DESIGN OF FUZZY DECISION SUPPORT SYSTEM (FDSS) IN TECHNICAL EMPLOYEE RECRUITMENT

Transcription

1 DESIGN OF FUZZY DECISION SUPPORT SYSTEM (FDSS) IN TECHNICAL EMPLOYEE RECRUITMENT Dpka Pramank NetajSubhashEngneerngCollege Mal d: Anupam Haldar NetajSubhashEngneerngCollege Mal d: Abstract:Now-a-days employee recrutment s one of the most mportant assets of any companes. The am of study s developng a fuzzy decson support system (FDSS) to select approprate employee n techncal feld by takng some qualtatve judgments of decson makers nto consderaton. Proposed approach s based on Fuzzy Analytc Herarchy Process (FAHP) and Technque for Order Preference by Smlarty to Ideal Soluton (TOPSIS) methods. FAHP method s used n determnng the weghts of the crtera by decson makers and then rankngs of the techncal employee are determned by TOPSIS method. Key words: employee recrutment, Fuzzy Decson Support System, Fuzzy Analytc Herarchy Process, Fuzzy TOPSIS. I. INTRODUCTION Today, the most mportant human resource factor and ts unque role as the strategc recourse n the organzaton become more tangble than the past tme. Fndng the best possble canddate who can ft wthn the culture and contrbute n an organzaton s a challenge and an opportunty. Keepng the best canddate, once we fnd them, s easy f we do the rght thngs rght. These specfc actons wll help for recrutng and retanng all the talent company need. Employee or personnel performances such as capablty, knowledge, skll, and other abltes play an mportant role n the success of an organzaton. Numerous topcs have been addressed by researchers and a large number of studes have been publshed n ths regard [7, 9, 12 and 13]. The objectve of all paper s a selecton process whch depends manly on assessng the dfferences among canddates and predctng the future performance. The selecton of qualfed canddates for certan posts s one of the most dffcult tasks ether n larger or smaller companes and requres an organzed system. Indeed, one of the major concerns of Human resource managers s the subject of techncal employee recrutment. In the past, organzatons had consderably stressed on the employment test. Under many condtons, crsp data are nadequate to model real-lfe stuatons. Snce human judgment ncludng preferences are often vague and cannot estmate hs preference wth an exact numercal value. A more realstc approach may be to use lngustc assessments nstead of numercal values. Wth ths respect, fuzzy logc gets a poston at human resource. As t s mentoned most dmensons of human resource are qualtatve varables. Chuu [3] develops fuzzy multple attrbute decson makng appled n group decson makng to mprove the advanced manufacturng technology selecton process.the decson support system (DSS) s a computer-based nformaton system that combnes models and data n an attempt to solve unstructured problems wth extensve user nvolvement through a frendly user's nterface (Laudon,2006). An ntegrated Fuzzy TOPSIS (FTOPSIS) method s proposed to mprove the qualty of decson makng for rankng alternatves by Dng [6]. FAHP, FTOPSIS methods are used by varous researchers [2, 7, 9, 10, 12 and 13] n dfferent selecton processes. Nezhad and Damghan [11] proposed an algorthm based on TOPSIS approach and an effcent fuzzy dstance measurement for a Fuzzy Multple Group Decson-Makng Problem whch has effcently been appled n assessment of traffc polce centers. Das et. al. [5] presented FTOPSIS n comparng techncal nsttutes. Haldar et. al. [8] presented a smplfed Fuzzy TOPSIS method for selectng reslent supplers. In ths paper we are plannng to develop a generc fuzzy decson support system whch removes the weakness of employment test. Then fuzzy TOPSIS s used to develop a fuzzy decson support system (FDSS). By comparson of concluson of two mentoned methods, we approved the relablty and valdty of FDSS. The remander of the paper s organzed as follows: Secton II depcts model desgn of techncal employee recrutment. 5

