The Cruise Port Place Selection Problem with Extended VIKOR and ANP Methodologies under Fuzzy Environment

Transcription

1 Proceedngs of the World Congress on Engneerng 2011 Vol II, July 6-8, 2011, London, U.K. The Cruse Port Place Selecton Problem wth Extended VIKOR and ANP Methodologes under Fuzzy Envronment Nhan ÇETİN DEMİREL, and G.Nlay YÜCENUR Abstract In ths paper, a decson mang problem s consdered and fuzzy analytc networ process (ANP) and extended fuzzy VIKOR methodologes have been proposed to deal wth the cruse port place selecton problem n Istanbul. In ths paper, we proposed a model for selecton of the most sutable place by usng analytcal networ process and VIKOR method under fuzzy envronment due to lngustc terms. The mplementaton of the system s demonstrated by a problem havng four stages of herarchy whch contans four crtera and thrteen sub-crtera. The study compares the fuzzy analytc networ process and VIKOR method results. Index Terms Mult-crtera decson mang problem, Fuzzy ANP, extended VIKOR method, cruse port I. INTRODUCTION RUISE toursm s one of the fastest growng sectors of Cglobal toursm. Cruse toursm s the quntessental footloose ndustry. Cruse shps are constantly beng moved between marets from one year to the next and even wthn a gven calendar year thus mang ndvdual cruse marets hghly contestable [1]. The harbor transformaton and redevelopment actvtes are mostly seen n ndustrally advanced countres, such as the US, Canada, some of the European countres, Japan and Australa. However, as ther economes grow rapdly, the newly ndustralzng countres are also affected by ths worldwde phenomenon. As a mddle-ncome developng country, the Republc of Turey has also begun to face a smlar phenomenon n some of ts harbors. Among them, Karaöy Harbor, one of the nner cty harbors n the Istanbul harbor system, has been undergong a transformaton snce the md-1980s. Ths change mght have mportant consequences for the harbor tself as well as for Istanbul cty [2]. The selecton of the cruse port place s a very mportant and complex problem. The decson maers have to consder all crtera whch have sgnfcant effects on the economy, envronment, human lfe, and socety. In ths paper, ANP and extended VIKOR methodologes are used under fuzzy envronment and the results are compared. The mplementaton of the system s demonstrated by a problem Manuscrpt receved March 01, 2011; revsed March 22, N. Çetn Demrel, Yıldız Techncal Unversty, Mechancal Faculty, Industral Engneerng Department, YTU Beştaş, İstanbul, Turey, Correspondng author to provde phone: +90(212)82868, e-mal: nhan@yldz.edu.tr). G.N. Yücenur, Yıldız Techncal Unversty, Mechancal Faculty, Industral Engneerng Department, YTU Beştaş, İstanbul, Turey, Correspondng author to provde phone: +90(212)82921, e-mal: nserbest@yldz.edu.tr). ISSN: (Prnt); ISSN: (Onlne) havng four stages of herarchy whch contans four crtera and thrteen sub-crtera. Mult-crtera decson mang nvolves determnng the optmal alternatve among multple, conflctng, and nteractve crtera [, 4]. The ANP s fundamentally a way to measure ntangble factors by usng par wse comparsons wth udgments that represent the domnance of one element over another wth respect to a property that they share [5]. Analytc networ process (ANP) s a new tool for mult-crtera decson mang but can also be appled n academc research to prortze factors or crtera. It enhances the functon of analytc herarchy process (AHP) to develop a complete model that can ncorporate nterdependent relatonshps between elements from dfferent levels or wthn levels, whch are