Proceedngs of the 2014 Wnter Smulaton Conference A. Tolk, S. Y. Dallo, I. O. Ryzhov, L. Ylmaz, S. Buckley, and J. A. Mller, eds. TOPSIS BASED TAGUCHI METHOD FOR MULTI-RESPONSE SIMULATION OPTIMIZATION OF FLEXIBLE MANUFACTURING SYSTEM Yusuf Tansel Ic Berna Dengz Orhan Dengz Department of Industral Engneerng DND Technologcal Solutons Baskent Unversty 06810, Ankara, TURKEY Ankara, TURKEY Gozde Czmec Republc of Turkey Prme Mnstry Under Secretarat of Treasury Ankara, TURKEY ABSTRACT Ths study presents a smulaton desgn and analyss case study of a flexble manufacturng system (FMS) consderng a mult-response smulaton optmzaton usng TOPSIS (Technque for Order Preference by Smlarty to Ideal Soluton) based Taguch approach. Whle n order to reduce expensve smulaton experments wth the Taguch desgn, the TOPSIS procedure s used to combne the multple FMS responses (performance measures) nto a sngle response n the optmzaton processes. Thus, TOPSIS carres out an mportant role to buld a surrogate objectve functon that represents multple responses of the system. The ntegrated approach fnds a new desgn consderng dscrete factors (physcal and operatonal parameters) whch affect the performance measures of FMS. Optmal desgn confguraton s obtaned for the consdered system wth mproved performance. 1 INTRODUCTION The Flexble manufacturng system (FMS) s a manufacturng system that has flexblty to allow the system to react n case of changes. Ths flexblty generally occurs n two classes; machne flexblty, meanng the ablty to be changed to produce new products, and routng flexblty, meanng the system has the ablty to use multple machnes to perform the same operaton on a part, as well as to make changes n volume, capacty or capablty of the machnes. FMS conssts of a group of processng statons wth hghly automated cellular manufacturng technology. It controls both materal and nformaton flows onlne, by an ntegrated computer system (Groover 2007). Whle the FMS has advantages lke reducton n number of workers, better plannng, gettng better qualty or greater productvty wth the same number of workers, some technologcal problems lke sophstcated envronments are among the known dsadvantages. Because of the varety of advantages and dsadvantages mentoned above, FMS has ganed ncreasng attenton among researchers durng the last 20 years to obtan hgh utlzaton rates and hgh productvty volumes. Therefore, the desgn and operaton of the FMS ncludes sophstcated and nterconnected factors that nfluence the performance of the system. Gupta and Buzacott (1989) mentoned that the flexblty s the result of a combnaton of factors such as desgn (physcal) characterstcs and operatng condtons. System 978-1-4799-7486-3/14/$31.00 2014 IEEE 2147
Ic, Dengz,Dengz, Czmec utlzaton, work-n-process, set-up tme, tool changes, producton rate, due dates, job tardness, flow tme, and the balance of machne usage are the most commonly consdered factors among those n the FMS desgn and performance evaluaton n the lterature. Thus, the problem of fndng the optmal confguraton (workng condtons) of an FMS s a complex stochastc non-lnear problem whch ncludes both physcal and operatng system characterstcs. Ths desgn problem s to choose the optmal parameter values to maxmze the FMS performance. Smulaton s one of the most useful modelng tools to solve a varety of problems n complex manufacturng systems. One objectve of the applcaton of smulaton s to search for a set of operatonal parameters so that system performance s optmzed (Tsa 2002). Thus, smulaton s wdely used to analyze the behavor, system desgn, schedulng or producton plannng n the FMS (Park et al. 2001; Savsar 2005; Um et al. 2009) nstead of usng complex mathematcal models because of ts stochastc nature and complex structure. The problem of evaluatng the most preferred alternatve systems through expermental desgn ntegrated by smulaton s called smulaton optmzaton. To date, some methodologes such as response surface methods, gradent search methods, and heurstc search methods have been used for sngle or mult response smulaton optmzaton. On the other hand, Taguch methods whch are ntally used n qualty engneerng (Phadke 