Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions
This paper proposes an energy-aware multi-objective optimization algorithm (EA-MOA) for solving the hybrid flow shop (HFS) scheduling problem with consideration of the setup energy consumptions. Two objectives, namely, the minimization of the makespan and the energy consumptions, are considered simu...
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| Vydáno v: | Journal of cleaner production Ročník 181; s. 584 - 598 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier Ltd
20.04.2018
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| Témata: | |
| ISSN: | 0959-6526, 1879-1786 |
| On-line přístup: | Získat plný text |
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| Abstract | This paper proposes an energy-aware multi-objective optimization algorithm (EA-MOA) for solving the hybrid flow shop (HFS) scheduling problem with consideration of the setup energy consumptions. Two objectives, namely, the minimization of the makespan and the energy consumptions, are considered simultaneously. In the proposed algorithm, first, each solution is represented by two vectors: the machine assignment priority vector and the scheduling vector. Second, four types of decoding approaches are investigated to consider both objectives. Third, two efficient crossover operators, namely, Single-point Pareto-based crossover (SPBC) and Two-point Pareto-based crossover (TPBC) are developed to utilize the parent solutions from the Pareto archive set. Then, considering the problem structure, eight neighborhood structures and an adaptive neighborhood selection method are designed. In addition, a right-shifting procedure is utilized to decrease the processing duration for all machines, thereby improving the energy consumption objective of the given solution. Furthermore, several deep-exploitation and deep-exploration strategies are developed to balance the global and local search abilities. Finally, the proposed algorithm is tested on sets of well-known benchmark instances. Through the analysis of the experimental results, the highly effective proposed EA-MOA algorithm is compared with several efficient algorithms from the literature. |
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| AbstractList | This paper proposes an energy-aware multi-objective optimization algorithm (EA-MOA) for solving the hybrid flow shop (HFS) scheduling problem with consideration of the setup energy consumptions. Two objectives, namely, the minimization of the makespan and the energy consumptions, are considered simultaneously. In the proposed algorithm, first, each solution is represented by two vectors: the machine assignment priority vector and the scheduling vector. Second, four types of decoding approaches are investigated to consider both objectives. Third, two efficient crossover operators, namely, Single-point Pareto-based crossover (SPBC) and Two-point Pareto-based crossover (TPBC) are developed to utilize the parent solutions from the Pareto archive set. Then, considering the problem structure, eight neighborhood structures and an adaptive neighborhood selection method are designed. In addition, a right-shifting procedure is utilized to decrease the processing duration for all machines, thereby improving the energy consumption objective of the given solution. Furthermore, several deep-exploitation and deep-exploration strategies are developed to balance the global and local search abilities. Finally, the proposed algorithm is tested on sets of well-known benchmark instances. Through the analysis of the experimental results, the highly effective proposed EA-MOA algorithm is compared with several efficient algorithms from the literature. |