assumed to be uncorrelated n AHP [6]. On the other hand, the VIKOR method was developed to solve mult-crtera decson mang problems wth conflctng and non-commensurable (dfferent unts) crtera, assumng that compromsng s acceptable for conflct resoluton, the decson maer wants a soluton that s the closest to the deal, and the alternatves are evaluated accordng to all establshed crtera. Ths method focuses on ranng and selectng from a set of alternatves n the presence of conflctng crtera, and on proposng compromse soluton (one or more) [7]. In the lterature, Lou et al. (2010) used a modfed VIKOR method for mprovng the domestc arlnes servce qualty and Chang and Hsu (2009) used VIKOR method for prortzng land-use restrant strateges n the Tseng-Wen reservor watershed [8, 9]. Sayad et al. (2009) used extenson VIKOR method for the soluton of the decson mang problem wth nterval numbers [10]. Zadeh (1965) ntroduced the fuzzy set theory to deal wth the uncertanty due to mprecson and vagueness. A maor contrbuton of fuzzy set theory s ts capablty of representng vague data. The theory also allows mathematcal operators and programmng apply to the fuzzy doman [11]. The remander of ths paper s organzed as follows. Secton 2 brefly ntroduces sea toursm commerce and cruse tours. Secton goes over descrpton of the proposed model and selecton crtera of the problem. Secton 4 and Secton 5 contan applcatons of fuzzy ANP method and extended fuzzy VIKOR method n a sutable cruse port place selecton, respectvely. Some concluson remars are made n Secton 6.

2 Proceedngs of the World Congress on Engneerng 2011 Vol II, July 6-8, 2011, London, U.K. II. SEA AND CRUISE TOURISM Toursm has been accepted as an alternatve economc development strategy by many governments n developng countres. Turey as a developng country s not exceptonal n ths case. After experencng three mltary coups caused by socal unrest and serous economc crses, Turey adopted toursm not only as an alternatve economc growth strategy, but also as a tool for socal change to poltcal strategy to create a favorable mage n the eyes of European people [12]. Water has been an attracton n establshng and developng ctes all around the world. The sgnfcance of water was renforced wth the ndustral revoluton. Wth ncreasng tradng actvtes, harbors became mportant propertes for ctes. However, today, across the world ndustral actvtes and trade are n dramatc change. As a reflecton of these changes, ndustral actvtes relocated ether to suburbs or to another country, leavng nner cty economc actvtes n declne. Addtonally, the technologcal developments n transportaton, shpbuldng and handlng, such as contaner transportaton and super capacty vessels resulted n outmoded facltes. All these developments have changed the comparatve advantages of many harbors. As a result, harbors all around the world have been undergong changes as an effect of the postndustral era wth most emphass of maret-led polces and ntatves [2]. Cruse toursm has certan features [12]: Be located some dstance away from potental vstors Be seen as a potental destnaton area Have reasonable accessblty to a potental maret Have some mnmum level of economc and socal nfrastructure that can support toursm development Be large enough to contan more than ust one communty. In Turey, n Istanbul there s one actve cruse port n Karaöy. Karaöy Harbor area s located at the European sde of Istanbul, where the hstorcal core of the cty surrounds t. The man functons of the surroundng area are fnance, wholesale and retal trade, small worshops, offce and resdental use. Along