2 Secton III depcts AHP and TOPSIS methodology In the Secton IV, V, VI and VII fuzzy multple crtera decson makng (MCDM) methods are dscussed. Secton VIII and IX depcts the methodology of ths research work and problem solvng steps. In the last secton dscusson and concluson s gven. II. MODEL DESIGN OF TECHNICAL EMPLOYEE RECRUITMENT In the present work, researchers tred the best to desgn the framework for techncal employee recrutment contrastng the tradtonal mportance of the common employee tests methods. Table 1 shows some of major test crtera whch are common n employee recrutment n techncal feld n an organzaton. The crtera and sub crtera are adopted from those tests. Table 1: Major test crtera for techncal employee recrutment Assessment Test Int erpersonal Skll, Co mmuncaton Skll, Pla nnng and Organzng, An alytcal Skll Bographca l Test Educat on, Trann g, Work experence, Team work, Gender, Age lmt Integrty Test P erson honesty, D ependablty, R elablty, P ro-socal behavor Personalty Test Op tmsm, Agreeableness, Ser vce Orentaton, Stress tolerance, Em otonal stablty, Int atve, Interperso nal nteracton Job Knowledg e Test T echncal Expertse, P rofessonal expertse, K nowledge for job III. ANALYTIC HIERARCHY PROCESS (AHP) The concept of Analytc Herarchy Process (AHP), was ntroduced by Saaty (1988), AHP s a robust, multcrtera decson analyss tool for dealng wth complex, unstructured mult-crtera decsons. Subjectve weghts are determned only accordng to the preference of decson makers. The comparsons are recorded n a square and recprocal matrx. The basc steps of AHP are: () Descrbng a complex decson-makng problem as a herarchy; () Developng Par-wse comparson Matrx (PWCM); () Estmatng relatve weghts of the PWCM elements; (v) Checkng the consstency of decson varables; Determnaton of the Consstency Rato (C.R.) An egen-vector (λ) for each row alternatve s computed to determne the Consstency Index (C.I.) C.I. = (λ max N) / (N 1)..(1) where, N s the dmenson of the matrx and λ s the egen-value. The C.R. for each of the matrces s checked usng the followng relaton: C.R. = C.I. / R.I... (2) where, R.I. s the Random Consstency Index, whch depends on the number of alternatves (Ref. Table 2). Table 2: Random Consstency Index correspondng to order of the matrx Dmenson, n R.I The consstency of the par wse comparson matrx can be measured by calculatng a consstency rato. A consstency rato of less than 0.1 should be obtaned otherwse t should be re-evaluated. However AHP s unable to handle uncertanty decsons n a comparson of the attrbutes. IV. FUZZY MULTIPLE CRITERIA DECISION MAKING (MCDM) WITH LINGUISTIC TERMS The decson problem for selectng a techncal employee can be descrbed as a complex, multobjectve task, based on uncertan test, n whch the alternatves are the canddates to be selected and the crtera are those attrbutes under consderaton. In tradtonal MCDM,alternatve ratngs and weghts are measured n crsp numbers that depend on decson makers judgments. Lngustc