| Author | Han, Yu-yan Wang, Cun-gang Gao, Kai-zhou Li, Jun-qing Sang, Hong-yan |
| Author_xml | – sequence: 1 givenname: Jun-qing surname: Li fullname: Li, Jun-qing email: lijunqing@lcu-cs.com organization: School of Information Science and Engineering, Shandong Normal University, 250014, PR China – sequence: 2 givenname: Hong-yan surname: Sang fullname: Sang, Hong-yan email: sanghongyan@lcu-cs.com organization: College of Computer Science, Liaocheng University, Liaocheng 252059, PR China – sequence: 3 givenname: Yu-yan surname: Han fullname: Han, Yu-yan organization: College of Computer Science, Liaocheng University, Liaocheng 252059, PR China – sequence: 4 givenname: Cun-gang surname: Wang fullname: Wang, Cun-gang organization: College of Computer Science, Liaocheng University, Liaocheng 252059, PR China – sequence: 5 givenname: Kai-zhou surname: Gao fullname: Gao, Kai-zhou organization: College of Computer Science, Liaocheng University, Liaocheng 252059, PR China |
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| Cites_doi | 10.1016/j.cie.2013.04.007 10.1016/j.ejor.2015.10.007 10.1016/j.cor.2009.11.001 10.1109/TEVC.2007.892759 10.1109/70.282536 10.1016/j.ins.2014.10.009 10.1109/TASE.2015.2425404 10.1016/j.ejor.2009.09.024 10.1080/00207543.2014.910627 10.1016/j.omega.2013.12.004 10.1007/s10845-014-0874-y 10.1109/TCYB.2015.2444383 10.1016/j.compind.2015.10.001 10.1016/0305-0483(83)90088-9 10.1016/j.asoc.2014.07.005 10.1109/TEVC.2013.2281535 10.1016/j.asoc.2013.03.006 10.1080/0951192X.2014.880803 10.1016/j.cor.2015.05.010 10.1016/S0377-2217(97)00333-0 10.1016/j.ijpe.2016.06.005 10.1016/j.apm.2013.07.038 10.1016/S0377-2217(98)00060-5 10.1016/j.ejor.2015.07.017 10.1016/j.jclepro.2016.03.150 10.1051/ro:2000103 10.1016/j.rcim.2013.04.001 10.1109/TEVC.2014.2339823 10.1016/j.cie.2016.05.006 10.1007/s10951-005-1640-y 10.1016/j.asoc.2012.01.011 10.1016/j.ejor.2015.05.019 10.1057/jors.1988.63 10.1016/j.cie.2015.06.002 10.1080/0952813X.2012.682752 10.1109/TEVC.2013.2240304 10.1109/TEVC.2014.2373386 10.1016/j.ins.2009.12.025 10.1109/4235.996017 10.1016/j.jclepro.2017.01.011 10.1080/00207721.2014.886742 10.1016/S0925-5273(03)00011-2 10.1016/j.ijpe.2013.01.028 10.1016/j.cirp.2014.03.011 10.1016/j.asoc.2010.12.006 10.1016/j.jclepro.2015.09.097 10.1109/TASE.2015.2495328 10.1080/00207543.2013.847983 10.1016/j.automatica.2015.09.013 10.1016/j.cor.2008.10.008 10.1080/0305215X.2012.685072 10.1016/j.asoc.2014.02.005 10.1016/j.ijpe.2012.05.015 |
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| References | Tran, Ng (bib49) 2013; 45 Wang, Liu (bib50) 2014; 52 Li, Pan, Tasgetiren (bib22) 2014; 38 Che, Zeng, Lyu (bib5) 2016; 129 Li, Meng, Liang, Zhao (bib23) 2015; 26 Zhang, Li (bib54) 2007; 11 Chou (bib6) 2013; 141 Gahm, Denz, Dirr, Tuma (bib15) 2016; 248 Lin, Liao (bib28) 2003; 86 Lei, Guo (bib19) 2016; 65 Li, Pan, Mao (bib26) 2016; 13 Dai, Tang, Giret, Salido, Li (bib8) 2013; 29 Marichelvam, Prabaharan, Yang (bib32) 2014; 18 Carlier, Néron (bib3) 2000; 34 Luo, Du, Huang, Chen, Li (bib30) 2013; 146 Portmann, Vignier, Dardilhac, Dezalay (bib41) 1998; 107 Tang, Dai, Salido, Giret (bib48) 2016; 81 Fu, Faust, Chachuat, Mitsos (bib14) 2015; 62 Pan, Tasgetiren, Suganthan, Chua (bib39) 2011; 181 Oĝuz, Ercan (bib36) 2005; 8 Engin, Ceran, Yilmaz (bib13) 2011; 11 Ruiz, Vázquez Rodríguez (bib45) 2010; 205 Chung, Liao (bib7) 2013; 13 Deb, Pratap, Agarwal, Meyarivan (bib10) 2002; 6 Chang, Liao (bib4) 1994; 10 Liao, Tjandradjaja, Chung (bib27) 2012; 12 Ying, Lin, Wan (bib52) 2014; 52 Huang, Yang, Hsu (bib17) 2015; 46 Gupta (bib16) 1988; 39 Deb, Jain (bib9) 2014; 18 Ribas, Leisten, Framiñan (bib44) 2010; 37 Sukkerd, Wuttipornpun (bib47) 2016; 97 Bożejko, Pempera, Smutnicki (bib2) 2013; 65 Nawaz, Enscore, Ham (bib35) 1983; 11 Pan, Wang, Qian (bib38) 2009; 36 Marichelvam, Prabaharan, Yang, Geetha (bib31) 2013; 5 Riane, Artiba, Elmaghraby (bib43) 1998; 109 Zhang, Zhao, Fang, Sutherland (bib55) 2014; 63 Ding, Song, Wu (bib11) 2016; 248 Ding, Song, Zhang, Wu (bib12) 2016; 13 Li, Deb, Zhang, Kwong (bib24) 2015; 19 Lu, Gao, Li, Pan, Wang (bib29) 2017; 144 Qin, Zhang, Song (bib42) 2015 Wang, Wang, Liu, Xu (bib51) 2015; 28 Jiang, Liu, Hao, Qian (bib18) 2015; 87 Shahvari, Logendran (bib46) 2016; 179 Moradinasab, Shafaei, Rabiee, Ramezani (bib34) 2013; 25 Li, Pan (bib20) 2015; 316 Li, Pan, Wang (bib21) 2014; 24 Pan, Wang, Li, Duan (bib40) 2014; 45 Asafuddoula, Ray, Sarker (bib1) 2015; 19 Marichelvam, Prabaharan, Yang (bib33) 2014; 19 Pan (bib37) 2016; 250 Li, Pan, Duan (bib25) 2016; 46 Zhang, Chiong (bib53) 2016; 112 Zhang (10.1016/j.jclepro.2018.02.004_bib53) 2016; 112 Huang (10.1016/j.jclepro.2018.02.004_bib17) 2015; 46 Deb (10.1016/j.jclepro.2018.02.004_bib9) 2014; 18 Dai (10.1016/j.jclepro.2018.02.004_bib8) 2013; 29 Zhang (10.1016/j.jclepro.2018.02.004_bib54) 2007; 11 Ruiz (10.1016/j.jclepro.2018.02.004_bib45) 2010; 205 Che (10.1016/j.jclepro.2018.02.004_bib5) 2016; 129 Moradinasab (10.1016/j.jclepro.2018.02.004_bib34) 2013; 25 Tran (10.1016/j.jclepro.2018.02.004_bib49) 2013; 45 Lin (10.1016/j.jclepro.2018.02.004_bib28) 2003; 86 Ding (10.1016/j.jclepro.2018.02.004_bib11) 2016; 248 Lei (10.1016/j.jclepro.2018.02.004_bib19) 2016; 65 Lu (10.1016/j.jclepro.2018.02.004_bib29) 2017; 144 Gupta (10.1016/j.jclepro.2018.02.004_bib16) 1988; 39 Li (10.1016/j.jclepro.2018.02.004_bib23) 2015; 26 Shahvari (10.1016/j.jclepro.2018.02.004_bib46) 2016; 179 Li (10.1016/j.jclepro.2018.02.004_bib24) 2015; 19 Ribas (10.1016/j.jclepro.2018.02.004_bib44) 2010; 37 Gahm (10.1016/j.jclepro.2018.02.004_bib15) 2016; 248 Liao (10.1016/j.jclepro.2018.02.004_bib27) 2012; 12 Chou (10.1016/j.jclepro.2018.02.004_bib6) 2013; 141 Marichelvam (10.1016/j.jclepro.2018.02.004_bib32) 2014; 18 Luo (10.1016/j.jclepro.2018.02.004_bib30) 2013; 146 Zhang (10.1016/j.jclepro.2018.02.004_bib55) 2014; 63 Engin (10.1016/j.jclepro.2018.02.004_bib13) 2011; 11 Li (10.1016/j.jclepro.2018.02.004_bib22) 2014; 38 Fu (10.1016/j.jclepro.2018.02.004_bib14) 2015; 62 Li (10.1016/j.jclepro.2018.02.004_bib21) 2014; 24 Wang (10.1016/j.jclepro.2018.02.004_bib50) 2014; 52 Marichelvam (10.1016/j.jclepro.2018.02.004_bib31) 2013; 5 Li (10.1016/j.jclepro.2018.02.004_bib20) 2015; 316 Asafuddoula (10.1016/j.jclepro.2018.02.004_bib1) 2015; 19 Li (10.1016/j.jclepro.2018.02.004_bib25) 2016; 46 Deb (10.1016/j.jclepro.2018.02.004_bib10) 2002; 6 Wang (10.1016/j.jclepro.2018.02.004_bib51) 2015; 28 Pan (10.1016/j.jclepro.2018.02.004_bib39) 2011; 181 Riane (10.1016/j.jclepro.2018.02.004_bib43) 1998; 109 Li (10.1016/j.jclepro.2018.02.004_bib26) 