wth the harbor, the Mmar Snan Unversty campus s also an mportant educaton and fne arts nsttuton n the area. Wth the becomng of the cruse toursm popular, Karaöy s not enough to accompany of all cruses overall the world. That s why the new port place s very bg problem n Istanbul. Ths paper tres to fnd a new soluton of ths problem. III. SELECTION CRITERIA FOR CRUISE PORT PLACE The problem has a herarchy wth four levels, and the dfferent decson crtera, sub-crtera and the decson alternatves, wll be further dscussed. In herarchy, the overall obectve s placed at level 1, crtera at level 2, attrbutes at level, and the decson alternatves at level 4. The man obectve here s the selecton of the most sutable cruse port place n Istanbul. The crtera whch are consdered here n selecton of the cty are strategc, techncal, economc and socal condtons. These crtera can be decomposed nto varous other attrbutes. The man four crtera and numbers of sub-crtera relevant to selecton are descrbed below. The crtera are denoted by C, sub-crtera by SC, and alternatve places by AP (where,, = 1, 2,, 4, 5 and = 1, 2,, 28). The herarchy of the selecton crtera, sub-crtera and decson alternatves can be seen n Fgure 1. The crtera, and attrbutes dentfed and analyzed n ths paper can be seen n the lterature and the government s researches. Strategc condtons (C 1 ): The factors affectng strategc condtons crtera can be stated as follows: nearlness of the tourstc places (SC 1 ), nearlness of the toursm maret (SC 2 ) and expanson possblty (SC ). Techncal condtons (C 2 ): The factors whch affect the techncal condtons can be stated as follows: meteorologcal Fg. 1. The herarchy of the selecton problem. characterstcs (SC 4 ), water depth (SC 5 ), doc convenence (SC 6 ), wave effects (SC 7 ) and status of coast lne (SC 8 ). Economc condtons (C ): The economc condtons crtera have the followng attrbutes: nvestment cost (SC 9 ), operatng cost (SC 10 ) and advertsng cost (SC 11 ). Socal condtons (C 4 ): The factors affectng the socal condtons can be stated as follows: means of transport (SC 12 ) and educatonal status (SC 1 ). Alternatve cruse port places n Istanbul: The abovementoned crtera and sub-crtera help n decdng the cruse port place n Istanbul. In ths paper, dependng on the selecton crtera and sub-crtera there are four dfferent alternatve places such as Haydarpaşa (AP 1 ), Tuzla (AP 2 ), Sarayburnu (AP ) and (AP 4 ). IV. APPLICATION OF THE FUZZY ANP IN THE CRUISE PORT PLACE SELECTION PROBLEM Analytc networ process (ANP) s a new tool for multcrtera decson-mang (MCDM) but can also be appled n academc research to prortze factors or crtera and t s the generc form of AHP and allows for more complex nterdependent relatonshps among elements [6]. The advantages of the ANP are that t s not only approprate for both quanttatve and qualtatve data types, but t also can overcome the problem of nterdependence and feedbac among crtera. Although the ANP have been wdely used n varous applcatons, two man problems should be hghlghted as follows. The frst s the problem of comparson. In the ANP, the decson maer s ased to answer the queston le How much mportance does a crteron have compared wth respect to our nterests or preferences? However, sometmes the questons are hard even for the expert to answer the queston above due to ISSN: (Prnt); ISSN: (Onlne)