varables are used to descrbe the degrees of a crteron specfcally as crsp data are nadequate to model real lfe stuatons n MCDM. In practce, alternatve ratngs and crtera weghts cannot be assessed precsely for varous reasons, such as: (1) unquantfable nformaton, (2) unobtanable nformaton, (3) partal gnorance, and (4) ncomplete nformaton. Fuzzy MCDM approaches are proposed to deal wth the nherent vagueness and mprecson n our study. Bellman and Zadeh [1, 14] frst ntroduced fuzzy set theory nto MCDM as an approach for dealng effectvely wth the nherent mprecson, vagueness and ambguty of the human decson makng process. Fuzzy tools provde a smplfed platform where the development and analyss 6

3 of models requre reduced development tme compared wth other approaches. As a result, fuzzy tools are easy to mplement and modfy. A lngustc varable s a varable that apples words or sentences n natural or artfcal language to descrbe degrees of value, and we use ths knd of expresson to compare each crteron by lngustc varables n a fuzzy envronment wth respect to a seven level fuzzy scale. Trangular fuzzy numbers are used to represent approxmate values for selecton crtera and are denoted as (a1, b1, c1) where 0 a1 b1 c1 10. Tables 3 defne the lngustc terms and show the membershp functons of these lngustc terms as trangular fuzzy numbers. V. TRIANGULAR FUZZY NUMBER In applcatons t s often convenent to work wth trangular fuzzy numbers (TFNs) because of ther computatonal smplcty, and they are useful n promotng representaton and nformaton processng n a fuzzy envronment. A trangular fuzzy number, M s shown n Fg 2: f(x) 1 0 Fg 2. A Trangular fuzzy number, M Trangular fuzzy numbers form a specal class of fuzzy numbers whose membershp s defned by three real numbers, expressed as (a, b, c) wth membershp functon gven below: f A (x)=(x-a) / (b-a) for a x b, =(x -c) / (b-c) for b x c, =0 otherwse (3) (a) Trangular fuzzy numbers are used n the nterval [0,10] for defnng the lngustc varables for the mportance weght of each crteron. (b) Lngustc varables used n the nterval [0, 10] for defnng the lngustc varables for the mportance weght of each crteron are shown n Table 3. Table 3. Lngustc varables and membershp n the nterval [0,10]. x Importance VI.SIMPLE AGGREGATE METHOD USING TRIANGULAR FUZZY NUMBER Assumng a decson group has K persons, the mportance of the crtera and the ratng of alternatve canddates wth respect to each crteron can be calculated as: 1 [ 1 ( ) 2 ( )...( ) K j j j j ]...(4) w w w w K Where, x k j and w K j are the smple aggregate ratngs of alternatves and the mportance weghts of the k th decson maker. (+) ndcates the fuzzy arthmetc summaton functon. VII. FUZZY TOPSIS USING TRIANGULAR FUZZY NUMBERS Techncal employee recrutment model s a group multple-crtera decson-makng (GMCDM) problem, whch may be descrbed by means of the followng sets: I. A set of K decson-makers called E = {DM1; DM2; ; DMK}; II. A set of m applcants called F = {A; B; C;... } III. A set of n crtera, C = {C1; C2;... ; Cn} IV. A set of s Sub crtera, S = {C11; C12;... ; C21; C22;.; C31; C32.} Step 1:A fuzzy mult-crtera group decson-makng problem can be concsely expressed n matrx format as: x x... x x x... x D x x... x n n m1 m1 mn 1 2 Abbrevaton Fuzzy number Very Poor VP (0,0,2) Poor P (0.5,2,3.5) Medum Poor MP (2,3.5,5) Far F (3.5,5,6.5) Medum Good MG (5,6.5,8) Good G (6.5,8,9.5) Very Good VG (8,10,10)...(5) W [ w, w,... w ]...(6) n 7

