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Hybrid Flow Shop Scheduling Problem Based on Evolutionary Multi-objective Algorithm


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Hybrid Flow Shop Scheduling Problem Based on Evolutionary Multi-objective Algorithm
WEI ZhongXU Xiao-feiDENG Sheng-chun
School of Computer Science and Technology,Harbin Institute of Technology,Harbin 150001,China
hybrid flow shop schedu ling mult-i ob jective opt im ization evo lut ionary comput ing f i-t ness assignmen t
TP 393. 07
A new evo lu tionary algorithm for so lv ing mu lt-i ob jective hybrid flow shop scheduling problem (HFSP) w hich is an important top ic in supp ly cha in netw ork opt im ization is presented. The genera lmodel for theHFSP is proposed, and am atrix gene encodingmethod and a sort of fitness assignm ent strategy w hich can approach the optimum so lutions w ith dynam ic w eight ing are d iscussed. The a lgorithm process is presented by using e litist strategy. The convergent performance of the algorithm is analyzed by comput ing the prog ress measuremen.t The performance analysis and the exper-i mental resu lts show that the a lgorithm is effect ive for h igh-dimensionalmult-i ob jective prob lem s and can converge to satisfactory so lutions at a h igh speed.


[ 1] Brah S A, H unsucker J L. B ranch and bound a lgorithm fo r the flow shop w ith mu ltiple processo rs [ J]. European Journal o fOperational Research, 1991, 51: 88- 99.
[ 2] Brah S A, Loo L L. H euristics fo r scheduling in a flow shop w ith m ultiple processors [ J]. European Journa l of Opera tiona l Research, 1999, 113: 113- 122.
[ 3] X iaoW endong, H ao Pe ifeng, Zhang Sen, Xu X inhe. H ybr id flow shop schedu ling us ing genetic algorithm s [ A]. IEEE Proceeding o f 3 rdW or ld Congress on Inte lligen t Contro l and Au tom ation [ C]. H efe :i Institute o f E lec trical and E lectronics Eng ineers Inc, 2000. 537- 541.
[ 4] Scha ffer J D. M u ltiple ob jec tive optim ization w ith vector evalua ted genetic a lgor ithm s [ A ]. Proceed ings of the F irst Internationa l Con ference on Gene tic A lgorithm s and Their App lication [ C ]. H illsda le: Lawrence Er lbaum Assoc ia tes, 1985. 93- 100.
[ 5] Richardson J T, Pa lm erM R, L iep ins G, et a .l Som e gu idelines fo r genetic a lgo rithm s w ith pena lty functions [ A] . Proceed ing s o f the Th ird Internationa l Con fe rence on Genetic A lgo rithm s [ C ]. W ash ing ton: M o rg an Kau fm ann Pub lishers, 1989. 191- 197.
[ 6] FonsecaCM, Flem ing P J. Genetic algo rithm s formu-l tiobjective optim ization: Formu lation, d iscussion and genera liza tion [ A ]. Genetic A lgo rithm s: Proceed ings of the F ifth International Conference [ C]. SanMa teo: Mo rgan Kau fm ann, 1993. 416- 423.
[ 7] Sr in ivas N, Deb K. Mu lt-i objective optim isation us ing non-dom inated so rting genetic a lgo rithm [ J] . Evo lutionary Com putation, 1994, 2 ( 3): 221- 248.
[ 8] Fonseca CM, F lem ing P J. An overv iew o f evo lu tionary a lgor ithm s in mu lt-i ob jective optim iza tion [ J ]. Evolutionary Com putation, 1995, 3 ( 1) : 1- 16.
[ 9] Ishibuch iH, Yo sh ida T, M ura ta T. Ba lance between genetic search and local search in m em e tic algorithm s for m ultiob jec tive perm utation flow shop scheduling [ J ]. IEEE Trans on Evolutionary Com putation, 2003, 7 ( 2): 204- 223.
[ 10] Ishibuch iH, M ura ta T. A m ult-i ob jec tive genetic local sea rch algor ithm and its applica tion to flow shop schedu ling [ J ] . IEEE Transactions on System s, M an, and Cybernetics-Part C: App lications and Reviews, 1998. 28 ( 3): 392- 403.
[ 11] Ve ldhu izen V, Lam ont G B. Evolutionary com puta tion and conve rgence to a pareto front [ A] . Late Break ing Papers at the Genetic Program. ru ing 1998 Con ference [ C ] . Stan fo rd, San Franc isco: Stanford Un iv ers ity Bookstore. 1998. 221- 228.
[ 12] Ve ldhu izen V, Dav id A, Lam ont G B. Mu ltiobjectiv e evo lutionary algor ithm s: analyzing the sta te-o-f the-art [ J]. Evo lutiona ry Compu tation, 2000, 8 ( 2): 125- 147.


Last Update: 2006-06-30