3 Proceedngs of the World Congress on Engneerng 2011 Vol II, July 6-8, 2011, London, U.K. some questons are ant-ntutve. Furthermore, the ey for the ANP s to determne the relatonshp structure among features n advance. The dfferent structure results n the dfferent prortes. However, t s usually hard for the decson maer to gve the true relatonshp structure by consderng many crtera []. The method of the ANP can be descrbed as follows: Step 1: The crtera and attrbutes are dentfed. Step 2: Local weghts of the crtera and attrbutes whch tae part n the second and thrd levels of ANP model same AHP model, provded n Fgure 1, are calculated. Par wse comparson matrces are analyzed by the Chang s extend analyss method and local weghts are determned. TABLE I COMBINATION OF CRITERIA OF THE GOAL Strategc Techncal Economc Socal Step : In ths step, nterdependent weghts of the factors are calculated and the dependences among the factors are consdered. The par wse comparson matrces are formed for the factors. The weght vector for strategc condtons s calculated as W 1 = (0.47, 0.47, 0.06) T, for techncal condtons s calculated as W 2 = (0.16, 0.81, 0.0) T, for economc condtons s calculated as W = (0.81, 0.16, 0.0) T and for socal condtons s calculated as W 4 = (0.06, 0.47, 0.47) T. Step 4: Usng the computed relatve mportance weghts, the dependence matrx of the crtera s formed. Interdependent weghts of the crtera are computed by multplyng the dependence matrx of the crtera we obtaned wth the local weghts of factors. The nterdependent weghts of the factors are calculated as follows: strategccondtons 1.00 techncalcondtons = 0.47 economccondtons 0.47 socalcondtons Alternatve Prorty Weght Cond. Cond. Con. Cond. Weght Alternatve Strateges Haydarpaşa Tuzla Sarayburnu = Step 5: Usng nterdependent weghts of the crtera and local weghts of the attrbutes, global weghts for the attrbutes are calculated n ths step. Global attrbutes weghts are computed by multplyng local weght of the attrbutes wth the nterdependent weght of the crtera to whch t belongs. The values are shown n Table II. Fnally, the overall prortes of the alternatve places, reflectng the nterrelatonshps wthn the crtera, are calculated. The herarchcal model gven n Fgure 1 was analyzed wth the Fuzzy ANP. Accordng to the ANP analyss, alternatve cruse port places are ordered as, Haydarpaşa, Tuzla and Sarayburnu. TABLE II COMPUTED GLOBAL WEIGHTS OF ATTRIBUTES Factors and local weghts Local weghts Global weghts Strategc condtons 0.26 Nearlness of the tourstc places Nearlness of the toursm maret Expanson possblty Techncal condtons Meteorologcal characterstcs Water depth Doc convenence Wave effects Status of coast lne Economc condtons 0.42 Investment cost Operatng cost Advertsng cost Socal condtons 0.04 Means of transport Educatonal status Weght Global 0.22 vectors weght = 0.21 for of 0.21 attrbutes attrbutes 0.6 V. APPLICATION OF THE EXTENDED VIKOR METHOD UNDER FUZZY ENVIRONMENT IN THE MOST SUITABLE CITY SELECTION PROBLEM Mult-crtera decson mang problems are usually under uncertanty. One of these uncertan parameters s the decson maer (DM) s degree of optmsm, whch has an mportant effect on the results. Fuzzy lngustc quantfers are used to obtan the assessments of ths parameter from DM and then, because of ts uncertanty t s assumed to have stochastc nature [1]. VIKOR was developed by Oprcovc (1998) and Oprcovc and Tzeng (2002) wth the Serban name: VlseKrterumsa Optmzaca I Kompromsno Resene, means mult-crtera optmzaton and compromse soluton. The VIKOR method was developed for mult-crtera optmzaton of complex systems and ths method focuses on ranng and selectng from a set of alternatves, and determnes compromse solutons for a problem wth conflctng crtera, whch can help the decson maers to reach a fnal decson. Here, the