4 The mportance weght of each crteron can be obtaned by assgnng ether drectly or ndrectly usng parwse comparsons. Decson makers used the lngustc varables shown n Tables 3 to evaluate the mportance of the crtera and the ratngs of alternatves wth respect to varous crtera. Step 2: The normalzed fuzzy decson matrx s formed usng Equatons (8), (9), (10) and (11) and t s denoted by R: Rj [ rj ] mn...(7) Where, B and C are the set of beneft crtera and cost crtera, respectvely, and aj bj cj j * * * c j c j c j r (,, ), j B;...(8) a a a j cj bj aj j j j r (,, ), j C;...(9) c max c, f j B;...(10) * j j a mn a, f j C;...(11) j j Step 3:Now the weghted normalzed decson matrx s formed usng Equatons (12) and (13): V v j, 1,2,..., n (12) mn v r w (13) j j j Step 4: Sortng of the fuzzy postve deal soluton (FPIS), A + and the fuzzy negatve deal solutons (FNIS), A- are determned usng Equatons (14) and (15): A v, v,..., v (14) n A v, v,..., v (15) Step 5: Calculaton of the separaton measure. Calculate the dstance of each alternatve from the postve deal soluton and the negatve deal soluton. Accordng to Dalah, et. al., [4] the dstance between two trangular fuzzy numbers A 1 = (a 1,b 1,c 1 ) and A 2 = (a 2,b 2,c 2 ) s calculated usng (16), as: d( A1, A2 ) a1 a2 b1 b2 c1 c2 (16) 3 k j j j1 k j j j1 d d v, v, 1,2,..., m (17) d d v, v, 1,2,..., m (18) Step 6: The closeness coeffcent (CC ) for each of the n canddate alternatves s determned usng equaton (17): d CC, 1,2,..., m (19) d d VIII. METHODOLOGY In desgnng a Fuzzy Decson Support System n Techncal Employee Recrutment, the steps are as follows Step1: Elmnate nsgnfcant Sub-crtera Step2: Weghts of the crtera are determned usng AHP. Step3: Determnaton of combned weghts of the Subcrtera. Step4: Determnaton of Aggregated Fuzzy Weghts (AFW) of the alternatves wth respect to each subcrteron. Step5: Determnaton of Normalzed AFW of alternatveswth respect to each sub-crteron. Step6: Normalzed fuzzy weght of the each subcrteron. Step7:Apply Fuzzy TOPSIS to rank canddate alternatves. Step8: Rankng of the canddate alternatves. IX. PROBLEM SOLVING Step1:Reduce the Sub-crtera usng AHP Base on fndngs, we develop the techncal employee recrutment model. By studes of tests, we found fve major crtera and twenty three sub-crtera whch are shown n Table 1. These crtera and sub-crtera are most emphaszed factors at dfferent organzaton. AHP s used n determnng the preference weghts of each sub-crteron.we dentfed the nsgnfcant sub-crtera whch has the preference weght less than a cut-off value and removed concerned nsgnfcant sub-crtera. An elmnaton process of the sub-crtera under Bographcal test s demonstrated below: As the value of preference weght of sub-crtera: Gender, Tranng, Team Work are very low.e. less than the predefned cut-off value (0.1), so these three sub-crtera are consdered as nsgnfcant. So, the revsed preference weghts are calculated as: 8

5 Table 5: Revsed sub-crtera and correspondng PWCM C1 C2 C3 C4 C5 Preference Weght C1 1 1/5 1/3 1/ C Step2: Determnaton ofweghts of the crtera The preference weghts of selecton crteron s calculated usng Analytcal Herarchy Process (AHP) and are shown n Table 6. Table 6: Preference weght of selecton crteron C3 3 1/2 1 1/ C4 4 1/ C5 1/4 1/8 1/4 1/ Educaton Age lmt Work exp. Pref. wt Educaton 1 1/ Age lmt Work exp. 1/3 1/ Those sub-crtera whch exceed the cut-off value of preference weght are consdered as qualfyng subcrtera for Techncal Employee Recrutment desgnng process.the