Applied Soft Computing

Transcription

1 Appld Soft omputng (20) ontnts lsts avalabl at ScncDrct Appld Soft omputng ournal hompag: A hybrd ANP modl n fuzzy nvronmnts for stratgc allanc partnr slcton n th arln ndustry Jams J.H. Lou a, Gwo-Hshung Tzng b,c,, hh-yuan Tsa d, hao-h Hsu a Dpartmnt of Industral Engnrng and Managmnt, Natonal Tap Unvrsty of Tchnology, No., Scton 3, hung-hsao East Road, Tap 06, Tawan b School of ommrc, Kanan Unvrsty, No., Kanan Road, Luchu, Taoyuan 338, Tawan c Insttut of Managmnt of Tchnology, Natonal hao Tung Unvrsty, 00 Ta-Hsuh Road, Hsnchu 300, Tawan d Dpartmnt of Industral Engnrng and Managmnt, Yuan Z Unvrsty, No. 35, Yuantung Road, hungl ty, Taoyuan ounty 320, Tawan Dpartmnt of Transportaton Managmnt, Tamkang Unvrsty, 5 Yng-huan Rd., Tamsu, Tap 25, Tawan artcl nfo abstract Artcl hstory: Rcvd 6 Sptmbr 2009 Rcvd n rvsd form 9 August 200 Accptd 7 January 20 Avalabl onln 26 Fbruary 20 Kywords: Stratgc allanc Fuzzy prfrnc programmng Analytc ntwork procss (ANP) Arln ndustry Stratgc arln allancs ar an ncrasngly common stratgy for nhancng arln compttvnss and satsfyng customr nds, spcally n an ra charactrzd by blurrng ndustry boundars, fastchangng tchnologs, and global ntgraton. Arlns hav bn vry actv n utlzng ths form of stratgc dvlopmnt. Howvr, th slcton of a sutabl partnr for a stratgc allanc s not an asy dcson, nvolvng a host of complx consdratons by dffrnt dpartmnts. Furthrmor th dcsonmakrs may hold dvrs opnons and prfrncs arsng du to ncomplt nformaton and knowldg or nhrnt conflct btwn varous dpartmnts. In ths study fuzzy prfrnc programmng and th analytc ntwork procss (ANP) ar combnd to form a modl for th slcton of partnrs for stratgc allancs. Th ffcts of uncrtanty and dsagrmnt btwn dcson-makrs as wll as th ntrdpndncy and fdback that ars from th us of dffrnt crtra and altrnatvs ar also addrssd. Ths gnrc modl can b asly xtndd to fulfll th spcfc nds of a varty of compans. 20 Publshd by Elsvr B.V.. Introducton Stratgc allancs btwn arlns ar now common n th avaton ndustry. Thy ar frquntly mad n rspons to changng conomc and rgulatory condtons []. Thr maor allancs stablshd wthn th last 0 yars Star Allanc, On-world and Sky Tam now account for narly 70% of passngrs and turnovr n th global markt [2]. Stratgc allanc stratgs allow arlns to xpand ntworks, attract mor passngrs, and tak advantag of product complmntarts, as wll as provdng costrducton opportunts n passngr srvc rlatd aras (such as cod-sharng, ont baggag handlng, ont us of loungs, gats and chck-n countrs, and xchang of flght attndants) [3]. A good stratgc partnr can furthr nhanc th qualty of thr connctng srvcs by adustng arrval and dpartur flghts so as to mnmz watng tm btwn flghts whl provdng suffcnt tm to mak connctons. On th othr hand, nffctv stratgc allancs can lad to th loss of cor comptncs and capablts, xposur to unxpctd rsk and vn busnss falur. Tak for xampl th fall of Swssar. Fnancal statmnts show that orrspondng author at: School of ommrc, Kanan Unvrsty, No., Kanan Road, Luchu, Taoyuan 338, Tawan. E-mal addrss: ghtzng@cc.nctu.du.tw (G.-H. Tzng). ts arln allanc polcy and nvstmnt stratgy wr rsponsbl for th maorty of ts losss from 997 to 200 [4]. Pror rsarch suggsts that th choc of allanc partnr s an mportant varabl wth sgnfcant nflunc on th prformanc of th stratgc allanc partnrs [5,6]. An approprat partnr s on that can contrbut rsourcs and capablts that th focal frm lacks. Ths ultmatly dtrmns th vablty of th stratgc allanc. Partnr-rlatd slcton crtra rqur consdraton to dtrmn whthr th corporat culturs of th partnrs ar compatbl, and whthr trust xsts btwn th partnrs managmnt tams. Ths nsurs that th slctd partnr and focal frm achv organzatonal ntrdpndnc. Although th mportanc of slctng th rght partnr for formng stratgc allancs has bn rcognzd n ltratur, thr hav bn fw mprcal studs on how to choos that partnr whch strss th ntrrlatonshp btwn th partnrs and th focal frm at th sam tm. Th analytc ntwork procss (ANP) was proposd by Saaty [7] to ovrcom th problm of ntrrlaton among crtra or altrnatvs. Th ANP s a gnral form of th analytc hrarchy procss (AHP), whch rlass th rstrctons of th hrarchcal structur. It has bn succssfully appld n many mult-crtra dcson makng (MDM) problms [8 ]. Howvr, du to problms such as ncomplt nformaton and subctv uncrtanty, vn xprts fnd t dffcult to quantfy th prcs rato of wghts for th dffrnt crtra. Th concpt of fuzzy sts has bn ncorporatd nto /$ s front mattr 20 Publshd by Elsvr B.V. do:0.06/.asoc

