Contractor Selection Based on Swot Analysis with Vikor and Topsis Methods in Fuzzy Environment

Transcription

1 World Appled Scences Journal 24 (4): , 203 ISSN IDOSI Publcatons, 203 DOI: /dos.was Contractor Selecton Based on Swot Analyss wth Vkor and Topss Methods n Fuzzy Envronment 2 Mohammad Al Forghan and Leyla Izad Department of Management, Unversty of Shahd Bahonar, Kerman, Iran 2 Department of Industral Engneerng, Unversty of Payame Noor, Hamedan, Iran Submtted: Feb 8, 203; Accepted: Mar 5, 203; Publshed: Aug 8, 203 Abstract: In now compettve world outsourcng s one of mportant strateges used by modern enterprses. Contractor selecton s a mult crtera decson makng (MCDM) problem that comprses tangble and ntangble factors. Prevous contractor selecton technques do not consder strategc perspectve. Ths paper, s appled quantfed SWOT n the context of contractor selecton under a fuzzy envronment, for the frst tme. In proposed strategc method, strengths weaknesses opportuntes threats (SWOT) and ther sub-factors are dentfed n a MCDM herarchy. By usng fuzzy logc and trangular fuzzy numbers, human vagueness n decson makng s consdered. For evaluatng and selectng the best contractor n volunteered contractors, VIKOR method s used. Results are compromsed wth TOPSIS, well known rankng technque. Proposed method s a quantfed strategc method and deal wth mprecsely human thought also. Moreover t s nterestng for managers for ts appled SWOT analyss and applcable for every enterprse wth some changes. Key words: Contractor selecton Fuzzy logc MCDM Quantfed SWOT Strengths Weaknesses Opportuntes and Threats TOPSIS Trangular fuzzy numbers VIKOR INTRODUCTION Today s growng numbers of contractor selecton methodologes reflect the ncreasng for mprovng Durng the recent swft progress of network procurement process and performance [5]. technology and economc globalzaton, modern ndustry When researchers and practtoners have realzed that has been trendng towards the ncreasngly precse lowest-prce s not the promsng approach to attan the dvson of labor. Consequently, ndvdual enterprses overall lowest proect cost upon proect completon, focus on developng ther core capabltes and outsource mult-crtera selecton becomes more popular [6, 7]. non-core affars to other partners wth dfferent In the prevous research, several authors consdered professonal capabltes to upgrade ther compettve the contractor selecton problem. But most of them advantage by applyng these external and specal sources consdered contractor selecton n constructon ndustry and technology knowledge []. Companes try to reduce and other felds are dsregarded, whereas many other costs and manage rsks. It s mportant to know that one companes, such as energy generaton and dstrbuton of the maor portons of the frms expenses s related to companes, face to ths problem. More ever, most of logstcs actvtes whch mostly are more than 50% of all prevous nvestgatons ddn t pay attenton to strategc companes costs [2, 3]. The overall obectve of perspectve. SWOT (Strengths, Weaknesses, contractor selecton process s to reduce proect rsk, Opportuntes and Threats) s a useful technque whch maxmze overall value to the proect owner and buld the s commonly known n strategc management area. close and long term relatonshps between members of the SWOT analyzes the external opportuntes and threats proect. Contractor selecton consttutes a crtcal decson as well as the nternal strengths and weaknesses. for proect owners. The selecton process should embrace Besdes, t s one of the most famous tools for strategy nvestgaton of contractors potental to delver a servce formulaton. The goal of the analyss of external of acceptable standard, on tme and wthn budget [4]. opportuntes and threats s to evaluate whether a Correspondng Author: Mohammad Al Forghan, Department of Management, Unversty of Shahd Bahonar, Kerman, Iran. Tel:

2 World Appl. Sc. J., 24 (4): , 203 company can capture opportuntes and avod threats El-Sawalh [4] suggested a state-of-the-art model for when facng an uncontrollable external envronment such contractor s pre-qualfcaton by usng a hybrd model, as change n the rule of law [8, 3]. combnng the merts of Analytcal Herarchy Process In ths paper we used a MADM method to rankng. (AHP), Neural Network (NN) and Genetc Algorthm (GA) VIKOR s the useful rankng method consdered opposte n one consoldated model. crtera n decson makng. In other way the VIKOR Juan [5] proposed a systematc decson support method, a recently ntroduced new MCDM method approach to solve housng refurbshment contractor developed to solve multple crtera decson makng selecton problem by usng case-based reasonng (MCDM) problems wth conflctng and non- (CBR) and data envelopment analyss (DEA). commensurable (dfferent unts) crtera [9]. Darvsh et al. [7] showed how the graph theory and matrx In ths paper, we use quantfed SWOT analyss as a methods may be served as a decson analyss tool for decson tool to formulate strategc plans for contractor contractor selecton. selecton. To our knowledge, no one has appled SWOT Dolo et al. [6] establshed a herarchcal analyss n contractor selecton. In ths paper, we used the structural model to understand pre-emptve qualfcaton concept of fuzzy set theory and lngustc values to crtera and ther lnks to contractors' performance on a overcome uncertanty and qualtatve factors. Then, two proect, by employng the structural equaton modelng herarches MCDM model based on fuzzy sets theory and technque. SWOT analyss are proposed to deal wth the contractor Jaskowsk et al. [7] suggested the applcaton of selecton problems. Parwse comparson used n model, an extended fuzzy AHP method to the process of make the obtaned weghts of crtera are more precse. group decson makng n contractor selecton problem. Fuzzy logc has been ntegrated wth SWOT analyss to Watt et al. [8] used an expermental desgn approach to deal wth vagueness and mprecson of human thought. quantfy the mportance of nne common crtera used n The model appled n power of electrcty dstrbuton an actual evaluaton and selecton of a company also t s applcable to use n other companes. contractor/suppler. The proposed decson model s comprehensve and Ng & Tang [9] establshed a set of Crtcal Success compettve for contractor selecton due to ts dynamc Factors (CSFs) for constructon sub-contractors whch are nature and strategc orented. labor-ntensve n nature. The organzaton of ths paper s as follows: Secton 2 dscusses the lterature revew about contractor Quantfed Swot: SWOT (Strengths, Weaknesses, selecton, quantfed SWOT and Fuzzy set theory, Opportuntes and Threats) s one of the most VIKOR and TOPSIS methods are presented n Secton 3. well-known technques for conductng a strategc In Secton 4, methodology s llustrated. Case study s study [3]. SWOT analyss s a commonly used tool presented n Secton 5. Fnally, conclusons are presented for analyzng nternal and external envronments n n Secton 6. order to attan a systematc approach and support for a decson stuaton (e.g. [20, 2]). Kurttla et al. Lterature Revew [22] presented a hybrd method for mprovng the Contractor Selecton: Holt et al. [0] developed a usablty of SWOT analyss. AHP s connecton to quanttatve model for selectng constructon contractors SWOT yelds analytcally determned prortes for whch utlzes the mult-attrbute analyss (MAA) the factors ncluded n SWOT analyss and makes technque. Sonmez [] appled the evdental reasonng them commensurable. Yûksel and Dagdevren [23] (ER) approach (whch s capable of processng both usng Analytc Network Process (ANP), demonstrated a quanttatve and qualtatve measures) as a means of process for quanttatve SWOT analyss that can be solvng the contractor selecton problem (CSP). performed when there s dependence among strategc Hatush and Sktmore [2] proposed a systematc mult- factors. crtera decson analyss technque that s descrbed for Chang and Huang [8] presented a Quantfed SWOT contractor selecton and bd evaluaton based on utlty analytcal method whch provdes more detaled and theory and whch permts dfferent types of contractor quantfed data for SWOT analyss. The Quantfed capabltes to be evaluated. Chau et al. [3] tested how SWOT analytcal method adopts the concept of dfferent managers choose mantenance contractors. Multple-Attrbute Decson Makng (MADM), whch Ths n turn led them to focus on the dentfcaton of the uses a mult-layer scheme to smplfy complcated maor selecton attrbutes and the trade-off weghtngs problems and thus s able to perform SWOT analyss on among attrbutes durng the selecton process. several enterprses smultaneously. 54