1989) are sutable, smple smulaton optmzaton strateges allowng a reducton n the number of expermentatons by usng the orthogonal array. However, the conventonal Taguch strategy does not provde a method for mult response smulaton optmzaton by default. In the technologcal envronment of an ndustral area, many problems that can occur n real FMS often embody many operatng and physcal characterstcs under more than one performance measure (mult response). In those cases, Taguch strategy needs a multple attrbute decson makng method such as TOPSIS, a technque used for order preference by smlarty to deal soluton (Yang and Chou 2005). To solve the problems faced n the FMS, mult response smulaton optmzaton has to be used frst to easly understand and explan the response behavor, and secondly to show the effects of physcal, operatonal, or both factors on the FMS performance. Therefore TOPSIS based Taguch optmzaton provdes a useful approach to convert mult-response smulaton-optmzaton problem nto the sngle-response problem. The TOPSIS method presents the global performance scores (C ) for all responses. Hence, accordng to the Taguch s robust desgn prncples, the optmal factor levels are easly obtaned. S/N (Sgnal to Nose) ratos and Taguch s basc orthogonal arrays are two man advantages of the Taguch method: ) The optmzaton process becomes more relable when the S/N rato s used, especally when varous conflctng responses can be treated as a dynamc system characterstc. )usng an orthogonal array reduces the experment tme and the smulaton costs. For example, n the Taguch s L 27 (35: 5 factors wth 3 levels each) orthogonal array only 27 smulaton experment scenaro are requred. If the full factoral desgn were used, t would have at least 5 3 = 125 smulaton runs. The optmzaton of stochastc smulaton ncludes gradent-based search methods, stochastc optmzaton, Response Surface Methodology (RSM), meta-heurstc methods or statstcal methods. RSM has attracted a growng nterest among these methodologes n recent years(dengz and Belgn 2014).The TOPSIS-based Taguch optmzaton s easy to perform compared to the RSM. The calculaton steps of the TOPSIS-based Taguch applcaton can easly be done. The RSM could be an unusable tool as the number of the desgn factors and/or the responses ncrease (Şmşek et al. 2013). For example, f the RSM desgn were used for 5 factors wth 3 levels each, t would have least 46 smulaton runs. All methods dscussed above; Taguch method, full factoral desgn and RSM can only consder a sngle response at a tme. In order to mprove the FMS performance a method ntegrated wth Taguch method for mult-response smulaton optmzaton s needed. In the lterature, some smulaton search heurstc procedures such as genetc algorthms (GA), smulated annealng (SA), tabu search (TS), and partcle swarm optmzaton (PSO) were developed and compared both wth respect to the best results acheved by each algorthm n a lmted tme span and ther speed of convergence to the results for fndng the optmum system performance (Alabaş et al. 2000). Smulaton s used to model the manufacturng system whle GA, SA, TS or PSO can be used to gude the 2148
Ic, Dengz O.,Dengz B., Czmec overall factor combnaton search process to dentfy the best performng ones. The stoppng crteron should be defned as a number of solutons vsted based on prelmnary expermentaton. Ths number changes dependng on the type of the problem and heurstc procedures. Generally, these procedures need more computatonal tme than the proposed method n ths study. Kosturak and Gregor (1998) descrbe the effectve smulaton applcaton steps of the manufacturng system and how dscrete event smulaton can be used for the mprovement of FMS wth the Taguch approach as a case study. They generalze ther experence ganed from many ndustral applcatons of smulaton projects that were worked on. They, fnally, nvestgate the effect of some producton control strateges on the manufacturng system parameters n FMS. Chan and Chan (2004) present a state of the art perspectve and comprehensve dscusson about studes focused on FMS schedulng and pont out that most past research on the desgn and operaton of the FMS consdered only