Scheduling Model and Algorithm for Collaborative Product Design Based on MD

Transcription

1 Inernaonal Conference on Informaon Technology and Managemen Innovaon (ICITMI 5 Schedulng Model and Algorhm for Collaborave Produc Desgn Based on MD Xaole Wang, a*, Melang He,b and Quan Xu,c * Dearmen of Mechancal Engneerng, Norh Chna Elecrc Power Unversy, Baodng, hebe, Chna Dearmen of Economcs and Trade, Hebe Fnance Unversy, Baodng, hebe, Chna a wxlwxl96@6.com, b uma6@6.com, c @qq.com Keywords: Collaborave roduc desgn, Schedulng model, Machng degree, Genec algorhm Absrac. In order o mrove he effcency of collaborave roduc desgn, rojec ask and eam should be allocaed and scheduled reasonably. Frsly, he machng degree beween ask and eam was defned. Then, radng-off rojec duraon and cos, mul-objecve schedulng model of collaborave roduc desgn based on machng degree was esablshed, and he mroved genec algorhm was redesgned o solve he schedulng model. Through an examle of collaborave desgn, he feasbly and valdy of he roosed model and algorhm were verfed. Smulaon resul shows ha he algorhm has advanages n recson and convergence seed. Inroducon Wh he rad develomen of echnology and he ncrease of roduc comlexy, he dvson of labor s more and more secalzed, so ha he core enerrse needs o jon wh cusomer, suler and research nsue o collaboravely work. Through cross-organzaonal collaborave roduc desgn (CPD, can realze he maxmzaon of resource negraon and he mrovemen of desgn effcency. However, n he rocess of collaborave roduc desgn, he dversy of desgn agen, and nerdeendence and muual resrcon beween asks, hey make collaborave nnovave desgn rocess become more comlcaed. Therefore, desgn ask and resource should be reasonably allocaed and scheduled o shoren he develomen cycle and reduce cos. Abou ask and resource allocaon of collaborave desgn, some researches focused on ask denfcaon, analyss of ask relaonshs and ask schedulng based on Per ne[] and desgn srucure marx (DSM[]. Some of oher researches focused on esablshng he schedulng omzaon model for collaborave desgn and model soluon algorhms[3,4,5,6]. In addon, currenly, n he sudy of caably machng, Wu & Qn roosed a resource caably measurng mehod and resource caably deloymen mechansm by mang RTCI o RPCI[7]. Zhang & L smulaed collaborave roduc develomen rocess based agen, desgn agen seleced hs arner accordng o he ably and characer machng degree[8]. In he rocess of CPD, desgn asks are accomlshed by dfferen eams. The revous researches have focused on he machng beween asks and eam members based on echncal caably or desgner reference. However, akng desgn eam as a whole, from macro ersecve, how o realze ask-eam reasonable machng? For desgn arner selecon or ask assgnmen, hey need measure eam comrehensve caacy. Movaed by he above observaons, frsly, comeency elemens of collaborave roduc desgn are resened. Then, machng degree beween ask and eam s defned and s comung model s consruced. Furhermore, mul-objecve schedulng model for collaborave roduc desgn based on machng degree s esablshed, and he mroved genec algorhm s desgned o solve he schedulng mode. Fnally, an examle s solved successfully o llusrae he feasbly and valdy of he roosed model and algorhm. 5. The auhors - Publshed by Alans Press 64