2 356 J.J.H. Lou t al. / Appld Soft omputng (20) AHP to dal wth th problm of uncrtanty, although ANP has not oftn bn usd to addrss ths typ of problm n fuzzy nvronmnts. A way to cop wth uncrtan udgmnts and to ncorporat th vagunss that typfs human thnkng s to xprss th prfrncs as fuzzy sts or fuzzy numbrs [2]. Thrfor, th obctv of ths study s to combn fuzzy prfrnc programmng and ANP to mak a modl capabl of hlpng arlns slct th bst partnr for stratgc allancs. Th rst of ths papr s structurd as follows: In Scton 2, w summarz som of th mportant prvous studs rgardng th stratgc allanc stratgy, and th problm charactrstcs ar dscrbd. In Scton 3, th basc concpts of fuzzy prfrnc programmng and ANP ar rvwd. In Scton 4, a stratgc allanc modl s dvlopd. Th mplmntaton usng th proposd fuzzy ANP s prsntd n Scton 5. Scton 6 ncluds dscussons and som conclusons. 2. Th stratgc allanc hl mrgr actvts hav slowd sgnfcantly snc 2000, stratgc allancs ar ncrasngly and wdly usd by arlns. Intrnatonal allancs gv arlns accss to parts of th world than would othrws b conomcal, or whr thr may lack th authorty to oprat thr own flghts [3]. Through allancs, partnrs ar abl to compt mor succssfully. Yoshno and Rangan [3] and Goms-assrs [4] dfn th allanc as a coopratv vntur btwn frms stuatd on th contnuum btwn markts and hrarchs. Th allanc s dstngushd by svral charactrstcs: ndpndnt frms; horzontal or vrtcal rlatonshps; rlatonshps whch ar not solly transactonal; partnrs shar rsourcs, rsks and bnfts but hav lmtd control and ncomplt contracts. Th typs of arln allanc may nclud rcprocal frqunt-flyr program rcognton, shard loungs and chck-n faclts, cod-sharng agrmnts, marktng arrangmnts, procurmnt polcs, systm commonalty, and vn th ntrchangrs of flght-crw prsonnl and arcraft [2]. Thr hav bn a numbr of mprcal studs on th ffctvnss of allancs, ncludng thos by Gllman Rsarch Assocats [5], Park and ho [6], Oum t al. [7], Park t al. [8], and Zhang t al. [3]. Rsults show that allancs mprov a carrr s prformanc on a numbr of conomc masurs, ncludng productvty, prcng, proftablty, and shar prc. Othr studs, such as Dv t al. [9] dscussd stratgc allancs from a numbr of thortcal prspctvs, ncludng transacton cost conomcs, ntwork rlatonshps, gam thory, dvlopmntal procsss, thcs and frm ntrnatonalzaton. Brucknr [20] analyzd th ffcts of ntrnatonal arln cod-sharng on traffc lvls and wlfar usng spcfc dmand and cost functons. H showd that th bnfcal ffcts of cod-sharng outwgh ts harmful ffcts for most paramtr valus n hs thortcal modl. Fan t al. [2] xamnd th forcs nfluncng th consoldaton and structur of th arln allanc. Thy hghlghtd th followng fv forcs: () ncrasd globalzaton n trad and ar transportaton; () ncrasd ntra-rgonal ntracton, () conomc ncntvs for arln consoldaton; (v) pac of lbralzaton of ntrnatonal ar transport ndustry, and (v) ant-trust concrns. Holtbrugg t al. [2] nvstgatd human rsourc managmnt (HRM) aftr stratgc allanc. Th man focus n all of ths allanc studs has bn th mportanc of th stratgc allanc or th prformanc masurs aftr th allanc. Dscusson of th ssu of stratgc partnr slcton has bn rlatvly rar. Th slcton of a sutabl partnr for a stratgc allanc s not an asy dcson, nvolvng many complx consdratons. It s ssntally a group-dcson nvolvng many dmnsons and nhrnt rsks, such as ntr-partnr conflcts, and potntal structural and cultural ncompatblts. Th proposd hybrd fuzzy prfrnc programmng and ANP modl s abl to consdr dcson-makrs uncrtanty and provds nsghts nto th ntrrlatonshp btwn allanc motvatons and partnr slcton crtra n th arln ndustry, whch to th bst of our knowldg, has largly bn nglctd. 3. Proposd hybrd fuzzy prfrnc programmng and ANP modl In ths scton, th concpts of fuzzy prfrnc programmng for copng wth th uncrtan udgmnts n a group-dcson procss ar frst ntroducd. Th ANP mthod for dtrmnng th bst partnr for th stratgc allanc s thn dscussd, ncludng consdraton of th dpndnc and fdback ffcts. Th combnd modl can hlp compans to valuat a sutabl partnr and fulfll thr spcfd nds. 3.. Fuzzy prfrnc programmng Fuzzy prfrnc programmng was frst proposd by Mkhalov and Sngh [22]. It s manly usd to drv prorty vctors from a st of comparson udgmnts or ntrval comparsons. Lt A = {l, u } rprsnt an ntrval comparson matrx wth n componnts, whr l and u ar th lowr and uppr bounds of th corrspondng uncrtan udgmnts. Intrval udgmnts ar consdrd consstnt f thr xsts a prorty vctor w that satsfs th followng nqualts: l w u w. () Inconsstncy n th udgmnts ndcats that no prorty vctor satsfs all th ntrval udgmnts smultanously. Thus, a suffcnt soluton vctor has to satsfy all th ntrval udgmnts as much as possbl, that s l w u w, =, 2,...,n ; = 2, 3,...,n; >, (2) whr dnots th statmnt fuzzy lss or qual to. In ordr to handl th abov nqualts w can rprsnt thm as a st of sngl-sdd fuzzy constrants: w w u 0, (3) w + w l 0. Th abov m fuzzy constrans can b rprsntd n th followng matrx form: Rw 0, (4) whr th matrx R R m n ; m = n(n ). Th kth row of Eq. (4) s a fuzzy lnar constrant, whch can b dfnd as a lnar mmbrshp functon of th typ: R kw, 0 < R d k w d k, Ãk (R k w) = k (5) 0, R k w d k,, R k w 0 whr d k s tolranc paramtr for th kth row, rprsntng th admssbl ntrval of approxmat satsfacton of crsp nqualty R k w 0. Th mmbrshp functon of R k w can b rprsntd as n Fg.. Th mmbrshp functon (5) s qual to zro whn th corrspondng crsp constrant R k w 0 s strongly volatd; t s btwn zro and on whn th crsp constrant s approxmatly satsfd; and t s qual to on whn th constrant s fully satsfd. To solv th fuzzy prfrnc programmng, two assumptons ar ndd. Frst lt Ãk (R k w), k =, 2,..., m b th mmbrshp functons of th fuzzy constrants Rw 0 onthn dmnsonal