a sngle performance measure as ther decson crtera. Chan et al. (2007) ponted out that the decson maker may requre a focus on effectve decson makng n manufacturng systems by consderng both physcal and operatng parameters of the system to dentfy the sutable type and level of flexblty wth all other parameters of the system that can affect the system performance. They establsh the need of modelng of the physcal and operatng parameters of FMS along wth flexblty, and present a smulaton study under Taguch s method to analyze the consdered parameters n ther study. Although there s a sgnfcant body of lterature for sngle and mult response optmzaton of manufacturng systems, recently, several researchers have appled mult-response optmzaton approaches partcularly to solve the schedulng and desgn problem of hypothetc FMS (Park et al. 2001; Kumar and Srdharan 2009; Um et al. 2009; Pandan et al. 2011; Joseph and Srdharan 2011). However, none of these methods consder both mult responses smultaneously and the approach by the Taguch qualty loss functon ntegrated wth TOPSIS to reduce multple responses to one objectve functon. A few studes nvolve Taguch method n the optmzaton of a sngle response n the FMS (Kosturak and Gregor 1998, Chan et al. 2007). In ths study, we am to show the usablty of the TOPSIS based Taguch method to solve a real case desgn problem by mult response smulaton optmzaton of the FMS problem. 2 PROPOSED METHODOLOGY 2.1 TOPSIS Based Taguch Optmzaton TOPSIS has been developed by Hwang and Yoon (1981) for solvng the MADM problems. It s based on the dea that an alternatve, whch s chosen, should have the farthest dstance from the negatve deal soluton and on the other sde, the shortest dstance from the postve deal soluton. TOPSIS procedure s ratonal and understandable. The computaton process of the TOPSIS method s depcted n a smple mathematcal form and the mportance weghts can be obtaned by drect assgnaton. For these reasons, the TOPSIS method s hghly stable for decson makng studes (Sen and Yang 1998). TOPSIS based Taguch optmzaton follows the Taguch optmzaton (Yang and Chou 2005) and s used to combne the multple FMS performance characterstcs nto a sngle value that can then be used as the sngle optmzaton functon. The frst step s to make smulaton runs whch are executed by followng the expermental structure of the selected orthogonal array. The sgnal-to-nose rato (S/N rato, ) s an effectve way to fnd sgnfcant factors by evaluatng mnmum varance (Yang and Chou 2005; Kuo et al. 2008). The S/N ratos can then be defned as shown n Eq. (1-2). Whle Eq.(1) s used for smaller-the-better responses, Eq.(2) s appled for larger-the-better responses (Kuo et al. 2008). n 1 = y 2 10log (1) n k k = 1 2149
2.2 Mult Response Smulaton Optmzaton Ic, Dengz O.,Dengz B., Czmec n 1 1 = 10log (2) 2 n k = 1 y k After computaton of the S/N ratos for each response of the system for all scenaros, the TOPSIS method s appled. The modfcaton of Yang and Chou s (2005) TOPSIS-based Taguch approach for mult response smulaton optmzaton of FMS s shown below: Step 1: Determne the factors that affect specfed performance measures for the FMS problem. Step 2: Formulate the expermental desgn matrx usng orthogonal array. Step 3: Make smulaton runs of FMS smulaton model accordng to the orthogonal array. Step 4: Compute the S/N ratos for all scenaros, () m r usng Eq. (1-2). Step 5: Enter characterstc values of S/N ratos at responses ( ; =1,2,,m number of scenaros, j=1,2,.,r number of responses ) as nputs n matrx as shown n Eq. (3). 11 D = 21... m1 12 22 m2...... 1r 2r mr (3) Step 6: Prepare normalzed decson matrx usng Eq. (4). = =1,2,..,mj=1,2,.,r (4) Step 7: Construct the weghted normalzed decson matrx usng Eq. (5-7). m = 1 2 V = X =1,2,..,mj=1,2,.,r (5) mxr X = w (6) j W = w1, w2,..., wr (7) Step 8: Determne the deal and negatve-deal solutons: The deal soluton (A) and negatve-deal soluton (A ), representng the maxmum and mnmum S/N ratos, respectvely, are as follows: 2150