3 Fuzzy Logc: Fuzzy set theory was ntroduced by Zadeh [24] n 965 to solve problems nvolvng the absence of sharply defned crtera. Because fuzzness and vagueness are common characterstcs n many decson-makng problems, good decson-makng models should be able to tolerate vagueness or ambguty [25]. Thus, f the uncertanty (fuzzness) of human decson-makng s not taken nto account, the results from the models can be msleadng. Fuzzy theory has been appled n a varety of felds snce ts ntroducton. Many fuzzy AHP methods are proposed to solve varous types of problems [26]. The man theme of these methods, s usng the concepts of fuzzy set theory and herarchcal structure analyss to present systematc approaches n selectng or ustfyng alternatves [27]. Zhu et al. [28] proves the basc theory of the trangular fuzzy number and mproves the formulaton of comparng the trangular fuzzy number's sze. The smple and popular method, centrod method s adopted to defuzzfy trangular fuzzy numbers [3, 29]. It should be mentoned that the above methodology s smple and easy to use for practtoners. A defuzzfed - + trangular fuzzy number (n, n, n ), s calculated by Eq. (). - + Defuzzfed number =/3 (n + n + n ) () MATERIALS AND METHODS Vkor Method: The mult-crtera mert for compromse rankng s developed from the Lp-metrc used n the compromse programmng method [30]. The compromse rankng method (known as VIKOR) s ntroduced as one applcable technque to mplement wthn the MCDM. Assumng that each alternatve s evaluated accordng to each crteron functon, the compromse rankng s performed by comparng the measure of closeness to the deal alternatve [3-33]. The VIKOR method was developed to solve MCDM problems wth conflctng and non-commensurable (dfferent unts) crtera, assumng that compromsng s acceptable for conflct resoluton, the decson maker wants a soluton that s the closest to the deal and the alternatves are evaluated accordng to all establshed crtera. Ths method focuses on rankng and selectng from a set of alternatves n the presence of conflctng crtera and on proposng compromse soluton (one or more). The VIKOR method s extended wth a stablty analyss determnng the weght stablty ntervals and wth trade-offs analyss. The extended VIKOR method s compared wth three mult-crtera decson makng methods: TOPSIS, PROMETHEE and ELECTRE [34]. The multple crtera decson makng (MCDM) methods VIKOR and TOPSIS are based on an aggregatng functon representng closeness to the deal, whch orgnated n the compromse programmng method. In VIKOR lnear normalzaton and n TOPSIS vector normalzaton s used to elmnate the unts of crteron functons. The VIKOR method of compromse rankng determnes a compromse soluton, provdng a maxmum group utlty for the maorty and a mnmum of an ndvdual regret for the opponent. The TOPSIS method determnes a soluton wth the shortest dstance to the deal soluton and the greatest dstance from the negatve-deal soluton, but t does not consder the relatve mportance of these dstances [33]. Sayad et al. [35] extend the VIKOR method for decson makng problems wth nterval number. The extended VIKOR method s rankng s obtaned through comparson of nterval numbers and for dong the comparsons between ntervals, They ntroduce á as optmsm level of decson maker. The VIKOR procedure conssts of the followng steps: - Determne the best f and the worst f = = f max f, f mn f, = = f mn f, f max f, S = n w ( f f ) /( f f ), = n R = max[ w ( f f ) /( f f )], f the -th detaled crtera represents Strengths or Opportuntes; f the -th detaled crtera represents Weaknesses or Threats. = + Q vs ( S) ( S S) ( v)( R R) ( R R), (6) (2) (3) Compute the values S and R, =,2,..., J, by the relatons, (4) (5) where w are weghts of detaled crtera that obtaned from DM s preferences. Compute the values Q, =,2,...,J, by the relaton 542