2 Mul-Objecve Schedulng Model of CPD Comeency elemens of collaborave roduc desgn. For cooerave ask allocaon, s necessary o analyze he requred caably for desgn ask. I no only ncludes echncal level, nnovaon caably, bu also ncludes sofware and hardware resources and socal resource[9]. The comeency elemens for CPD are lsed n Table. Goal Core comeency Basc comeency Basc resource Table Comeency elemens of collaborave roduc desgn hgh effcency and hgh qualy eam nnovaon caably eam learnng caably eam communcaon caably eam collaboraon caably eam execuve caably eam servce conscousness eam echncal caably eam managemen caably nformaon resource brand resource hardware and sofware resource socal ne resource Defnon of machng degree. Machng degree(md s a conce o measure he fness beween elemens. As an examle, when machng rojec ask wh echncal eam, f he machng degree s oo low, manfess ha echncal eam s caables and resources are no enough o suor hem o comlee he ask. Hgher machng degree ensures ha eam can accomlsh he ask hgh-effcency and hgh-qualy. Bu f machng degree s oo hgh, means cos rse. Thus, machng degree beween ask-eam should be ke n a reasonable range. The ask-eam machng degree model s consruced n wo ways, one s ersonnel caably of echncal eam, and anoher s he avalable resources. The machng degree TC j beween ask and eam j a he dmenson of ersonnel caables s defned as follows: 8 ( gj - ( e ( TCj = α ( ± ( e = Where denoes he h ersonnel caably, α s he wegh of he h ersonnel caably for ask, g j s he acual level of he h ersonnel caably of echncal eam j for ask, e s he requred level of he h ersonnel caably for ask. In formula (, If g j > e, ake + ; else ake -. The machng degree TR j beween ask and eam j a he dmenson of resource s defned as follows: 4 r= r r ( gj - ( e r TRj = β ( ± ( r ( e Where r denoes he rh resource, ß r s he wegh of he rh resource for ask, g j r s he acual level of he rh resource of echncal eam j for ask, e r s he requred level of he rh resource for ask. Furhermore, he machng degree MD j beween ask and echncal eam j s defned as: r r ( - ( ( - ( ( 8 g 4 j e gj e r MDj = ω α ( ± + ω ( ( β ± (3 = ( r = r e ( e Where w and w are he weghs of ersonnel caably and resource for ask, resecvely. Mul-objecve omzaon schedulng model. In collaborave nnovaon rojec, hrough raonal resource selecon and confguraon accordng o rojec asks requremens, duraon and cos omal are acheved. Parameers: PT: he rojec duraon; C: he rojec cos; T: he se of rojec asks, T={ T, T,,T m }; G: he se of echncal eams, G={ G, G,, G n}, n s he number of echncal eam; s : he sar me of ask, S={s, s,, s,, s m, s m+ }, ask m+ s a vrual ask; 65

3 MD j : he machng degree beween ask and echncal eam j; N : he sandard execed execuon me of ask ; : he maxmum shoren amlude of execuon me for ask ; j : he execed me of echncal eam j o execue ask. Abou schedulng objecve rade-off, Chen e al roosed ha here was lnear relaon beween acvy me reducon and cos ncreases, he me-cos rade-off roblem can be ransferred no a lnear rogrammng roblem[]. Thus, he omzaon objecve s as follows: mn f ( x = W PT + W C = W S + W C (4 Consrans: m+, eam j comlee ask x = (5 j else j e rmn e r e rmax (6 n j= x = (7 j A S q = x = (8 j = max mn( S +, T B (9 N N MD j, j, MDj < ( MDj ( N N N ( q N Where W and W are he weghs of rojec duraon and cos resecvely. Consran(6 exresses resource consran. Consran(7 ensures ha ask jus only be done by echncal eam j. Consran(8 ensures ha one eam jus only can erform one ask a a erod, A s he collecon of asks carred ou a me. Consran(9 denoes me consran, B(q s he recedence acves se of ask q. Equaon( denoes he me wll be aken for echncal eam j o fnsh ask whle consderng he machng degree MD j. The Imroved Genec Algorhm Based on sngle codng, mul-on crossover and smulaed annealng oeraor, he genec algorhm s mroved o enhance he accuracy and reduce he comlexy of comuaon. The ses of he mroved genec algorhm are as follow: Se : Codng. Adong decmal sngle codng, each gene locus reresens he ask code and he number on he gene locus reresens he corresondng machng echncal eam. Se : Selecng he nal oulaon. Randomly generae a ceran number of ndvduals. Then, remove he reeaed ndvduals and he ndvduals ha do no mee he consrans, choose he bes ndvdual no he nal oulaon and selec a- ndvdual from he remanng ndvduals randomly, comose nal oulaon wh number of a. Se 3: Muaon oeraor. In he rocess of muaon, sngle on muaon s used he frs half of he gene, and mul-on muaon s adoed n he second ar. Se 4: Crossover oeraor. Mul-on crossover s adoed. Cross ons are seleced randomly, and he amoun of cross ons s also seleced randomly bu greaer han. Se 5: Selecon oeraor. The revous generaon oulaon, oulaon afer crossover and oulaon afer muaon, oal 3a ndvduals are ncluded n he selecon se. Frsly, ndvduals ha reeaed and do no mee he consrans are removed. Then, he bes ndvdual of recedng generaon oulaon, crossover oulaon and muaon oulaon are reaned resecvely. For he remanng ndvduals, wo 66 (