3 J.J.H. Lou t al. / Appld Soft omputng (20) smplx, whr à k s a fuzzy numbr of th kth par-ws comparson: Q n ={w = (w,w 2,...,w n ) w > 0, w + w w n = }. Th fasbl fuzzy P ara on th smplx Q n s a fuzzy st, dscrbd by th mmbrshp functon: P (w) = [mn { (R w),..., m (R m w } w w n = ], (7) (6) of mmbrshp n th dcson st: D (x max ) = maxmn{ G (x), (x)}. (0) x 0 Howvr, n our proposd fuzzy prfrnc programmng mthod, w only apply th fuzzy constrants and do not us th mmbrshp functon of fuzzy goals G (s Appndx A). Furthrmor, th ˇ valu s qual to, rprsntng th hghst consstnt lvl (whch s smlar to 0 n th AHP consstncy rato ); 0 ndcats whn th constrants ar compltly volatd. Th fasbl fuzzy ara s dfnd as th ntrscton of all fuzzy constrants on th smplx. Th scond assumpton of th fuzzy prfrnc programmng slcts a prorty vctor wth th hghst dgr of mmbrshp as follows: ˇ = max[mn{ã (R w),...,ãm (R m w)} w w n = ], (8) whr m = n(n ). 2 Bllman and Zadh [23] proposd a max-mn oprator for drvng a maxmzng soluton for gnral dcson-makng problms wth fuzzy goals and fuzzy constrants. Zmmrmann [24] mployd Bllman and Zadh s da to show that th max-mn fuzzy lnar problm can b transfrrd nto a convntonal lnar programmng: Maxmz ˇ Subct to d kˇ + R k w d k, w + w w n =, w > 0,=, 2,...,n, k=, 2,...,m. whr ˇ (R k w/d k ) can now b wrttn as d kˇ + R k w d k. Th dtals of th max-mn oprator and ts rlatonshp btwn fuzzy prfrnc programmng ar llustratd n Appndx A. For comparson, w also addd th comproms solutons wth multpl obctvs obtand usng th mn-max oprator, as shown n Appndx B. Th optmal soluton for th abov lnar program s a vctor (w,ˇ ), whos frst componnt rprsnts a prorty vctor whch has th maxmum dgr of mmbrshp n th fasbl fuzzy ara, and th scond componnt gvs us th valu of that maxmum dgr, th so-calld consstncy ndx [2]. Plas not that for th max-mn oprator, th maxmum ˇ rprsnts th hghst dgr = 2 m (9) 2 n n 2 m m Analytc ntwork procss ANP s th gnrc form of AHP, allowng for mor complx ntrdpndnt rlatons among lmnts/crtra [7]. Saaty [25] frst dvlopd AHP n 97, to hlp stablsh dcson modls through a procss that contans both qualtatv and quanttatv componnts. Qualtatvly, t dcomposs a dcson problm from th top ovrall goal to a st of managabl clustrs, sub-clustrs, and so on, down to th bottom lvl, whch usually contans scnaros or altrnatvs [26]. Although both th AHP and th ANP drv rato scal prorts by makng pard comparsons of lmnts on a crtron, thr ar som dffrncs btwn thm. Th frst dffrnc s that th AHP s a spcal cas of th ANP, bcaus th ANP handls dpndnc wthn a clustr (nnr dpndnc) and among dffrnt clustrs (outr dpndnc). Scond, th ANP s a nonlnar structur, whl th AHP s hrarchcal and lnar, wth a goal at th top lvl and th altrnatvs on th bottom lvl [27]. Th frst stp n th ANP s to dvlop th structur of th dsgnd modl. Th AHP dcson modl s always rstrctd to bng hrarchcal, contanng svral lvls assumd to hav ndpndnt crtra. Only adustd lvls of th ANP ar assumd to hav dpndnc/corrlaton wth ach othr. Thrfor, th ANP s a ntwork structur, whr th hrarchcal rstrcton s rlaxd so that dpndnc/corrlaton can b stpulatd n any part of th dcson modl to form th sub-matrcs for th so-calld suprmatrx [7,26]. Th scond stp s to compar th crtra for th whol systm to form a suprmatrx. Ths s don through par-ws comparsons by askng How much mportanc dos a crtron hav compard to anothr crtron wth rspct to our ntrsts or prfrncs? Th rlatv mportanc valu s dtrmnd usng a scal of 9, rprsntng qual mportanc to xtrm mportanc, rspctvly [7,28]. Th gnral form of th suprmatrx can b dscrbd as follows: 2 m n m2 2 2n 2 m m mn m m 2m mm, mnm ()

4 358 J.J.H. Lou t al. / Appld Soft omputng (20) μ ~ Ak ( Rw) k lustr lustr 2 lustr Rw k d k Fg.. Illustratd mmbrshp functon. whr m dnots th mth clustr; mn dnots th nth lmnt n th mth clustr; and matrx s compos of a sral prncpal gnvctor of th nflunc of th lmnts compard n th th clustr to th th clustr. Th form of th suprmatrx dpnds on th varty of th structur. For xampl, f th structur of th systm s shown as n Fg. 2, th unwghtd suprmatrx, contanng th local prorts drvd from th par-ws comparsons throughout th ntwork can b llustratd as follows: 0 = (2) 2 s a matrx that rprsnts th wghts of clustr 2 wth rspct to clustr ; matrx 32 dnots th wghts of clustr 3 wth rspct to clustr 2; and matrx 3 shows th wghts of clustr wth rspct to clustr 3. In addton, matrx 33 s dnotd as th nnr dpndnc and fdback wthn clustr 3. Aftr formng th suprmatrx, th wghtd suprmatrx s drvd by transformng th sum of all columns to xactly unty. Ths stp s vry smlar n concpt to th Markov chan for nsurng that th sum of th probablts of all stats quals [28]. Th wghtd suprmatrx can thn b rasd to lmtng powrs, to calculat th ovrall prorts that ar rprsntd on ach row n th convrgd matrx. 