compromse soluton s a feasble soluton whch s the closest to the deal, and a compromse means an agreement establshed by mutual concessons. It ntroduces the mult-crtera ranng ndex based on the partcular measure of closeness to the deal soluton [14]. In ths paper the problem s evaluated under fuzzy envronment wth fuzzy sets. The man steps of the algorthm are taen from Sanaye et al. s (2010) study [14]: Step 1: Identfyng the obectves of the decson mang process and defne the problem scope. Step 2: Arrangng the decson mang group and defne and descrbe a fnte set of relevant attrbutes. Step : Identfyng the approprate lngustc varables: In ths step, the approprate lngustc varables for the mportance weght of crtera, and the fuzzy ratng for alternatves wth regard to each sub-crteron these lngustc varables can be expressed n postve trapezodal fuzzy numbers. ISSN: (Prnt); ISSN: (Onlne)

4 Proceedngs of the World Congress on Engneerng 2011 Vol II, July 6-8, 2011, London, U.K. Step 4: Pull the decson maers opnons to get the aggregated fuzzy weght of crtera, and aggregated fuzzy ratng of alternatves and construct a fuzzy decson matrx: Let the fuzzy ratng and mportance weght of the th x = x, x, x, x and decson maer be ( 1 2 4) w = ( w1, w 2, w, w 4) ; = 1, 2,, m and = 1, 2,, n respectvely. Hence, the aggregated fuzzy ratngs ( x ) of alternatves wth respect to each crteron can be calculated as: x = ( x1, x 2, x, x 4), (1) x where x1 mn{ x1} = max =, x { x } K 2 x 2 =, x = x, 1 K The aggregated fuzzy weghts ( w ) of each crteron can be calculated as: w = w, w, w, w, (2) w ( 1 2 4) =, 1 K w2 w 2 where w1 mn{ w1} = max { w } K =, w = w, A sutable nsurance frm selecton problem can be concsely expressed n matrx format as follows: x 11 x 12 x 1 n x 12 x 22 x 2n D =, W = [ w 1, w 2,, w ], n x m 1 x m 2 x mn where x the ratng of alternatve A wth respect to C, w the mportance weght of the th crteron holds, x = ( x1, x 2, x, x 4) and w = ( w1, w2, w, w4) ; = 1, 2,, m and = 1, 2,, n are lngustc varables can be approxmated by postve trapezodal fuzzy numbers. Step 5: Defuzzfy the fuzzy decson matrx and fuzzy weght of each crteron nto crsp values: Ths calculaton s done by usng center of area defuzzfcaton method. Step 6: Determne the best f and the worst f values of all crteron ratngs, = 1, 2,, n f = max x () f = mn x (4) Step 7: Compute the values S and R by the relatons n S = w ( f f ) / ( f f ) (5) = 1 ( ) ( ) R = max w f f / f f (6) Step 8: Compute the values Q by the relatons Q = v S S / S S + 1 v R R / R R (7) ( ) ( ) ( )( ) ( ) where S = mn S, max S = S, R = mn R, R = max R and v s ntroduced as a weght for the strategy of maxmum group utlty, whereas 1-v s the weght of the ndvdual regret. Step 9: Ran the alternatves, sortng by the values S, R and Q n ascendng order. Step 10: Propose as a compromse soluton the alternatve ( 1) ( A ) whch s the best raned by the measure Q (mnmum) f the followng two condtons are satsfed C1. Acceptable advantage: ( 2) 1 Q A Q A DQ, (8) ( ) ( ) ( ) where A (2) s the alternatve wth second poston n the ranng lst by Q; DQ = 1 / (J 1). C2. Acceptable stablty n decson mang: The alternatve A (1) must also be the best raned by S or/and R. Ths compromse soluton s stable wthn a decson mang process, whch could be the strategy of maxmum group utlty (when v > 0.5 s needed), or by consensus v 0.5, or wth veto (v < 0.5). Here, v s the weght of decson mang strategy of maxmum group utlty. If one of the condtons s not satsfed, then a set of compromse solutons s proposed, whch consst of: Alternatves A (1) and A (2) f only the condtons