reduced number of sub-crtera led to nneteen and s shown n Fg 1, n the herarchcal structure of Techncal Employee Recrutment Model. GOAL Assessment Test (C1) Interperso Educa Person Techncal Stress nal Skll, ton, honesty, Expertse, tolerance, Commun Work Dependab Professo Emotona caton - lty, nal l stablty, Skll, exper Relablty expertse, Intatve, Plannng ence,, Knowled Interperso and Age Pro-socal ge for job nal Organzn lmt behavor nteracto g, Table 4: Determnaton the weght of sub-crtera n, of Bo-graphcal test Analytcal Skll Consoldated rankng of Techncal Employee Bographcal Test (C2) Integrt y Test (C3) Job Knowledge Test (C4) A B C ALTERNATIVES Personalty Test (C5) Optmsm, Fg 1: Herarchcal Structure of Fnalzed and Subcrtera Educat on Age lmt Work exp Gende r Tran ng Team work Ed uc at on Age lm t Wor k exp. Gen der Tra n ng Tea m wor k / /3 1/ /6 1/6 1/ /2 1/6 1/6 1/4 1/2 1 1/4 1/5 1/5 1/ Pref. wt Step3: Combned weghts of the Sub-crtera are determned usng AHP. Now preference weghts of the sub-crtera are determned usng AHP and tabulated n Table 7. The product of crtera weght & sub-crtera weght produces the combned weght of each sub-crteron. Table 7: Combned preference weght of sub-crteron C1 C2 C3 C4 C5 Sub- Weght of crtera Weght of sub-crtera Combned weght of each subcrtera C C C C C C C C C C C C C C C C C C C

6 Step4: Aggregate Fuzzy Weghts of the alternatves wth respect to each sub-crteron s determned. Each DM rates each alternatve wth respect to each sub-crteron and the results are tabulated n Table 8. DMs recommendatons are appled n fuzzy lngustc terms whch are converted n fuzzy numbers usng Table 3. By applyng equatons (4) and (5), the aggregate fuzzy weghts for DMs (AFW) for each alternatve are determned wth respect to each subcrteron and shown n Table 9. Step5: Normalzed AFW of alternatveswth respect to each sub-crteron. Normalzed AFW of each alternatvewth respect to each sub-crteron are determned usng equaton (7) and (9) and tabulated n column 5 of Table 9. Step6: Normalzed fuzzy weght of the each subcrteron. Normalzed fuzzy weght of the each sub-crteron s determned and s tabulated n column 6 of Table 9. Step7:Apply Fuzzy TOPSIS to rank canddates. Now weghted normalzed decson matrx s formed usng equaton (15). As shown n column 7 of Table 9. Table 10 shows Fuzzy weght of applcants comparable to sub-crtera.fuzzy postve deal soluton (FPIS), A + and the fuzzy negatve deal solutons (FNIS), A- are determned usng Equatons (13) and (14). The separaton measure s computed usng equaton (15), (16) and (17) and shown n Table 11. Step8: Rankng of the canddate alternatves. Rankng of the canddate alternatves s done on the bass of closeness coeffcent (CC ) of each of the alternatves are computed usng equaton (18) whch results the rankng of the alternatves. Table 8: Lngustc terms for each crteron weght and aggregate Fuzzy weght (AFW) of each crteron. C1 C2 C3 C4 C5 Sub C11 C12 C13 C14 C21 C22 C23 C31 C32 C33 C34 C41 C42 C43 C51 C52 C53 C54 C55 Alternatves DM1 DM2 DM3 DM4 A VP VG F G B G F P VG C MG G VG VP A F VP G P B P VG F G C F MG VP P A VP F MG MP B MP P MP VP C G F VP VG A VP MP G F B F VP MP MG C VG MP G VP A MG G VP F B VP MP G G C P VP VG VP A VG G F P B MP VP G VG C F VG MG MP A G F VP VG B P G VG F C MP VP P G A VG VP G MG B F G MP VP C VP MG VG F A VG P G VG B MP G P VG C VP P VG P A G P MG P B VG VP G VG C F MG P G A MP G VP MG B G F MP P C VP VG F G A F F VG VP B G MP P VG C MG VP MP G A F MG G VP B VP F VG P C F VG F P A F P VP MG B G MP P VP C F VP VG F A F MG MP MG B VP VG G VG C VG F VP VG A G VP P F B VG MP VP VG C P G MP VP A F MG VG P B MP VP G F C P F VP MP A VP P G VG B F MP P VP C MG VP P MG A P F VG G B F P VP MP C VP VG MG F 10