4. onstructng a stratgc partnrng modl for analyss Th modl was dvlopd and valdatd usng nput from an ntrnatonal arln opratng n Tawan. Ths arln currntly fls to mor than 40 dstnatons around th world, although most ar wthn th Asa Pacfc rgon. Th company has sought to on stratgc allancs n ordr to dvlop a far-rachng srvc ntwork and ncras compttv powr, to nhanc th ffctvnss of ts global logstcs and to provd bttr srvc for satsfyng customr nds. Th dcson s a stratgc on, n that th succss of th dvlopmnt would hav grat mpact on th compttvnss of th company. Snc partnr slcton s crucal to succss, t s mpratv for dcson-makrs to dvs, dntfy and rcognz ffctv partnr slcton factors as wll as to valuat qustons of compatblty and fasblty pror to onng or dvlopng any stratgc allanc. Th concptual modl of th stratgc partnr slcton procss s frst dvlopd basd on prvous work [0,6,8,26,29]. Thn, through th Dlph mthod w consultd wth som snor managrs of th arln n ordr to modfy th orgnal modl. Aftr addng/dltng som lmnts and modfyng th flow graph, th fnal stratgc modl usd n ths study s llustratd n Fg. 3. Of Fg. 2. Illustratd structur of th systm (xampl). cours, th prsnt ntwork was manly basd on th managrs opnons of th cas company; othr compans may nd up wth dffrnt ntworks basd on thr own opraton nvronmnt. Th rlatv factors and altrnatvs ar structurd n th form of a hrarchy. Th modl rqurs th dvlopmnt of attrbuts at ach lvl and a dfnton of thr rlatonshp. Th ultmat goal s to slct th bst partnr. To do ths, t s frst ncssary to stratgcally analyz th ntrnal organzatonal and xtrnal nvronmntal drvng forcs, whch act as th undrlyng motvaton and rasons for allanc formaton. Basd on th consdrd drvng forcs, th allancs scop and structur ar provdd for valuaton. Thr ar fv maor ways to mplmnt stratgc allancs, ncludng markt, product/srvc, computr systms, qupmnt and qupmnt srvcng, and logstcs. Aftr th typs of stratgc allanc ar nvstgatd, som chocs of approprat partnrs for stratgc allanc formaton ar consdrd. Fnally, th valuaton of th allanc s fd back nto th analytcal phas, to ncorporat any changs basd upon xprnc. ar skng to dtrmn whch of svral altrnatvs would bst support th ralzaton of th ultmat goal whl fdback ffcts ar consdrd. Dtals of th procdur ar dscrbd as follows: () Stratgc analyss: Th frst stp s th stratgc analyss phas whr ntrnal and xtrnal drvng forcs for a stratgc allanc ar analyzd. Th ntrnal drvrs nclud rsk sharng, conoms of scal, accss to assts, rsourcs and comptncs, and shapng comptton. Stratgc allancs ar sn as an attractv mchansm for hdgng rsk, bcaus nthr partnr has to bar th full rsk or cost of th allanc actvts [30]. Th conoms of scal advantag can b achvd whn allanc partnrs lnk up thr xstng ntworks so that thy can provd connctng srvcs for nw markts. Marktng costs can b shard btwn allanc partnrs, whch may hav strongly ntrnchd postons n crtan markts [3]. Th rgulatory framwork for blatral agrmnts, landng rghts and congston at crtan arports mans that arlns alrady possssng lcnss to oprat a rout or hav slots at congstd arports hav mportant and marktabl assts that ar attractv to allanc partnrs [29]. Stratgc allancs can also b usd as a dfnsv ploy to rduc comptton, snc an obvous bnft of stratgc allancs s convrtng a compttor nto a partnr [32]. Altrnatvly, allanc formaton may form part of an offnsv stratgy, for xampl by lnkng wth a rval n ordr to put prssur on th profts and markt shar of a common compttor [33]. Th xtrnal drvrs nclud nformaton rvoluton, conomc rstructurng, and global comptton. omputr rsrvatons systms (RS) allow arlns to montor, manag and control thr capacty through yld managmnt and thr clnts through frqunt flyr programs. Undoubtdly, th arlns that own th RS wll favor thr own flghts. Jont arln ownrshp may rduc th chancs of RS bng basd n favor of a partcular arln, but th domnanc of RS compans gvs thm consdrabl markt powr [3]. Also, consumrs oftn favor thr own natonal arln or ts partnrs to an