C2 s not satsfed or Alternatves A (1), A (2),, A (M) f the condton C1 s not satsfed; A (M) s determned by the relaton Q(A (M) ) Q(A (1) ) < DQ for maxmum M (the postons of these alternatves are n closeness ). As an applcaton, the proposed model has been appled to select the cruse port place n Istanbul. The steps of the soluton process can be defned as n follows: Four alternatve places (Haydarpaşa, Tuzla,, Sarayburnu) A commttee of three decson maers, D1, D2 and D, has been formed to select the most sutable place wth the crtera. The research model shown n Fgure 1 s used. Three decson maers use the lngustc weghtng varables to assess the mportance of the crtera. The mportance weghts of the crtera determned by these three decson maers are shown n Table III. Also the decson maers use the lngustc ratng varables to evaluate the ratngs of canddates wth respect to each crteron. The ratngs of the four place alternatves by the decson maers under the varous crtera are shown n Table IV. The lngustc evaluatons shown n Tables III and Table IV are converted nto trapezodal fuzzy numbers. Then the aggregated weght of crtera and aggregated fuzzy ratng of alternatves s calculated to construct the fuzzy decson matrx and determne the fuzzy weght of each crteron, as n Table V. ISSN: (Prnt); ISSN: (Onlne)

5 Proceedngs of the World Congress on Engneerng 2011 Vol II, July 6-8, 2011, London, U.K. TABLE III IMPORTANCE WEIGHT OF CRITERIA FROM THREE DECISION MAKERS Crtera Decson maers D1 D2 D Strategc condtons H H VH Techncal condtons H H MH Economc condtons M M ML Socal condtons ML M ML TABLE IV RATINGS OF THE FOUR ALTERNATIVE PLACES BY THE DECISION MAKERS UNDER THE VARIOUS CRITERIA Crtera Alternatves Strategc Techncal Economc Socal Haydarpaşa P P P G D1 Tuzla G MP MP P Sarayburnu P P P G VG VG VP M Haydarpaşa P P MP MG D2 Tuzla MG M M P Sarayburnu P P MP MG G G P M Haydarpaşa MP MP P MG D Tuzla MG MP M MP Sarayburnu MP MP P MG VG VG P MP TABLE VI CRISP VALUES FOR DECISION MATRIX AND WEIGHT OF EACH CRITERIA Crtera Strategc Techncal Economc Socal Weght Haydarpaşa 0.60 Tuzla Sarayburnu The crsp values for decson matrx and weght of each crteron are computed as shown n Table VI. The best and the worst values of all crteron ratngs are determned as follows: f * 1 = 0.88 f * 2 = 0.88 f * = 0.4 f * 4 = 0.60 f - 1 = f - 2 = f - = 0.15 f - 4 = The ranng of the alternatve frms by S, R and Q n decreasng order s shown n Table VIII. TABLE VIII THE RANKING OF THE ALTERNATIVE PLACES BY S, R AND Q IN DECREASING ORDER Ranng alternatves By S Tuzla Sarayburnu Haydarpaşa By R Tuzla Sarayburnu Haydarpaşa By Q Tuzla Sarayburnu Haydarpaşa Due to the extended fuzzy VIKOR method, s the most sutable place for a cruse port and the alternatve places after are Tuzla, Sarayburnu and Haydarpaşa accordng to Q value, respectvely. VI. CONCLUSION The analytc networ process (ANP) s a smple mathematcally based mult-crtera decson mang tool to deal wth unstructured and mult-attrbute problems and t allows for more complex nterrelatonshps among decson levels and attrbutes. On the other hand, the VIKOR method focuses on ranng and selectng from a set of alternatves n the presence of conflctng crtera. It determnes a compromse soluton that could be accepted by the decson maers. For these reasons, n ths paper ANP and extended VIKOR approaches have been presented under the fuzzy envronment to select the most sutable place for a cruse port n Istanbul. The results of dfferent place alternatves produced fuzzy ANP and extended fuzzy VIKOR have been compared. In concluson, accordng to the