Ic, Dengz O.,Dengz B., Czmec A = 2 X 1, X,..., X r (8) X j = X j J = m,... (9) max 1 A = X 1, X 2,..., X r (10) X j = mn X j J = 1 m,... (11) The deal soluton, (A), s made of all the best values (maxmum S/N rato) and the negatve-deal soluton, (A - ), s made of all the worst values (mnmum S/N rato) at the responses n the weghted normalzed decson matrx (Sen and Yang 1998) (Eq.5). Step 9: Calculate the dstance of scenaro to the deal soluton (d ), and from the negatve deal soluton (d - ) usng Eq. (12-13). d = r ( X X j j= 1 ) 2 =1,2,.m; j=1,2, r (12) r 2 d = ( X X j ) =1,2,.m; j=1,2, r (13) j= 1 Step 10: Calculate the rankng score (C ) usng Eq. (14). C = d /( d + d ), =1,2,.m; j=1,2, r (14) Step 11: Determne the optmal parametrc combnaton to maxmze S/N: The optmal combnaton of factor-levels s fnally determned, n vew of the fact that a larger TOPSIS value ndcates better qualty. Taguch method s to be appled fnally to evaluate ths optmal settng (by maxmzng the TOPSIS ndex). 3 PROBLEM DESCRIPTION: A CASE STUDY OF FMS 3.1 Company Overvew The ncreasng competton and rapd technologcal changes n the world have forced manufacturers to fnd new adaptaton ways for ther manufacturng systems. Therefore, manufacturng companes need new approaches to be able to respond to market changes rapdly and wth hgh productvty. In ths study, a new desgn for an exstng FMS s obtaned wth hgh productvty n a company usng the smulaton optmzaton method. X Manufacturng Co. produces an extensve range of over 100 2151
Ic, Dengz O.,Dengz B., Czmec types of products such as cams, crankshafts, shafts, motor blocks, pstons and transmsson elements. The company ncludes three major producton lnes and a FMS department. 3.2 The Producton System & Performance Measures The producton system consdered n ths research s the FMS department, whch s producng brake cylnder casng, gear box and flywheel housng. As known, there are two types of levels of flexblty: dedcated and random. The current FMS n ths company s a dedcated type FMS n whch parts are routed to the next operaton n the machnng center. There are four CNC machnng centers (MAZAK FH 6800) wth one separate local buffer area that has a capacty of 20 pallets for ncomng and outgong parts n ths department. Parts are moved by transportng robots on bdrectonal paths, and processed at any one of the avalable CNC machnng centers. The buffer szes n the system are the same as advsed n Groover (2007). A schematc layout of the system s gven n Fgure 1. When a transportng robot completes ts servce, t stays dle f there s no work watng for the load-unload staton. Fgure 1: FMS Layout One of the unque characterstcs of dedcated FMS s to use no routng flexblty, so t s not allowed for a part to be processed on an alternatve machne for each operaton (Park et al. 2001). The challenge of copng wth large fluctuatons n product demand cannot be solved wth dedcated FMS that are not scalable. It s commonly known that the FMS should be able to produce (1) any part wthn the machne capablty, (2) any mx of parts, and (3) be able to use dfferent sequences. Ths approach ncreases cost snce t requres a parallel system structure for the FMS. On the other hand, CNC machnes, that are the cornerstones of an FMS, are desgned as general-purpose machnes that use a sngle tool that can be manpulated n dfferent drectons to provde flexblty (Koren 2006). The company has already mplemented three polces such as lne-balancng, shorten set-up tme, tranng semnars for employees to mprove the performance of the FMS n the current system. However, the FMS stll had some problems due to the delay of fnal product delvery. Two man reasons behnd ths problem are determned (1) long cycle tme (CT) and (2) the long watng tmes n queue (WIQ), resultng n a throughput level that s less than desred. The company s am s to ncrease the overall productvty. 3.3 Decson Varables To obtan a hghly productve and cost-effectve system desgn, fve factors, or decson varables, such as the number of cuttng tools, the number of operators, the number of pallets and the velocty of transporter robots (m/mn) and one operatonal parameter (pallet selecton procedure such as dedcated or random) are selected based on our prelmnary nvestgaton. 2152