5 J.J.H. Lou t al. / Appld Soft omputng (20) Stratgc Allanc Motvatons Intrnal Drvrs Rsk sharng Economs of scal, scop & larnng Accss to assts, rsourcs & comptncs Shapng comptton Extrnal Drvrs Informaton rvoluton Economc rstructurng Global comptton Stratgc Allanc Structur Marktng Product / srvc omputr systms Equpmnt and Logstcs od-sharng Intgratd brands Intgratd qupmnt srvcng Shard offcs Frqunt flyr Brands rman Shard Shard qupmnt Sparat offcs rcprocty sparat Sparat Sparat qupmnt Shard trmnals Promoton ntgratd Adoptd undr Shard mantnanc Sparat trmnals Promoton sparat lcns Sparat mantnanc hoc of Allanc Partnrs/Altrnatvs apablty ompatblty ontrol Gographcal ft ommtmnt Star Allanc Onworld SkyTam Fg. 3. Ntwork structur of proct partnrng. xtraordnary dgr. Such a patrotc atttud to purchasng, rarly rplcatd n othr ndustrs, drvs arlns to form allancs as th only ffctv mans of markt ntry [29]. Economc rstructurng through th phlosophy of conomc dsngagmnt by govrnmnts, as s currntly occurrng n many parts of th world, has also had a maor mpact on arln ndustry structur. In addton, lbralzaton, prvatzaton, forgn ownrshp and transnatonal mrgrs may also hav a maor mpact upon th futur structur of th arln ndustry, vn though many rgulatory and ownrshp barrrs rman n forc worldwd. Snc ths mans that mrgrs and acqustons ar oftn prcludd as vabl growth stratgs for ntrnatonal arlns. onsquntly th formaton of stratgc allancs s, n many cass, th only avalabl form of markt ntry. Arlns sk to maxmz thr global rach, n th blf that thos that offr a global srvc wll b n th strongst compttv poston. In othr words, globalzaton s an mportant xtrnal drv for allanc formaton n today s hghly compttv nvronmnt. (2) Stratgc dvlopmnt: In dtrmnng th mthods by whch stratgc dvlopmnt wll tak plac, organzatonal managmnt s facd wth makng a choc btwn a varty of dffrnt allanc structurs and scops, as ndcatd n Fg. 3. Th arln wll prortz ts stratgc dvlopmnt basd on prvous stratgc analyss and ts currnt opratonal stuaton. Thr ar many ways to mplmnt a stratgc allanc, such as shard arport faclts, synchronzd schdulng, rcprocty n frqunt flyr programs, frght coordnaton and ont marktng actvts. Thr ar usually nnr dpndnc and fdback ffcts btwn ths dffrnt stratgc allanc stratgs. Also, du to rsourc constrants, t may b possbl to pursu only som of ths optons. Onc th prfrnc

6 3520 J.J.H. Lou t al. / Appld Soft omputng (20) Tabl Fuzzy wght comparsons of xtrnal drvrs. Markt Srvc/product omputr Equpmnt Logstc ghts Markt (, 3) (2, 5) (3, 5) (, 3) 0.4 Srvc/Product (/3, ) (/5, /3) (3, 7) (, 3) 0.6 omputr Systm (/5, /2) (3, 5) (4, 7) (3, 5) 0.24 Equpmnt (/5, /2) (/3, ) (/7, /4) (/2, 3) 0.08 Logstc (/3, ) (/3, ) (/5, /3) (/3, 2) 0. onsstncy ndx = s mad, th arln s commttd to pursung ths courss of acton. (3) Stratgc partnr slcton: Thr ar svral rasons for th succssful mplmntaton of stratgc allancs but th mportanc of partnr slcton has bn mphaszd by svral wrtrs [34,35]. Thr ar som mportant factors that nd to b consdrd whn choosng approprat partnrs. Th slctd partnr should hav th capablty to carry out ts rol wthn th allanc. Partnrs should also b abl to dmonstrat qual commtmnt to an allanc through xprncng commnsurat lvls of rsk. Th compatblty of th partnr and th focal frm, both n cultural and opratonal trms, s anothr sgnfcant factor. For xampl, th falur of th Alcazar allanc n th arly 990s was du to msundrstandngs btwn th varous Amrcan partnrs and dffrntly afflatd RS systms. Th succss of th stratgc allanc also dpnds on an ffctv control systm and whthr partnrs ar lkly to contrbut to th allanc. Somtms, a strong focusd ladrshp can b vwd as opportunstc, and a powr mbalanc lnds potntal for conflct among th partnrs. A ky quston that nds to b addrssd n th assssmnt of allanc control s th xtnt to whch ach partnr s abl to achv whatvr stratgc obctvs thy hav st thmslvs whn ntrng nto th allanc rlatonshp [29]. Th gographcal ft also nds to b consdrd whn slctng stratgc partnrs. Arlns ar carful to avod formng partnrshps wth arlns that hav ovrlappng markts. For xampl, n th Northwst/KLM allanc, partnrs hav dstnctv gographcal strngths n th USA and Europ. (4) Fdback valuaton: Aftr th partnrs ar dcdd, th slctd partnrs wll hav som dgr of mpact or ffct on th focal frm, both ntrnal and xtrnal. hthr an allanc can mprov a carrr s prformanc and fulfll th obctvs that drov th allanc s an ssntal factor for long-lastng stratgc allancs. If th ntrdpndnc wthn th allanc s not strong nough and prformanc mprovmnt s lmtd, th allanc wll asly collaps. Thrfor, th valuaton and fdback for slctd partnrs as rlatd to th drvng forcs s ncludd n ths modl. 5. Implmntaton of th proposd hybrd modl In ths study, th gnral managr of th arln undr study dsgnatd a tam to dvlop a stratgc partnr slcton plan. Twnty-fv managrs from dffrnt dpartmnts, ncludng plannng, opraton, mantnanc, human rsourcs, nformaton systms, and safty, wth at last 5 yars xprnc n th arln and xprts n thr own partcular flds flld out a survy. 