fnal score both fuzzy ANP and extended fuzzy VIKOR analyss, s the most sutable place for a new cruse port n Istanbul. On the bass of the numercal results, we can conclude that the proposed methods can soundly deal wth the structural mult-crtera decson mang problem wth our crtera and sub-crtera. The value of S, R and Q are calculated for all alternatve places as Table VII. TABLE VII THE VALUES OF S, R AND Q FOR ALL ALTERNATIVE PLACES Alternatves Haydarpaşa Tuzla Sarayburnu S R Q TABLE V AGGREGATED FUZZY WEIGHT OF CRITERIA AND AGGREGATED FUZZY RATING OF ALTERNATIVES Crtera Strategc condtons Techncal condtons Economc condtons Socal condtons Weght (0.70, 0.8, 0.87, 1.00) (0.40, 0.57, 0.6, 0.80) (0.20, 0.4, 0.47, 0.60) (0.10, 0.27, 0., 0.50) Haydarpaşa (0.10, 0.2, 0.27, 0.50) (0.10, 0.2, 0.27, 0.50) (0.10, 0.2, 0.27, 0.50) (0.40, 0.57, 0.6, 0.80) Tuzla (0.40, 0.57, 0.6, 0.80) (0.10, 0.27, 0., 0.50) (0.20, 0.4, 0.47, 0.60) (0.10, 0.2, 0.27, 0.50) Sarayburnu (0.10, 0.2, 0.27, 0.50) (0.70, 0.8, 0.87, 1.00) (0.10, 0.2, 0.27, 0.50) (0.70, 0.8, 0.87, 1.00) (0.10, 0.2, 0.27, 0.50) (0.00, 0.1, 0.17, 0.0) (0.40, 0.55, 0.6, 0.80) (0.20, 0.4, 0.47, 0.60) ISSN: (Prnt); ISSN: (Onlne)

6 Proceedngs of the World Congress on Engneerng 2011 Vol II, July 6-8, 2011, London, U.K. REFERENCES [1] J. Ma, C. Sheehey, and S. Tor, The Passenger Vessel Servces Act and Amerca s Cruse Toursm Industry, Research n Transportaton Economcs, vol. 26, pp , [2] A. Ö. Erbl, and T. Erbl, Redevelopment of Karaöy Harbor, Istanbul, Need for a New Plannng Approach n the Mdst of Change, Ctes, vol. 18, no., pp , [] R. Yu, and G.-H. Tzeng, A Soft Computng Method for Mult- Crtera Decson Mang wth Dependence and Feedbac, Appled Mathematcs and Computaton, vol. 180, pp. 6 75, [4] N.A. Ismaylova, M. Sağr, and R.N. Gasmov, A Multobectve Faculty Course Tme Slot Assgnment Problem wth Preferences, Mathematcal and Computer Modellng, vol. 46, pp , [5] R. Whtaer, Valdaton Examples of the Analytc Herarchy Process and Analytc Networ Process, Mathematcal and Computer Modellng, vol. 46, pp , [6] E.W.L. Cheng, and H. L, Applcaton of ANP n Process Models: An Example of Strategc Partnerng, Buldng and Envronment, vol. 42, pp , [7] S. Oprcovc, and G.-H. Tzeng, Extended VIKOR Method n Comparson wth Outranng Methods, European Journal of Operatonal Research, vol. 178, pp , [8] J.J.H. Lou, C.-Y. Tsa, R.-H. Ln, and G.-H. Tzeng, A Modfed VIKOR Multple-Crtera Decson Method for Improvng Domestc Arlnes Servce Qualty, Journal of Ar Transport Management, vol. 1-5, do: /.artraman , [9] C.-L. Chang, and C.-H. Hsu, Mult-Crtera Analyss Va the VIKOR Method for Prortzng Land-Use Restrant Strateges n the Tseng- Wen Reservor Watershed, Journal of Envronmental Management, vol. 90, pp , [10] M.K. Sayad, M. Heydar, and K. Shahanagh, Extenson of VIKOR Method for Decson Mang Problem wth Interval Numbers, Appled Mathematcal Modellng, vol., pp , [11] M. Dağdevren, İ, Yüsel, and M. Kurt, A Fuzzy Analytc Networ Process (ANP) Model to Identfy Faulty Behavor Rs (FBR) n Wor System, Safety Scence, do: /.ssc , [12] C. Tosun, and C. L. Jenns, Regonal Plannng Approaches to Toursm Development: The Case of Turey, Toursm Management, vol. 17, No. 7, pp , [1] M. Zargham and F. Szdarovszy, Revsng the OWA Operator for Mult Crtera Decson Mang Problems under Uncertanty, European Journal of Operatonal Research, vol. 198, pp , [14] A. Sanaye, S.F. Mousav, and A. Yazdanhah, Group Decson Mang Process for Suppler Selecton wth VIKOR under Fuzzy Envronment, Expert Systems wth Applcatons, vol. 7, pp. 24-0, ISSN: (Prnt); ISSN: (Onlne)