4 S = mn S, S max, = S R = mn R, R = max R; where and v s ntroduced as a weght for the strategy of maxmum group utlty, whereas v s the weght of the ndvdual regret. Rank the alternatves, sortng by the values S, R and Q n decreasng order. Propose as a compromse soluton the alternatve () (A ) whch s the best ranked by the measure Q (mnmum) f the followng two condtons are satsfed: C. Acceptable advantage: (2) () Q(A ) Q(A ) DQ, (2) where A s the alternatve wth second poston n the rankng lst by Q; DQ = /(J ). C2. Acceptable stablty n decson makng: () The alternatve A must also be the best ranked by S or/and R. Ths compromse soluton s stable wthn a decson makng process, whch could be the strategy of maxmum group utlty (when v>0.5 s needed), or by consensus v 0.5v, or wth veto (v < 0.5). Here, v s the weght of decson makng strategy of maxmum group utlty. Here, v s the weght of decson makng strategy of maxmum group utlty. If one of the condtons s not satsfed, then a set of compromse solutons s proposed, whch conssts of () (2) Alternatves A and A f only the condton C2 s not satsfed, or () () (M) Alternatves A, A,...,A f the condton C s not (M) (M) satsfed; A s determned by the relaton Q(A ) () Q(A ) < DQ for maxmum M (the postons of these alternatves are n closeness ) [34]. Topss Method: TOPSIS (technque for order preference by smlarty to an deal soluton) s one of the useful technques to manage real-world problems [36]. TOPSIS method s presented n Chen & Hwang [37], wth reference to Hwang, C.L., Yoon [38]. Accordng to ths technque, the best would be the one that s nearest to the postve deal soluton and farthest from the negatve deal soluton [39, 40]. The TOPSIS procedure conssts of the followng steps: J r 2 = f f, =,, J ; =,, n. = v = wr, =,, J; =,, n, Calculate the normalzed decson matrx. The normalzed value r s calculated as n A = { v,, v } = {(max v I ),(mn v I )}, A = { v,, v } = {(mn v I ),(max v I )}, n n D ( 2 = v v ), =,, J. = n D ( ) 2 = v v, =,, J. = C = D ( D + D ), =,, J. (7) Calculate the weghted normalzed decson matrx. The weghted normalzed value v s calculated as wherews the weght of the th attrbute or crteron and (8) n = w =. Determne the deal and negatve-deal soluton. (9) (0) where I' s assocated wth beneft crtera and I s assocated wth cost crtera. Calculate the separaton measures, usng the dmensonal Eucldean dstance. The separaton of each alternatve from the deal soluton s gven as () Smlarly, the separaton from the negatve deal soluton s gven as (2) Calculate the relatve closeness to the deal soluton. The relatve closeness of the alternatve a wth respect to A s defned as Rank the preference order [33]. (3) 543

5 Fg. : SWOT herarchy Fg. 2: Lngustc scale for parwse comparson Table I: Characterstc functon of the fuzzy numbers Fuzzy number Membershp functon x (x-2, x, x+2) x (3,, ) 9 (9,9,7 ) ((X+2), x,(x 2) ) Methodology: A systematc fuzzy model for contractor selecton s proposed n ths secton. The steps are summarzed as follows: Step. Step 2. Form the commttee of experts: n the frst step t s necessary to form a commttee of experts n contractor selecton n the company and defne the problem and model. Model a SWOT herarchy Model a SWOT herarchy: model a herarchy wth contractor selecton as goal n frst level, SWOT merts are n second level; form sub-crtera for each mert n thrd level and lowest level contans the alternatves (contractors) that are under evaluaton. SWOT herarchy s sown n Fg.. Step 3. Publsh an advertsement: The members of commttee decde publsh an advertsement n newspaper to dentfy the ter supplers who are nterested to contrbute n the proect. The team announce requrements such as fnancal ablty,... Step 4. Determne the prortes of SWOT and subcrtera n problem: Formulate the questonnare and gve to experts to fll them. Ths step s very mportant and tme consumer. When the number of canddates and crtera grows, the parwse comparson process becomes cumbersome and the rsk of generatng nconsstences grows. In addton, AHP, lke as many systems whch work based on parwse comparsons, can produce rank reversal results [4]. Then n ths study we ddn t use par wse comparson between crtera n MCDM herarchy. Step 5. Determne the mportance of SWOT, sub-crtera and detaled crteron, wth respect to upper level, usng lngustc varables. Lngustc varables set s consst of "equal mportant (E), rather mportant (R), mportant (I), very mportant (V) and absolutely mportant (A) ". These lngustc varables are quantfed as fuzzy numbers shown n Fg. 2. Fuzzy relatons and membershp functon n ths secton are shown n Table I: 544

