)C10 (029206230085)

Table 14 Aminus

C1 (054003680092)C2 (052204300049)C3 (027306600067)C4 (019307480059)C5 (036105660073)C6 (029206380070)C7 (086301270010)C8 (052704180055)C9 (008808740038)C10 (010408130083)



119862119862lowast119894

Mersin 02166 01466 04036

Canakkale 01342 02131 06135

Izmir 01136 02425 06811

Samsun 02409 01075 03086





Data Availability





Table16R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(461218049)

(724090090)

(284327044)

(349262044)

(538379032

)(42515

6041)

(697026052

)(313

335022)

(255294039

)(132

317079)

A2(728000000)

(905000000)

(491098065)

(556055044)

(854032063)

(270311041)

(54215

5077)

(537

067067)

(38214

7059)

(282211035

)A

3(655024049)

(859015030)

(48013

1044

)(524065065)

(901016032

)(238332052

)(361387026)

(50310

1067)

(37317

7039)

(36113

2035)

A4(54610

9073)

(513

362030)

(349262044)

(284327044)

(601285063)

(467093062)

(60714

2026)

(425224022)

(196333059)

(282211035)








119878minus

119862119862lowast119894




References


























Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in





Table 13 A+

C1 (081701580025)C2 (090001000000)C3 (048603960118)C4 (045304960050)C5 (060503420053)C6 (054703280126)C7 (041205630024)C8 (024307090048)C9 (017407640062)C10 (029206230085)

Table 14 Aminus

C1 (054003680092)C2 (052204300049)C3 (027306600067)C4 (019307480059)C5 (036105660073)C6 (029206380070)C7 (086301270010)C8 (052704180055)C9 (008808740038)C10 (010408130083)



119862119862lowast119894

Mersin 02166 01466 04036

Canakkale 01342 02131 06135

Izmir 01136 02425 06811

Samsun 02409 01075 03086





Data Availability





Table16R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(461218049)

(724090090)

(284327044)

(349262044)

(538379032

)(42515

6041)

(697026052

)(313

335022)

(255294039

)(132

317079)

A2(728000000)

(905000000)

(491098065)

(556055044)

(854032063)

(270311041)

(54215

5077)

(537

067067)

(38214

7059)

(282211035

)A

3(655024049)

(859015030)

(48013

1044

)(524065065)

(901016032

)(238332052

)(361387026)

(50310

1067)

(37317

7039)

(36113

2035)

A4(54610

9073)

(513

362030)

(349262044)

(284327044)

(601285063)

(467093062)

(60714

2026)

(425224022)

(196333059)

(282211035)








119878minus

119862119862lowast119894




References


























Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in





Table16R

matrix

C1

C2C3

C4C5

C6C7

C8C9

C10

A1

(461218049)

(724090090)

(284327044)

(349262044)

(538379032

)(42515

6041)

(697026052

)(313

335022)

(255294039

)(132

317079)

A2(728000000)

(905000000)

(491098065)

(556055044)

(854032063)

(270311041)

(54215

5077)

(537

067067)

(38214

7059)

(282211035

)A

3(655024049)

(859015030)

(48013

1044

)(524065065)

(901016032

)(238332052

)(361387026)

(50310

1067)

(37317

7039)

(36113

2035)

A4(54610

9073)

(513

362030)

(349262044)

(284327044)

(601285063)

(467093062)

(60714

2026)

(425224022)

(196333059)

(282211035)








119878minus

119862119862lowast119894




References


























Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in











119878minus

119862119862lowast119894




References


























Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in

















Advances in

FuzzySystems


Volume 2018













Journal of

Journal of



Hindawi


Advances in

Multimedia


Biomedical Imaging





RoboticsJournal of









Volume 2018



Advances in




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