7 Table 9: Aggregate value of fuzzy trangular number [0, 10] for each canddate by assessment of decson maker C1 C2 C3 C4 Sub C11 C12 C13 C14 C21 C22 C23 C31 C32 C33 C34 C41 C42 C43 Alternatves DM1 DM2 DM3 DM4 AFW of alternatves A (0,0,2) (8,10,10) (3.5,5,6.5) (6.5,8,9.5) (4.5,5.75,7) B (6.5,8,9.5) (3.5,5,6.5) (0.5,2,3.5) (8,10,10) (4.625,6.25,7.375) C (5,6.5,8) (6.5,8,9.5) (8,10,10) (0,0,2) (4.875,6.125,7.375) A (3.5,5,6.5) (0,0,2) (6.5,8,9.5) (0.5,2,3.5) (2.625,3.75,5.375) B (0.5,2,3.5) (8,10,10) (3.5,5,6.5) (6.5,8,9.5) (4.625,6.25,7.375) C (3.5,5,6.5) (5,6.5,8) (0,0,2) (0.5,2,3.5) (2.25,3.375,5) A (0,0,2) (3.5,5,6.5) (5,6.5,8) (2,3.5,5) (2.625,3.75,5.375) B (2,3.5,5) (0.5,2,3.5) (2,3.5,5) (0,0,2) (1.125,2.25,3.875) C (6.5,8,9.5) (3.5,5,6.5) (0,0,2) (8,10,10) (4.5,5.75,7) A (0,0,2) (2,3.5,5) (6.5,8,9.5) (3.5,5,6.5) (3,4.125,5.75) B (3.5,5,6.5 (0,0,2) (2,3.5,5) (5,6.5,8) (2.625,3.75,5.375) C (8,10,10) (2,3.5,5) (6.5,8,9.5) (0,0,2) (4.125,5.375,6.625) A (5,6.5,8) (6.5,8,9.5) (0,0,2) (3.5,5,6.5) (3.75,4.875,6.5) B (0,0,2) (2,3.5,5) (6.5,8,9.5) (6.5,8,9.5) (3.75,4.875,6.5) C (0.5,2,3.5) (0,0,2) (8,10,10) (0,0,2) (2.125,3,4.375) A (8,10,10) (6.5,8,9.5) (3.5,5,6.5) (0.5,2,3.5) (4.625,6.25,7.375) B (2,3.5,5) (0,0,2) (6.5,8,9.5) (8,10,10) (4.125,5.375,6.625) C (3.5,5,6.5) (8,10,10) (5,6.5,8) (2,3.5,5) (4.625,6.25,7.375) A (6.5,8,9.5) (3.5,5,6.5) (0,0,2) (8,10,10) (4.5,5.75,7) B (0.5,2,3.5) (6.5,8,9.5) (8,10,10) (3.5,5,6.5) (4.625,6.25,7.375) C (2,3.5,5) (0,0,2) (0.5,2,3.5) (6.5,8,9.5) (2.25,3.375,5) A (8,10,10) (0,0,2) (6.5,8,9.5) (5,6.5,8) (4.875,6.125,7.375) B (3.5,5,6.5) (6.5,8,9.5) (2,3.5,5) (0,0,2) (3,4.125,5.75) C (0,0,2) (5,6.5,8) (8,10,10) (3.5,5,6.5) (4.125,5.375,6.625) A (8,10,10) (0.5,2,3.5) (6.5,8,9.5) (8,10,10) (5.75,7.5,8.25) B (2,3.5,5) (6.5,8,9.5) (0.5,2,3.5) (8,10,10) (4.25,5.875,7) C (0,0,2) (0.5,2,3.5) (8,10,10) (0.5,2,3.5) (2.25,3.5,4.75) A (6.5,8,9.5) (0.5,2,3.5) (5,6.5,8) (0.5,2,3.5) (3.125,4.625,6.125) B (8,10,10) (0,0,2) (6.5,8,9.5) (8,10,10) (5.625,7,7.875) C (3.5,5,6.5) (5,6.5,8) (0.5,2,3.5) (6.5,8,9.5) (3.875,5.375,6.875) A (2,3.5,5) (6.5,8,9.5) (0,0,2) (5,6.5,8) (3.375,4.5,6.125) B (6.5,8,9.5) (3.5,5,6.5) (2,3.5,5) (0.5,2,3.5) (3.125,4.625,6.125) C (0,0,2) (8,10,10) (3.5,5,6.5) (6.5,8,9.5) (4.5,5.75,7) A (3.5,5,6.5) (3.5,5,6.5) (8,10,10) (0,0,2) (3.75,5,6.25) B (6.5,8,9.5) (2,3.5,5) (0.5,2,3.5) (8,10,10) (4.25,5.875,7) C (5,6.5,8) (0,0,2) (2,3.5,5) (6.5,8,9.5) (3.375,4.5,6.125) A (3.5,5,6.5) (5,6.5,8) (6.5,8,9.5) (0,0,2) (3.75,4.875,6.5) B (0,0,2) (3.5,5,6.5) (8,10,10) (0.5,2,3.5) (3,4.25,5.5) C (3.5,5,6.5) (8,10,10) (3.5,5,6.5) (0.5,2,3.5) (3.875,5.5,6.625) A (3.5,5,6.5) (0.5,2,3.5) (0,0,2) (5,6.5,8) (2.25,3.375,5) B (6.5,8,9.5) (2,3.5,5) (0.5,2,3.5) (0,0,2) (2.25,3.375,5) C (3.5,5,6.5) (0,0,2) (8,10,10) (3.5,5,6.5) (3.75,5,6.25) 11