6 Fg. 3: SWOT herarchy for Electrc Company Step 6. Calculate crsp relatve weghts for SWOT merts A commttee ncludng three experts from engneerng and sub-crtera: Calculate crsp relatve and commercal departments n Electrc Company s mportance weghts of SWOT merts and sub- formed. The research scope s n transton lnes crtera and detaled crtera from expert's constructors n Iran. opnons. (l t, m t, u t) s the fuzzy mportance Wth revew of lterature, consultaton wth experts weght form expert t. A trangular fuzzy number s and consderaton of documents, SWOT herarchy (Fg. 3) obtaned by aggregatng the expert's optons. s organzed. Then defuzz obtaned number for each of SWOT An advertsement s publshed n newspaper to factors and sub-crtera by centrod method Eq. dentfy the ter contractors who are nterested to (). contrbute n the proect and requrement s announced. Step 7. Model a questonnare and determne scores of The questonnares are prepared and targeted on contractors: formulate a questonnare based on experts to fll. Fuzzy mportance relatve for man crtera s Fg. to consder preferences and scores of the establshed based on the parwse comparson results. contractors wth respect to detaled crtera and For example, the parwse comparson results between gve to experts to fll them. Usng the lngustc qualty and suffcent delvery are (3,5,7), (,3,5), (5,7,9). varables that shown n Fg. 2. The experts opnons are as below: Step 8. Calculate crsp score relatve contractors: Ths step s performed by usng smlar procedure n step 5. Step 9. Use VIKOR to select the best contractor: select n = (3 5) 3 = the best contractor usng VIKOR method mentoned n Secton 3.. n = (5 3 7) 3 = 4.78 Step 0. Use TOPSIS to select the best contractor: select the best contractor usng TOPSIS method mentoned n Secton 3.2. Step. Compare the results of VIKOR and TOPIS n + = (7 5 9) 3 = methods. Crsp weghts of SWOT, sub-crtera and detaled crtera are obtaned by usng Eq. (), results are shown n Case Study: A case study s presented n ths secton TABLE II. to demonstrate the practcalty of proposed model. Score of every detaled crtera s provded by The model examned n Electrcty Company of South decson makers. Average of decson makers Kerman. opnons s the score of detaled crtera whch s a 545

7 Table II: Relatve prortes of crtera (S,W, O, T), Sub-crtera, Detaled crtera no Factors Sub-crtera Local weghts Detaled crtera Local weghts Fnal prortes Strengths(0.269) Qualty Qualty of stuff Qualty systems Experts& sklled personnel Suffcent equpment Delvery Delvery tme Delvery hstory Geographcal locaton Cooperaton n natural accdents Weaknesses (0.256) Cost Opportuntes (0.22) Relatonshp development Jont product/knowledge development Acquston of contractor s experts Technology and knowledge Technologcal systems Future technology development Threats (0.253) Fnancal constrant Low captal Low asset Bad performance hstory 0.44 Earler employer s dssatsfacton Accdent s redounded to death Insuffcent hstory Bad performance n defect correcton durng& after work Table III: Scores of each contractor n detaled crtera f f and, A, A values Contractor Contractor 2 Contractor Detaled crtera Fuzzy number Crsp value Fuzzy number Crsp value Fuzzy number Crsp value f f A A (.5,3,5) 3.7 (2,4,6) 4 (4,6,8) (0.33,,2.33).22 (2.33,4.33,6.33) 4.33 (2,3.67,5.67) (3,5,7) 5 (4.33,6.33,8.33) 6.33 (5.67,7.67,9) (6.33,7.67,9.33) 7.78 (5.67, 7.67,9.33) 7.56 (5,7,8.67) (4.33,6.33,8.33) 6.33 (4.33, 6.33,8.33) 6.33 (7,8.67,9.67) (5,7,8.67) 6.89 (7,9,0) 8.67 (5,7,8.67) (7,8.67,9.67) 8.44 (3,5,7) 5 (2.67,4.33,6.33) (5,7,8.67) 6.89 (.33,3,5) 3. (3,5,7) (3,5,7) 6.89 (2.33,4.33,6.33) 4.33 (3,5,7) (3.67,5.67,7.33) 5.56 (3,5,7) 5 (5.67,6.67,9.33) (3.67,5.67,7.33) 5.56 (3.67,5.67,7.67) 5.67 (5.67,7.67,9.33) (3,5,7) 5 (3.67,5.67,7.67) 5.67 (3.67,5.67,7.67) (3.67,5.67,7.67) 5.67 (4.33,6.33,8.33) 6.33 (5.67,7.67,9.33) (2.33,4.33,6.33) 4.33 (.33,3,5) 3. (2,3.67,5.67) (3,5,7) 5 (.67,3.67,5.67) 3.67 (2,3.67,5.67) (2.33,4.33,6.33) 4.33 (,3,5) 3 (.33,3,5) (2.67,4.33,6.33) 4.44 (0,0.67,2.33) (0,0.33,.67) (.33,2.67,4.33) 2.78 (,3,5) 3 (0.67,2.33,4.33) (0.33,.67,3.67).89 (.33,3,5) 3. (0.67,2.33,4.33) Table IV: Results obtaned VIKOR and TOPSIS Altenatves Contractor Contractor 2 Contractor 3 Rankng VIKOR S ,2, R ,2, Q ,2, TOPSIS D ,2, D ,2, C ,2, 546