4 ndvduals are seleced randomly and one s chosen usng smulaed annealng oeraor wh robably ex(-δc/θ o brng no he nex generaon, he anoher one s aken back. Reea he above rocedure unl he amoun of nex generaon reached number a, hen urn no he se 3. Se 6: End, ouu he omal. When mee one of he condons, he eraon s soed: ( Fness of he bes ndvdual and he grou are no longer rsng; ( The number of eraons reaches he rese number. Case Sudy The omzaon model and algorhm were aled no a moble hone desgn rojec. The relaonsh marx of desgn ask was shown n Fgure. Toally 5 asks were ncluded n he rojec, he avalable echncal eam were G, G,, G, resecvely. Fg. Task relaonsh marx of desgn rojec Sandard execuon me of he asks and he maxmum shoren mes were shown n Table. The coss ha echncal eam comlee he asks were n Table 3, and he machng degree beween echncal eam and ask were shown n able 4. Table Sandard execuon me and he maxmum shoren me of desgn asks Task T T T 3 T 4 T 5 T 6 T 7 T 8 [day] N Task T 8 T 9 T T T T 3 T 4 T 5 N Table 3 The cos ha echncal eam comlee he ask G G G 3 G 4 G 5 G 6 G 7 G 8 G 8 G 9 G [ 3 ] T T T T T T M T T

5 Table 4 Machng degree beween echncal eam and ask G G G 3 G 4 G 5 G 6 G 7 G 8 G 8 G 9 G T T T T T T M T T The fness funcon f(x =.6*PT+.4*C. Based on he daa above, he rocedures of he mroved genec algorhm were wren by MATLAB, and he omal rogram of ask-eam machng was go n 458h eraon and he resul was shown n Table 5. Table 5 Tasks - eam machng rogram Task echncal eam Task echncal eam Under hs machng rogram, he objecve omal value s 76., whle duraon s days and cos s * 3 RMB. The convergence curve of he objecve funcon n he erave rocess was shown n Fgure. The runnng resul by sandard genec algorhm was shown n Fgure 3. The objecve funcon value of he omal was The eraon number of fndng he omal was greaer han he mroved algorhm. The resul of he comarson dslayed he advanage of he mroved algorhm n fndng he omal and convergence seed. Fness Fness Ieraon Ieraon Fg. Convergence curves of he objecve funcon Fg.3 The runnng resul of radonal GA The rojec ask allocaon and schedulng lan was shown n Fgure 4. 68

6 Conclusons Fg.4 Projec ask allocaon and mng char Resources allocaon of collaborave nnovaon rojec has an moran nfluence on nnovaon effcency and cos. Thus, comeence elemens for CPD are resened. Then, he defnon of machng degree s aled o descrbe he fness beween eam and ask. On he bass, a schedulng model for collaborave roduc desgn based on MD s esablshed, and he mroved genec algorhm s adoed o search he omal soluon. Based on sngle-codng sraegy, mul-on cross and combned wh smulaed annealng oeraor, he erformance of he algorhm s mroved. Fnally, he feasbly and effcency of he roosed model and algorhm are verfed. In he fuure, we shall focus on how o mrove nnovave effcency from nnovaon agen neracon. Acknowledgemens Ths research s suored by Naural Scence Foundaon of Hebe rovnce (No.G358, he Fundamenal Research Funds for he Cenral Unverses (No.4 MS and he Scence Foundaon of Hebe Fnance Unversy (No.JY56. References [] Y. H. Lee, C. T. Chang, D. S. Wong, S. S. Jang, Chemcal Engneerng Research and Desgn. 89 ( 9-3. [] T. R. Wang, S. S. Guo, B. R. Sarker, Y. B L, P Advanced Engneerng Informacs. 6 ( 8-9. [3] M. E. Brun, P. Berald, F. Guerrero, Comuers & Oeraons Research. 38 ( [4] A. Tahooneh, K. Zara, Usng arfcal bee colony o solve sochasc resource consraned rojec schedulng roblem, Advances n Swarm Inellgence, Proceedngs of he Second Inernaonal Conference., [5] H. Pang, Z. D. Fang, H. Guo, Y. Zhao, Sysems Engneerng and Elecroncs, 3 ( (In Chnese. [6] Y. Zhao, Q. Song, Advances n Informaon Scences and Servce Scences. 4 ( 9-7. [7] Y. H. Wu, X. S. Qn, Indusral Engneerng Journal. 4 ( 8-84(In Chnese. [8] S. Zhang, Y. Z. L, Comuer Modelng and New Technologes. 8 ( [9] B. Du, J. F. L, Scence Research Managemen. 33 ( 4-48 (In Chnese. [] S. P. Chen, M. J. Tsa, Euroean Journal of oeraonal research. (