Ic, Dengz O.,Dengz B., Czmec The dfferent flexblty prncples are used to see whether the performance of the FMS s affected or not. In addton, three performance measures; CT (hour), Throughput (T) (unts/month) and WIQ (hour) are consdered for ths desgn optmzaton problem. Table 1 represents fve factors wth three-levels. TOPSIS based the Taguch approach s used to fnd the optmum workng condton whch reveals hgh productvty wth the values of CT, T and WIQ. Table 1: Factors and levels Symbol Factor Lower (Current System) Level Mddle Upper A Number of operators 2 3 4 B Velocty of 1 1.5 2 transporter (m/mn) C Number of cuttng 120 160 200 tools D Pallet selecton strategy E Number of pallets 20 30 40 Dedcated Random Dedcated to frst non-busy pallet The smulaton model was constructed usng ARENA software. Valdated smulaton outputs (three responses T, CT and WIQ) are collected va an L 27 orthogonal array desgn. The smulatons are run for ten replcatons at each desgn pont. The normalzed S/N ratos are computed usng Eq. (1-2) and the rest of the methodology s appled followng steps 4 through 11 as descrbed n Secton 2.1. Table 2 shows the resultng factor effects that are obtaned by the TOPSIS-based Taguch approach explaned n Secton 2. New desgn parameters of the FMS s A 2 B 1 C 3 D 3 E 3. Table 2: Average TOPSIS values by factor levels usng vector normalzaton Level A B C D E 1 0.4063 0.4926 0.2983 0.3321 0.4422 2 0.4772 0.4602 0.5029 0.3312 0.3939 3 0.4471 0.3779 0.5294 0.6673 0.4946 Selected Factor Levels A2 B1 C3 D3 E3 The performance comparson of current and the new FMS desgns s gven n terms of consdered performance measures. Table 3 shows obtaned performance mprovements for the three responses, respectvely. The results show a sgnfcant postve change n overall performance measures. The benefts of changng the FMS desgn as suggested by the smulaton optmzaton wth the TOPSIS based Taguch method s seen as multples of current system s capacty. The overall performance mprovement of the new proposed desgn s approxmately 3 tmes better than the current system desgn. 2153
4 CONCLUSION Ic, Dengz O.,Dengz B., Czmec Table 3: Comparson of the Performance of Results wth the Current System Performance Measures Current System (I) TOPSISbased Taguch (II) Improvement T(unts/month) 348 1183 239.94 % CT (hour) 1.815 0.636 64.95 % WIQ(hour) 1.776 0.434 75.56 % Ths study presents a TOPSIS based Taguch method as mult response smulaton optmzaton approach to solve the mult response optmzaton problem for a real case study of a FMS. The performance of the current FMS desgn and the proposed FMS desgn obtaned by the TOPSISbased Taguch method are compared n terms of throughput, cycle tme and watng tme n queue. It s shown that the proposed system, desgned by smulaton optmzaton usng the TOPSIS-based Taguch method reveals better throughput rates, shorter cycle tmes and better WIQ. The proposed system has an overall performance mprovement of approxmately 3.4 tmes hgher throughput, 2.86 tmes shorter cycle tme, and watng tmes decreased to 1/5 th when compared wth the current FMS n use. REFERENCES Alabaş, Ç., Altıparmak, F., Dengz, B. 2000. The Optmzaton of Number of Kanbans wth Genetc Algorthms, Smulated Annealng and Tabu Search. Evolutonary Computaton. Chan, F.T.S. and Chan, H.K. 2004. A comprehensve survey and future trend of smulaton study on FMS schedulng. Journal of Intellgent Manufacturng 15: 87-102. Chan, F.T.S., Bhagwat R., and Wadhwa, S.2007. Flexblty performance: Taguch s method study of physcal system and operatng control parameters of FMS. Robotcs and Computer Integrated Manufacturng 23:25-37. Dengz, B., and Belgn, Ö. 2014. Smulaton optmzaton of a mult-stage mult-product pant shop lne wth Response Surface Methodology. Smulaton: Transactons of the Socety for Modelng and Smulaton Internatonal, 1 10. Groover, M.P. 2007.Automaton, Producton Systems, and Computer Integrated Manufacturng. Thrd Edton. Prentce Hall Press Upper Saddle Rver, NJ, USA. Gupta, D. and Buzacott, J. A. 1989. A Framework for Understandng Flexblty of Manufacturng Systems. Journal of Manufacturng Systems 8:89-97. Joseph O.A. and Srdharan R. 2011. Effects of flexblty and schedulng decsons on the performance of an FMS: smulaton modelng