8 trangular fuzzy number, these fuzzy numbers are defuzzed by Eq. () and crsp score for each contractor s obtaned. Values of, for each contractor n detaled f f crtera are calculated and shown n Table III. Fnally contractors s ranked by VIKOR. The values of S, R, Q are calculated for all contractors from Eq. (4)- (6) as shown n TABLE IV. The rankng of the contractors by S, R, Q n ncreasng order s shown n TABLE IV. As we see n Table, the contractor 3 s the best ranked by S, R, Q. So contractor 3 s the best choce. Moreover values of A and A for each detaled crtera are obtaned from Eq. (9), (0). Results are shown n TABLE III. Then D, C and D are calculated from Eq. ()- (3). The rankng of the contractors by D, C n decreasng order and by D n ncreasng order s shown n TABLE IV. As we see n Table, the contractor 3 s the best ranked by D, C and C. So contractor3 s the best choce. As t can be seen n TABLE IV, contractor rankng s 3, 2, and contractor 3 s the best contractor n both methods. Obtaned results show that two methods construct the same rankng, wth ther dfferences n utlty functon. Contractor 3 has external opportuntes for development and potentally has nternal competng strength to get the opportuntes. Therefore, t can be concluded that ths contractor s n the best poston for facng competton. Contractor 2 (n the thrd quadrant) has low compettve strength and facng threats from other compettors. CONCLUSION Durng the recent swft progress of network technology and economc globalzaton, modern ndustry has been trendng towards the ncreasngly precse dvson of labor. Consequently, ndvdual enterprses focus on developng ther core capabltes and outsource non-core affars to other partners wth dfferent professonal capabltes. Companes try to reduce costs and manage rsks. It s mportant to know that one of the maor portons of the frms expenses s related to logstcs actvtes whch mostly are more than 50% of all companes costs. The overall obectve of contractor selecton process s to reduce proect rsk, maxmze overall value to the proect owner and buld the close and long term relatonshps between members of the proect. The am of ths paper s to propose a model based on quantfed SWOT n fuzzy envronment to solve contractor selecton problem. In proposed strategc method, a strengths, weaknesses, opportuntes, threats (SWOT) herarchy s establshed. Weghts of attrbutes and score matrx obtaned from decson makers opnon, whch are n lngustc varable form. Wth aggregaton decson makers opnon, fuzzy decson matrx s obtaned. Then the contractors rankng s done by two useful rankng methods, VIKOR and TOPSIS, then results are compared. A case study (n Electrcty Company of South regon of Kerman) s presented to demonstrate the practcalty of proposed model. Proposed method s a quantfed strategc method and deal wth mprecsely human thought also. Moreover t s nterestng for managers for ts appled SWOT analyss. Ths model s applcable for every enterprse wth some changes. ACKNOWLEDGMENT The researchers wsh to express ther grattude to the Electrcty Dstrbuton Company of South regon of Kerman for ther nformatonal supports. REFERENCES. Chen, Y.J., 20. Structured methodology for suppler selecton and evaluaton n a supply chan. Informaton Scences, Artcle n press. 2. Assaou, N., M. Haouar and E. Hassn, Suppler selecton and order lot szng modelng: A revew. Computers & Operatons Research, 34(2): Hassanzadeh Amn, S., J. Razm and G. Zhang, 20. Suppler selecton and order allocaton based on fuzzy SWOT analyss and fuzzy lnear programmng. Expert Systems wth Applcatons, 38: Topcu, Y.I., A decson model proposal for constructon contractor selecton n Turkey. Buldng and Envronment, 39: Wong, C.H., J. Ncholas and G.D. Holt, Usng multvarate technques for developng contractor classfcaton models. Engneerng, Constructon and Archtectural Management, 0(2): Cheng, E.W.L. and H. L, Contractor selecton usng the analytc network process. Constructon Management and Economcs, 22: Darvsh, M., M. Yasae and A. Saeed, Applcaton of the graph theory and matrx methods to contractor rankng. Internatonal Journal of Proect Management, 27:

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