5.. Par-ws comparsons and fuzzy prfrnc programmng In ANP, lk AHP, managrs ar askd to mak par-ws comparsons of th lmnts n ach lvl wth rspct to thr rlatv mportanc toward thr uppr/control crtron. To nsur that no xtrm cass xst, th Dlph mthod s appld to collct th data. Snc dffrnt xprts com from dffrnt dpartmnts thy propound a varty of vwponts, and hs or hr udgmnt wll b dffrnt. Aftr crculatng th qustonnar svral tms, ach par-ws comparson convrgs to an accptabl rang, wthout xtrm cass. As mntond n Scton 3.2, a scal of 9 s usd to compar th two componnts, wth a scor of rprsntng no dffrnc btwn th two componnts and 9 rprsntng ovrwhlmng domnanc of th componnt undr consdraton (row componnt) ovr th comparson componnt (column componnt). hn scorng s conductd for a par, a rcprocal valu s automatcally assgnd to th rvrs comparson wthn th matrx (.., a =/a ). Snc many of ths valus ar stratgc and subctv, th comparson ratos ar rprsntd as an ntrval (l,u ), wth uppr and lowr bounds. Two sparat par-ws comparson matrcs (ntrnal and xtrnal drvrs) hav to b dvlopd n ths stp. An xampl of th par-ws comparson matrx of xtrnal drvrs for th stratgc allanc s shown n Tabl. Plas not that th ntrvals shown n Tabl ndcat a rang of answrs from 25 managrs. Usng th fuzzy prfrnc programmng ntroducd n Scton 3., th ntrval of th comparson ratos (Tabl ) can b transfrrd nto a lnar programmng problm as follows (th tolranc paramtr d k = ): maxmz ˇ Subct to ˇ + w 3w 2, ˇ w + w 2, ˇ + w 4 3w 5, ˇ w w 5, w + w 2 +w 5 =. Th abov lnar programmng problm s solvd n ordr to drv th consstncy ndx and th wghtd prorts for ths matrx, as ndcatd n th last column of Tabl. Th wghtd ntrnal drv prorts can b usd for smlar procdurs to obtan th sub-matrx showng motvaton, as llustratd n Tabl 2. It s obvous that th markt (0.4) has th hghst prorty wth rspct to xtrnal drvs, whl th computr systm (0.43) s consdrd th most mportant of ntrnal drvs. Th scond stp n our par-ws comparson of dffrnt allanc structurs s to compar th rlatv mportanc of thr proposd allancs. Th thr proposd allancs hav bn arrvd Tabl 2 ght prorts for motvaton. ghts Markt Srvc/product omputr systm Equpmnt Logstc Intrnal drvs Extrnal drvs

7 J.J.H. Lou t al. / Appld Soft omputng (20) Tabl 3 Fuzzy wght comparsons for marktng. Star Allanc On-world Sky Tam ghts Star Allanc (, 5) (3, 7) 0.57 On-world (/5, ) (, 3) 0.29 Sky Tam (/7, /3) (/3, ) 0.4 Tabl 4 ght prorts undr dffrnt allanc structurs. ghts Star Allanc On-world Sky Tam Motvatons Stratgc Allanc Structur hoc of Allanc Partnr Stratgc hoc of Motvatons Allanc Allanc Structur Partnr Fg. 4. Structur of stratgc allanc n th ANP modl. 3 Marktng Srvc/product omputr systm Equpmnt Logstcs at through dscusson wth 25 arln managrs through th Dlph mthod. Fv sparat par-ws comparson matrcs (markt, srvc/product, computr systm, qupmnt, and logstcs) ar rqurd to fully dscrb th rlatv mportanc of dffrnt allancs wth rspct to allanc structur. An xampl of on of ths matrcs s shown n Tabl 3. Th wght prorts (last column of Tabl 3) can b drvd by fuzzy prfrnc programmng smlar to stp on. In ths cas, th Star Allanc would hav th gratst mportanc wth rspct to marktng consdraton. Th othr wght prorts (undr dffrnt allanc structurs) ar shown n Tabl 4. Th rsults ndcat that On-world s bttr for thr computr systm whl th Sky Tam lads on logstcs. As dscrbd n our modl (Fg. 3), th motvatons for stratgc allancs nclud both xtrnal and ntrnal drvrs. Any formulatd stratgc allanc should fulfll ts orgnal motvatons. Thrfor, w must consdr th fd-back ffct that wll occur f th slctd allanc can satsfy ts ntrnal and xtrnal drvs. Usng smlar procdurs to stps on and two, w obtan th wght prorts wth rspct to dffrnt allancs as shown n Tabl 5. Inth Star Allanc thr s probably mor mphass placd on xtrnal drvs, On-world placs gratr mportanc on ntrnal drvs, whl n Sky Tam ntrnal and xtrnal drvs ar found to b farly clos n mportanc to ach othr Snstv analyss In our fuzzy prfrnc programmng, th tolranc d k was st as. That s th mmbrshp functon of par-ws comparson wll dcras monotoncally from to 0 ovr th tolranc ntrval d k = (Fg. ). To nvstgat th nflunc of th tolranc on th obtand wghts, w conductd a snstvty analyss by sttng dffrnt d k valus. trd valus rangng from 0. to 9, whch rprsntd th maxmum par-ws comparson valu to th mnmum valu. Ths rsults ndcat that th obtand wghts wr all th sam for all th dffrnt tolranc sttng valus, xcpt that th ˇ valu ncrasd from 0.89 to 0.99 as th tolranc d k ncrasd from 0. to 9. Snc our man purpos s to drv th wghts of th crtra, th proposd modl s robust wth th varous tolrancs of th mmbrshp functons. Tabl 5 ghts of ntrnal and xtrnal drvs undr dffrnt allancs. ghts Intrnal drvs Star Allanc On-world Sky Tam Extrnal drvs 5.3. Supr-matrx and lmt matrx Gvn th ntrdpndnt nfluncs, a systm that conssts of procss stps and fdback ffcts nds to b transformd nto a supr-matrx. Ths can b achvd by ntrng th local prorty vctors nto th supr-matrx, to n turn obtan global prorts. Th nnr dpndnc and fdback ffcts btwn lvls/clustrs for th modl dvlopd for stratgc allanc slcton ar shown n Fg. 3. Innr dpndnc xsts wthn th allanc structur and fdback ffcts ar rlatd to motvaton. A gnral vw of th supr-matrx for ths study s also shown (Fg. 4), whr th parws comparson matrcs of th thr stps ar ntrd nto