and analyss. Internatonal Journal of Producton Research 1-21. Koren, Y. 2006. General RMS Characterstcs Comparson wth Dedcated and Flexble Systems: Reconfgurable Manufacturng Systems and Transformable Factores, Sprnger, 27-45. Kosturak, J. and Gregor M. 1998. FMS smulaton: Some experence and recommendatons. Smulaton Practce and Theory 6:423-442. Kuo,Y., Yang T., Huang G.W. 2008. The use of a grey based Taguch method for optmzng mult response smulaton problems. Engneerng Optmzaton 40(6):517-528. Kumar, N.S. and Srdharan, R. 2009. Smulaton modelng and analyss of part and tool flow control decsons n a flexble manufacturng system. Robotcs and Computer-Integrated Manufacturng 25: 829 838. 2154
Ic, Dengz O.,Dengz B., Czmec Pandan, P.P., Sankar, S.S., Ponnambalam S.G. and Bathrnath S. 2011. Secondary populaton mplementaton n mult-objectve evolutonary algorthm for schedulng of FMS. Internatonal Journal of Advanced Manufacturng Technology 1-12:, In Press.DOI10.1007/s00170-011-3359-6. Park, T., Lee, H., Lee H. 2001. FMS desgn model wth multple objectves usng compromse programmng. Internatonal Journal of Producton Research 39(15):3513-3528. Phadke, S.M. 1989. Qualty Engneerng Usng Robust Desgn. Prentce Hall, Englewood Clffs, NJ. Sen, P. and Yang, J.-B. 1998. Multple Crtera Decson Support n Engneerng Desgn. Sprnger, London Savsar, M. 2005. Performance analyss of an FMS operatng under dfferent falure rates and mantenance polces. The Internatonal Journal of Flexble Manufacturng Systems16:229 249. Şmşek, B., İç, Y.T., Şmşek, E.H. 2013. A TOPSIS-based Taguch optmzaton to determne optmal mxture proportons of the hgh strength self-compactng concrete. Chemometrcs and Intellgent Laboratory Systems 125, 18 32. Tsa, C.S., 2002. Evaluaton and optmzaton of ntegrated manufacturng system operatons usng Taguch s experment desgn n computer smulaton. Computers & Industral Engneerng 43:591-604. Um, I., Cheon, H., Lee, H. 2009. The smulaton desgn and analyss of a Flexble Manufacturng System wth Automated Guded Vehcle System. Journal of Manufacturng Systems 28:115-122. Yang, T., Chou, P. 2005. Solvng a mult-response smulaton-optmzaton problem wth dscrete varables usng a multple-attrbute decson makng method. Mathematcs and Computers n Smulaton 68:9-21. AUTHOR BIOGRAPHIES YUSUF TANSEL IC s an Assstant Professor of Department of Industral Engneerng at Baskent Unversty. He receved a PhD degree n Mechancal Engneerng from Gaz Unversty Insttute of Scence and Technology. He has more than 10 years of experence n the bankng ndustry. Hs research nterests nclude applcaton of expert systems to manufacturng systems, modelng and analyss of producton systems, mult-crtera decson makng, and fnancal rsk management n commercal banks. ORHAN DENGIZ s the founder of an engneerng consultant frm, DND Technologcal Solutons, based n Ankara, TURKEY. Asde from the ndustry, he also teaches as an adjunct professor. Dr. Dengz receved hs B.S. from Mddle East Techncal Unversty, Turkey, and receved hs Ph.D. degree from Auburn Unversty, USA. Hs research nterests nclude large scale system optmzaton wth meta heurstcs, artfcal ntellgence, machne learnng, machne to machne communcaton, telecommuncaton network desgn and smulaton optmzaton of ndustral systems. Hs emal address s odengz@dndltd.com.tr. BERNA DENGIZ s the dean of Engneerng at Baskent Unversty. Her feld of study s manly smulaton modelng and optmzaton of complex large szed systems besdes heurstc optmzaton. She has receved research fundng for her collaboratve studes from the NATO-B2 program, TUBITAK (The Scentfc and Techncal Research Councl of Turkey), Government Plannng Center of Turkey and Natonal Scence Foundaton (NSF) of the USA. She has worked as vstng professor at the Unversty of Pttsburgh and Auburn Unversty. Her web page can be reached at http://www.baskent.edu.tr/~bdengz. Dr. Dengz s emal address s bdengz@baskent.edu.tr. GOZDE CIZMECI s an Industral Engneer. She currently works at Republc of Turkey Prme Mnstry Under Secretarat of Treasury. She receved her B.S. from Baskent Unversty, Ankara, Turkey. Her feld of study s manly smulaton modelng of manufacturng and servce systems. 2155