8 C5 C51 C52 C53 C54 C55 A (3.5,5,6.5) (5,6.5,8) (2,3.5,5) (5,6.5,8) 3.875,5.375,6.875) B (0,0,2) (8,10,10) (6.5,8,9.5) (8,10,10) (5.625,7,7.875) C (8,10,10) (3.5,5,6.5) (0,0,2) (8,10,10) (4.875,6.25,7.125) A (6.5,8,9.5) (0,0,2) (0.5,2,3.5) (3.5,5,6.5) (2.625,3.75,5.375) B (8,10,10) (2,3.5,5) (0,0,2) (8,10,10) (4.5,5.875,6.75) C (0.5,2,3.5) (6.5,8,9.5) (2,3.5,5) (0,0,2) (2.25,3.375,5) A (3.5,5,6.5) (5,6.5,8) (8,10,10) (0.5,2,3.5) (4.25,5.875,7) B (2,3.5,5) (0,0,2) (6.5,8,9.5) (3.5,5,6.5) (3,4.125,5.75) C (0.5,2,3.5) (3.5,5,6.5) (0,0,2) (2,3.5,5) (1.5,2.625,4.25) A (0,0,2) (0.5,2,3.5) (6.5,8,9.5) (8,10,10) (3.75,5,6.25) B (3.5,5,6.5) (2,3.5,5) (0.5,2,3.5) (0,0,2) (1.5,2.625,4.25) C (5,6.5,8) (0,0,2) (0.5,2,3.5) (5,6.5,8) (2.625,3.75,5.375) A (0.5,2,3.5) (3.5,5,6.5) (8,10,10) (6.5,8,9.5) (4.625,6.25,7.375) B (3.5,5,6.5) (0.5,2,3.5) (0,0,2) (2,3.5,5) (1.5,2.625,4.25) C (0,0,2) (8,10,10) (5,6.5,8) (3.5,5,6.5) (4.125,5.375,6.625) Table 10: Fuzzy weght of applcants comparable to sub-crtera C1 C2 Sub C11 C12 C13 C14 C21 C22 C23 C31 Alterna tves AFW of alternatves Normalzed AFW of alternatves (NAFW) Normalzed weght of Subcrtera (NWSC) Multplcaton of NAFW and NWSC A (4.5,5.75,7) ( , , ) ( , , ) (0.0102, B (4.625,6.25,7.375) ( , , 1) ( , ,0.0102) ,0.0102) C (4.875,6.125,7.375) ( , ,1) ( , ,0.0102) A (2.625,3.75,5.375) ( , , ) ( , , ) (0.0217, , B (4.625,6.25,7.375) ( , ,1) ( , ,0.0217) ) C (2.25,3.375,5) ( , , ) ( , , ) A (2.625,3.75,5.375) (0.375, , ) ( , , ) (0.0052, , B (1.125,2.25,3.875) ( , , ) ( , , ) ) C (4.5,5.75,7) ( , ,1) ( , ,0.0052) A (3,4.125,5.75) ( , , ) ( , , ) (0.0508, , B (2.625,3.75,5.375) ( , , ) ( , , ) ) C (4.125,5.375,6.625) ( , ,1) ( , ,0.0508) A (3.75,4.875,6.5) ( ,0.75,1) ( , ,0.1293) (0.1293, , B (3.75,4.875,6.5) ( ,0.75,1) ( , ,0.1293) ) C (2.125,3,4.375) ( , , ) ( , , ) A (4.625,6.25,7.375) ( , ,1) ( , ,0.226) (0.226, 0.226, B (4.125,5.375,6.625) ( , , ) ( , , ) 0.226) C (4.625,6.25,7.375) ( , ,1) ( , ,0.226) A (4.5,5.75,7) ( , , ) ( , , ) (0.0493, , B (4.625,6.25,7.375) ( , ,1) ( , ,0.0493) ) C (2.25,3.375,5) ( , , ) ( , , ) A (4.875,6.125,7.375) ( , ,1) ( , ,0.0157) (0.0157, , B (3,4.125,5.75) ( ,0.6, ) ( , , ) ) C (4.125,5.375,6.625) (0.6, , ) ( , , ) A (5.75,7.5,8.25) ( , ,1) ( , ,0.0908) (0.0908, , C32 B (4.25,5.875,7) ( , , ) ( , , ) ) C (2.25,3.5,4.75) ( , , ) ( , , ) C3 A (3.125,4.625,6.125) ( , , ) ( , , ) (0.0529, , C33 B (5.625,7,7.875) ( , ,1) ( , ,0.0529) ) C (3.875,5.375,6.875) ( , , ) ( , , ) A (3.375,4.5,6.125) ( , ,0.875) ( ,0.0153, ) (0.0238, , C34 B (3.125,4.625,6.125) ( , ,0.875) ( , , ) ) C (4.5,5.75,7) ( , ,1) (0.0153, ,0.0238) C4 C41 A (3.75,5,6.25) ( , , ) (0.145, 0.145, ( , , ) 12

9 C5 C42 C43 C51 C52 C53 C54 C55 B (4.25,5.875,7) ( , ,1) 0.145) ( , ,0.145) C (3.375,4.5,6.125) ( , ,0.875) ( , , ) A (3.75,4.875,6.5) ( , , ) ( , , ) (0.0323, , B (3,4.25,5.5) ( , , ) ( , , ) ) C (3.875,5.5,6.625) ( , ,1) ( , ,0.0323) A (2.25,3.375,5) (0.36,0.54,0.8) ( , , ) (0.1083, , B (2.25,3.375,5) (0.36,0.54,0.8) ( , , ) ) C (3.75,5,6.25) (0.6,0.8,1) ( , ,0.1083) A 3.875,5.375,6.875) ( , , ) ( , , ) (0.005, 0.005, B (5.625,7,7.875) ( , ,1) ( , ,0.005) 0.005) C (4.875,6.25,7.125) ( , , ) ( , , ) A (2.625,3.75,5.375) ( , , ) ( , , ) (0.0091, , B (4.5,5.875,6.75) ( , ,1) ( ,0.0091) ) C (2.25,3.375,5) ( ,0.5, ) ( , , ) A (4.25,5.875,7) ( , ,1) ( , ,0.0055) (0.0055, , B (3,4.125,5.75) ( , , ) ( , , ) ) C (1.5,2.625,4.25) ( ,0.375, ) ( , , ) A (3.75,5,6.25) (0.6,0.8,1) ( , ,0.0169) (0.0169, , B (1.5,2.625,4.25) (0.24,0.42,0.68) ( , , ) ) C (2.625,3.75,5.375) (0.42,0.6,0.86) ( , , ) A (4.625,6.25,7.375) ( , ,1) ( , ,0.0015) (0.0015, , B (1.5,2.625,4.25) ( , , ) ( , , ) ) C (4.125,5.375,6.625) ( , , ) ( , , ) Table 11: Determnng separaton measure (FPIS and FNIS) Table 12: Computatons of d +, d - and CC Alternatves d + d - d + + d - CC Rank A B C