th corrct locatons. In a supr matrx, ths ndvdual matrcs ar calld sub-matrcs. For xampl, 2 s th sub-matrx of motvaton, whl 22 s th sub-matrx of nnr dpndnc wthn th allanc structurs clustr. Th complt un-wghtd suprmatrx for th ANP modl s show n Tabl 6. Plas not that du to nnr dpndnc 22, th dagonal lmnts of 22 ar frst st to 0.5 whl th othr lmnts ar st to 0, thn th column vctors (undr th allanc structur) ar normalzd to sum up to on [28]. Th un-wghtd supr-matrx s thn rasd to a suffcntly larg powr untl convrgnc occurs. In ths study, convrgnc occurs at 36 tms. Tabl 7 provds th fnal lmt matrx. Ths lmt matrx s a column stochastc and rprsnts th fnal gnvctor. Th altrnatv wth th largst valu should b th on slctd. As shown n Tabl 7, th rsults of th allanc-partnr altrnatvs n th cas study pont to th slcton of On-world as th bst choc, du to a wght of 0.08, whch s largr than that of th othr two altrnatvs Rsult analyss and dscusson Although ANP has bn wdly usd n varous applcatons, t s hard for dcson-makrs to quantfy prcs udgmnts about crtra undr condtons of ncomplt nformaton and subctv uncrtanty. In ths papr, w propos a hybrd modl combnng fuzzy prfrnc programmng and ANP, whch xtnds th orgnal ANP by usng fuzzy udgmnts to compar th ratos of wghts btwn crtra. Ths modl can avod th convrgnc problms ncountrd usng standard fuzzy arthmtc opratons n fuzzy ANP. Snc standard fuzzy arthmtc opratons ar usd to multply and dvd fuzzy numbrs, th mthod may rsult n th convrgnc and ratonal problms of fuzzy global wghts. us lnar programmng to drv th stady-stat prorty vctors, and thn us ANP to consdr clustrs/crtra dpndnc. Th modl should b mor practcal for actual applcaton than ANP, whch gnors th uncrtan udgmnts oftn mad n th ral world, and convntonal fuzzy ANP, whch causs convrgnt problms. Tabl 8 shows a comparson of th rsults obtand btwn our proposd modl and th orgnal ANP mthod. Our modl ndcats that On-world s th bst slcton whl th orgnal ANP mthod ponts to Star Allanc (wth a hghr wght 0.00 than of On-world) as bng optmal. Howvr, n our

8 3522 J.J.H. Lou t al. / Appld Soft omputng (20) Tabl 6 Un-wghtd suprmatrx. Motvatons Allanc structur Partnrs Not: = ntrnal drv; 2 = xtrnal drv; 2 = marktng; 22 = srvc/product; 23 = computr systm; 24 = qupmnts; 25 = logstc; 3 = Star Allanc; 32 = Onworld; 33 = Sky Tam. Tabl 7 Lmt suprmatrx. Motvatons Allanc structur Partnrs proposd modl, w consdrd dcson-makr uncrtanty whn thy mak a dcson, whch could mak ths modl mor ralstc than th orgnal mthod. Furthrmor, w dvdd th xprts nto two groups, tchncal (opratonal, mantnanc and safty dpartmnts) and non-tchncal (fnancal, marktng and srvc dpartmnts). Th opnon of th tchncal group was that Onworld was th bst allanc, but th rsult for th non-tchncal group gav Star Allanc th hghst wght. Ths rsults mght b bcaus Star Allanc has a hghr marktng shar, and nontchncal groups dmd marktng and srvc to b th mportant crtra. On th othr hand, On-orld was chosn by tchncal groups du to th xprts thnkng that On-world offrd mor rlabl tchncal opraton. Agan, ths s th advantag of our modl that t can ntgrat dffrnt opnons to com up wth an optmal soluton. Th mprcal rsults ndcat that On-world s th bst slcton from th arln s vwpont. Howvr whthr to on an allanc or not s not only dpndnt on th company s wllngnss, but also on accptanc of th allanc. Hr w provd a tool to hlp arlns slct an optmzd stratgc allanc gvn thr own rqurmnts. It s also worth notng that dffrnt arlns may nd up wth dffrnt rsults, basd on thr own spcfc nds. Although th prsnt modl has provn valuabl, thr ar stll som aras that nd furthr dscusson. It s acknowldgd that th dcson lvls and crtra nvolvd n any partcular mplmntaton may dffr dpndng on th arlns/ntrprss nvolvd. In fact, ths s on of th strngths of ANP, whch can b usd to construct varous structurs consdrng nnr dpndnc and fdback ffcts. A st of crtra should b dsgnd for ach applcaton, dpndng upon what s dmd mportant for that applcaton. Dcson crtra or dpndnc wthn/btwn clustrs that a company consdrs to b crucal can b asly addd to th gnrc modl. Also, th wghtng gvn ach componnt n th modl s dpndnt on th dcson-makrs valuaton of th componnt. Ths hlps facltat talorng of th modl to th company n quston. For xampl, an arln that strsss nlargng markts would lkly slct crtra and wghtngs dffrnt from an arln skng to provd bttr srvcs/products. On th othr hand, not all possbl crtra and ntractons ar consdrd. Agan, dcson factors could b addd, dpndng on th dcson nvronmnt. Possbl xtnsons n ths ara currntly bng xplord nclud rsk analyss of stratgc allancs and dffrnt ntractons btwn clustrs. For nstanc, currntly, only a on-way nflunc btwn motvatons, allanc structurs and partnrs s ncludd n th modl. Th ntractons could b modld as two-way ntractons. Prhaps a mor ntrstng and usful xtnson of th modl would b to nclud ntractons wthn allanc structurs and altrnatvs (partnrs). On of th lmtatons of th orgnal ANP s ts dpndncy on th dcson-makrs. Th wghtngs obtand ar basd on th dcson makrs subctv opnons and many of ths valus ar stratgc, thrfor, addtonal stratgc group dcson-makng tools ar ndd. Although w can us scnaro plannng or th Dlph approach, ths ar stll tm-consumng and t s somtms Tabl 8 omparson btwn fuzzy prfrnc programmng wth ANP and orgnal ANP. Motvatons Allanc structur Partnrs FPP + ANP ANP

9 J.J.H. Lou t al. / Appld Soft omputng (20) μ g ( x) μ () x f () x f * ( x ) f ( x) b (Ax) b+ p (Ax) Fg. A. Mmbrshp functon of fuzzy goals. hard to rach a consnsus. In ths study, th uncrtanty of udgmnt s rmovd by xprssng th comparson ratos as an ntrval, to ncorporat th vagunss nhrnt n human thnkng. Th proposd modl has som furthr advantags. It provds opportunty for solvng prortzaton problms wth mxd typs of comparson udgmnts, such as ntrvals or crsp numbrs. Also, th prortzaton problm s tratd as a lnar program, whch can asly b solvd. 6. oncludng rmarks Th purpos of ths papr s to dscrb a mthod for stratgc allanc slcton that allows for consdraton of mportant ntractons among dcson lvls and crtra. us a hybrd modl combnng fuzzy prfrnc programmng and ANP mthodology that consdrs uncrtanty n group dcsons, and both nnr dpndnc and fdback ffcts for ths valuaton. dvlop a modl for th stratgc allanc partnr slcton procss basd on th ltratur and adaptd for an arln n Tawan. Th arln acts as a cas study for valdaton of th modl approach. Ths work should b valuabl to practtonrs bcaus t provds a gnrc modl for partnr slcton. Ths stratgc dcson-makng tool can assst an arln n comparng proposd allanc partnrs wth rspct to dffrnt procss stags and allanc structurs. Th modl suggsts that th On-world allanc s th bst opton for ths partcular arln. Th cas study hlps to vrfy that th proposd modl s an ffctv and ffcnt dcson-makng tool whch can b asly xtndd. Appndx A. Th max-mn oprator A.. Fuzzy goal and fuzzy constrant programmng In fuzzy goal and fuzzy constrant programmng problms, t can mathmatcally b rprsntd as max s.t. [ f (x), f 2 (x),, f k (x)] Ãx b x 0 (A) whr x s th vctor of varabls and b s th vctor for th fuzzy rght hand sd. Frst, w can dfn th mmbrshp functon of fuzzy goals and fuzzy constrants as follows (s Fgs. A and A2):, f (x) >f (x) g (x) = f (x) f (x) f (x) f (x), f (x) f (x) f (x) (A2) 0, f (x) <f (x) Fg. A2. Mmbrshp functon of fuzzy constrants., (Ax) <b (x) = (Ax) b, b p (Ax) b + p 0, (Ax) >b + p (A3) Th mmbrshp functon (A2) of th fuzzy obctv functon should b 0 for f (x) lvls qual to lss than lowr bound, for f (x) qual to or gratr than th uppr bounds, and monotoncally ncrasng from 0 to. Th mmbrshp functon (3) of th fuzzy st rprsntng constrant should b 0 f th constrant s strongly volatd (f t xcds b + p ), f t s satsfd n th crsp sns (f qual to or lss than b ), and should dcras monotoncally from to 0 ovr th tolranc ntrval (b, b + p ). Th mmbrshp functon of th dcson st, D (x), s gvn by D (x) = mn{ G (x), (x)}. (A4) Th mn-oprator s usd to modl th ntrscton of th fuzzy sts of obctvs and constrants. Snc th dcson makr wants to hav a crsp dcson proposal, th maxmzng dcson wll corrspond to th valu of x, x max, that has th hghst dgr of mmbrshp n th dcson st: D (x max ) = max x 0 mn{ G (x), (x)}. (A5) In ths cas, w can transfr Eq. (A) to ˇ xprsson mthod as follows: max x s.t. ˇ ˇ f (x) f (x) f (x) f (x), =, 2,...,k ˇ (Ax). (A6) b, =, 2,...,m p x 0 In our proposd fuzzy prfrnc programmng, w only apply th fuzzy constrant programmng ( c ) (Eq. (A3)) wth th b qual to 0, (Ax) qual to (Rw) k or R k w and p qual to d k (Fgs. and A2). Appndx B. Th mn-max oprator A mult-obctv programmng (MOP) problm can b mathmatcally rprsntd as follows: max s.t. [f (x),f 2 (x),...f k (x)] Ax b. (B) x 0 Th comproms soluton mthod was orgnally proposd by Yu [36] n 973. Th basc da s to fnd th mnmum dstanc

10 3524 J.J.H. Lou t al. / Appld Soft omputng (20) * f 2 ( x) f 2 ( x) f 2 (x) Ngatv dal pont f ( x) * f ( x) Idal pont d p f (x) Fg. B. Eucldan dstancs to th dal soluton (asprd lvls) and ngatv-dal solutons (worst valus) n two dmnsons. (d p ) btwn fasbl solutons and dal pont (Fg. B). Th d p s dfnd as n Eq. (B2). [ k ( ) f d p = w p (x) f p ] /p (x) f (x) f. (B2) (x) hn th p =, Eq. (B2) can b xprssd as follows: k ( ) f d p= = w p (x) f (x) f = (x) f, (B3) (x) ( ) ] f d p= = max [w (x) f (x) =, 2,..., k f (x) f. (B4) (x) Usng th dfnton for d p dstanc as Eqs. (B3) and (B4), th mult-obctv programmng Eq. (B) can b transfrrd as follows: mn d x s.t. Ax b mn max d x f (x) f (x) or s.t. Ax b (B5) f (x) f d, =, 2,..., k (x) x 0 x 0 Rfrncs [] S. Albrs, B. Koch,. Ruff, Stratgc allancs btwn arlns and arportsthortcal assssmnt and practcal vdnc, Journal of Ar Transport Managmnt (2) (2005) [2] D. Holtbrugg, S. lson, N. 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