10 Internatonal Journal of Advanced Technology & Engneerng Research (IJATER) Natonal Conference On Emergng Trends n Technology (NCET-Tech 2012) X. CONCLUSION Ths paper ntroduced TOPSIS n a fuzzy envronment for evaluatng the canddates of techncal employee recrutment process. Usng fuzzy theory, measurng the mportance of techncal employees for a company can reduce ambgutes and uncertantes that are nherent n the performance measurement procedure of tradtonal approaches. In ths hybrd approach, a trangular fuzzy number s used for evaluatng the employee on the bass of techncal attrbutes possessed by the canddates. The dstance of each fuzzy number from both the Fuzzy Crter a C1 C2 C3 C4 C5 Sub- Crter FPIS(A+) a C11 (0.0067, , ) C12 (0.0136, , ) C13 (0.0033, ,0.0052) C14 (0.0316, , ) C21 (0.0745, , ) C22 (0.1417, , 0.226) C23 (0.0014, , ) C31 (0.0111, , ) C32 (0.0632, , ) C33 (0.0377,0.0470, ) C34 (0.0153, , ) C41 (0.0880, , 0.145) C42 (0.0188, , ) C43 (0.0649, , ) C51 (0.0035, , 0.005) C52 (0.0060, , ) C53 (0.0033, , ) C54 (0.0101, , ) C55 (0.0009, , ) FNIS(A-) (0.0062, , ) (0.0066, , ) (0.0019, , ) (0.0201, , ) (0.0422, , ) (0.1264, , ) (0.0150, , ) (0.0068, , ) (0.0247, , ) (0.0209, , ) (0.0106, , ) (0.0699, , ) (0.0146, , ) (0.0389, , ) (0.0024, , ) (0.0030, , ) (0.0011, , ) (0.0040, , ) (0.0003, , ) Postve Ideal Soluton (FPIS) and the Fuzzy Negatve Ideal Soluton (FNIS) are calculated. Then, the rankng orders of the canddates for the techncal attrbutes are measured on the bass of closeness rato (CR ). For evaluatng the weghts of each crteron, the ratngs of each alternatve are made n lngustc terms by decson makers, represented as trangular fuzzy numbers. The process contradcts the tradtonal evaluaton process by consderng the flexblty n ratng the mportance of alternatves n the evaluaton process. The methodology adopted here s a sound alternatve n a conflctng, unstructured, mult-crtera envronment. However, thorough nvestgaton s requred to mprove ths methodology as both frameworks and crtera for decson makng may devate wdely dependng on the context. References [1] Bellman, R. E., and Zadeh, L. A., Decsonmakng n a fuzzy envronment management. Scence, 17, pp , [2] Chang, C.W., Wu, C.R. and Ln, H.L., Applyng fuzzy herarchy multple attrbutes to construct an expert decson makng process, Expert Systems wth Applcatons, 36, pp , [3] Chuu, S. J., Selectng the advanced manufacturng technology usng fuzzy multple attrbutes group decson makng wth multple fuzzy nformaton. Computers & Industral Engneerng, 57, pp , [4] Dalah, D., Hayajneh, M., & Bateeha, F., A fuzzy multcrtera decson makng model for suppler selecton. Expert Systems wth Applcatons, 38, pp , [5] Das, M.C., Sarkar, B. and Ray, S., Comparatve evaluaton of Indan techncal nsttutons usng dstance based approach method", Benchmarkng: An Internatonal Journal, 20(5), pp , [6] Dng, J. F., An ntegrated fuzzy TOPSIS method for rankng alternatves and ts applcaton. Journal of Marne Scence and Technology, 19 (4), pp , [7] Gungor, Z., Serhadlıoglu, G. and Kesen, S. E., A fuzzy AHP approach to personnel selecton problem, Appled Soft Computng 9, pp , [8] Haldar A., Ray A., Banerjee D. and Ghosh S., Reslent suppler selecton under a fuzzy envronment, Internatonal Journal of Management Scence and Engneerng Management, 9(2), pp , [9] Klncc, O. and Onal, A.S., Fuzzy AHP approach for suppler selecton n a washng machne company, Expert Systems wth Applcatons 38, pp ,

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