2016; 13 Portmann (10.1016/j.jclepro.2018.02.004_bib41) 1998; 107 Bożejko (10.1016/j.jclepro.2018.02.004_bib2) 2013; 65 Sukkerd (10.1016/j.jclepro.2018.02.004_bib47) 2016; 97 Qin (10.1016/j.jclepro.2018.02.004_bib42) 2015 Pan (10.1016/j.jclepro.2018.02.004_bib37) 2016; 250 Carlier (10.1016/j.jclepro.2018.02.004_bib3) 2000; 34 Chang (10.1016/j.jclepro.2018.02.004_bib4) 1994; 10 Ding (10.1016/j.jclepro.2018.02.004_bib12) 2016; 13 Marichelvam (10.1016/j.jclepro.2018.02.004_bib33) 2014; 19 Jiang (10.1016/j.jclepro.2018.02.004_bib18) 2015; 87 Ying (10.1016/j.jclepro.2018.02.004_bib52) 2014; 52 Nawaz (10.1016/j.jclepro.2018.02.004_bib35) 1983; 11 Chung (10.1016/j.jclepro.2018.02.004_bib7) 2013; 13 Oĝuz (10.1016/j.jclepro.2018.02.004_bib36) 2005; 8 Pan (10.1016/j.jclepro.2018.02.004_bib38) 2009; 36 Tang (10.1016/j.jclepro.2018.02.004_bib48) 2016; 81 Pan (10.1016/j.jclepro.2018.02.004_bib40) 2014; 45 |
| References_xml | – volume: 129 start-page: 565 year: 2016 end-page: 577 ident: bib5 article-title: An efficient greedy insertion heuristic for energy-conscious single machine scheduling problem under time-of-use electricity tariffs publication-title: J. Clean. Prod. – volume: 18 start-page: 301 year: 2014 end-page: 305 ident: bib32 article-title: A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems publication-title: IEEE Trans. Evol. Comput. – volume: 18 start-page: 577 year: 2014 end-page: 601 ident: bib9 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints publication-title: IEEE Trans. Evol. Comput. – volume: 248 start-page: 744 year: 2016 end-page: 757 ident: bib15 article-title: Energy-efficient scheduling in manufacturing companies: a review and research framework publication-title: Eur. J. Oper. Res. – volume: 38 start-page: 1111 year: 2014 end-page: 1132 ident: bib22 article-title: A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities publication-title: Appl. Math. Model. – volume: 11 start-page: 3056 year: 2011 end-page: 3065 ident: bib13 article-title: An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems publication-title: Appl. Soft Comput. – volume: 181 start-page: 2455 year: 2011 end-page: 2468 ident: bib39 article-title: A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem publication-title: Inf. Sci. – volume: 144 start-page: 228 year: 2017 end-page: 238 ident: bib29 article-title: Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm publication-title: J. Clean. Prod. – volume: 19 start-page: 694 year: 2015 end-page: 716 ident: bib24 article-title: An evolutionary many-objective optimization algorithm based on dominance and decomposition publication-title: IEEE Trans. Evol. Comput. – volume: 5 start-page: 15 year: 2013 end-page: 29 ident: bib31 article-title: Solving hybrid flow shop scheduling problems using bat algorithm publication-title: Int. J. Logist. Econ. Glob. – volume: 107 start-page: 389 year: 1998 end-page: 400 ident: bib41 article-title: Branch and bound crossed with GA to solve hybrid flowshops publication-title: Eur. J. Oper. Res. – volume: 63 start-page: 37 year: 2014 end-page: 40 ident: bib55 article-title: Energy-conscious flow shop scheduling under time-of-use electricity tariffs publication-title: CIRP Ann. - Manuf. Technol. – volume: 109 start-page: 321 year: 1998 end-page: 329 ident: bib43 article-title: A hybrid three-stage flowshop problem: efficient heuristics to minimize makespan publication-title: Eur. J. Oper. Res. – volume: 179 start-page: 239 year: 2016 end-page: 258 ident: bib46 article-title: Hybrid flow shop batching and scheduling with a bi-criteria objective publication-title: Int. J. Prod. Econ. – volume: 10 start-page: 112 year: 1994 end-page: 122 ident: bib4 article-title: Scheduling flexible flow shops with no setup effects publication-title: IEEE Trans. Robot. Autom. – volume: 97 start-page: 157 year: 2016 end-page: 169 ident: bib47 article-title: Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operations publication-title: Comput. Ind. Eng. – volume: 65 start-page: 76 year: 2016 end-page: 82 ident: bib19 article-title: Hybrid flow shop scheduling with not-all-machines options via local search with controlled deterioration publication-title: Comput. Oper. Res. – volume: 28 start-page: 307 year: 2015 end-page: 320 ident: bib51 article-title: An order-based estimation of distribution algorithm for stochastic hybrid flow-shop scheduling problem publication-title: Int. J. Comput. Integrated Manuf. – volume: 316 start-page: 487 year: 2015 end-page: 502 ident: bib20 article-title: Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm publication-title: Inf. Sci. – volume: 39 start-page: 359 year: 1988 end-page: 364 ident: bib16 article-title: Two-stage, hybrid flow shop scheduling problem publication-title: J. Oper. Res. Soc. – volume: 12 start-page: 1755 year: 2012 end-page: 1764 ident: bib27 article-title: An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem publication-title: Appl. Soft Comput. – volume: 81 start-page: 82 year: 2016 end-page: 95 ident: bib48 article-title: Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization publication-title: Comput. Ind. – volume: 11 start-page: 712 year: 2007 end-page: 731 ident: bib54 article-title: MOEA/D: a multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. – volume: 87 start-page: 518 year: 2015 end-page: 531 ident: bib18 article-title: A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry publication-title: Comput. Ind. Eng. – volume: 250 start-page: 702 year: 2016 end-page: 714 ident: bib37 article-title: An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling publication-title: Eur. J. Oper. Res. – volume: 205 start-page: 1 year: 2010 end-page: 18 ident: bib45 article-title: The hybrid flow shop scheduling problem publication-title: Eur. J. Oper. Res. – volume: 46 start-page: 3010 year: 2015 end-page: 3018 ident: bib17 article-title: Multi-objective two-stage multiprocessor flow shop scheduling–a subgroup particle swarm optimisation approach publication-title: Int. J. Syst. Sci. – volume: 45 start-page: 483 year: 2013 end-page: 502 ident: bib49 article-title: A hybrid water flow algorithm for multi-objective flexible flow shop scheduling problems publication-title: Eng. Optim. – volume: 24 start-page: 63 year: 2014 end-page: 77 ident: bib21 article-title: A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem publication-title: Appl. Soft Comput. – volume: 19 start-page: 445 year: 2015 end-page: 460 ident: bib1 article-title: A decomposition-based evolutionary algorithm for many objective optimization publication-title: IEEE Trans. Evol. Comput. – volume: 26 start-page: 873 year: 2015 end-page: 890 ident: bib23 article-title: A heuristic-search genetic algorithm for multi-stage hybrid flow shop scheduling with single processing machines and batch processing machines publication-title: J. Intell. Manuf. – volume: 86 start-page: 133 year: 2003 end-page: 143 ident: bib28 article-title: A case study in a two-stage hybrid flow shop with setup time and dedicated machines publication-title: Int. J. Prod. Econ. – volume: 37 start-page: 1439 year: 2010 end-page: 1454 ident: bib44 article-title: Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective publication-title: Comput. Oper. Res. – volume: 13 start-page: 1138 year: 2016 end-page: 1154 ident: bib12 article-title: Parallel machine scheduling under time-of-use electricity prices: new models and optimization approaches publication-title: IEEE Trans. Autom. Sci. Eng. – volume: 19 start-page: 93 year: 2014 end-page: 101 ident: bib33 article-title: Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan publication-title: Appl. Soft Comput. – volume: 52 start-page: 5735 year: 2014 end-page: 5747 ident: bib52 article-title: Bi-objective reentrant hybrid flowshop scheduling: an iterated Pareto greedy algorithm publication-title: Int. J. Prod. Res. – volume: 34 start-page: 1 year: 2000 end-page: 25 ident: bib3 article-title: An exact method for solving the multi-processor flow-shop publication-title: Rairo-Oper. Res. – volume: 146 start-page: 423 year: 2013 end-page: 439 ident: bib30 article-title: Hybrid flow shop scheduling considering machine electricity consumption cost publication-title: Int. J. Prod. Econ. – volume: 62 start-page: 184 year: 2015 end-page: 192 ident: bib14 article-title: Local optimization of dynamic programs with guaranteed satisfaction of path constraints publication-title: Automatica – volume: 13 start-page: 3729 year: 2013 end-page: 3736 ident: bib7 article-title: An immunoglobulin-based artificial immune system for solving the hybrid flow shop problem publication-title: Appl. Soft Comput. – volume: 25 start-page: 207 year: 2013 end-page: 225 ident: bib34 article-title: No-wait two stage hybrid flow shop scheduling with genetic and adaptive imperialist competitive algorithms publication-title: J. Exp. Theor. Artif. Intell. – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: bib10 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. – volume: 13 start-page: 932 year: 2016 end-page: 949 ident: bib26 article-title: Hybrid fruit fly optimization algorithm for the realistic hybrid flowshop rescheduling problem in steelmaking systems publication-title: IEEE Trans. Autom. Sci. Eng. – volume: 112 start-page: 3361 year: 2016 end-page: 3375 ident: bib53 article-title: Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption publication-title: J. Clean. Prod. – volume: 65 start-page: 466 year: 2013 end-page: 474 ident: bib2 article-title: Parallel tabu search algorithm for the hybrid flow shop problem publication-title: Comput. Ind. Eng. – volume: 11 start-page: 91 year: 1983 end-page: 95 ident: bib35 article-title: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem publication-title: Omega – volume: 36 start-page: 2498 year: 2009 end-page: 2511 ident: bib38 article-title: A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems publication-title: Comput. Oper. Res. – year: 2015 ident: bib42 article-title: An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time publication-title: J. Intell. Manuf. – volume: 8 start-page: 323 year: 2005 end-page: 351 ident: bib36 article-title: A genetic algorithm for hybrid flow-shop scheduling with multiprocessor tasks publication-title: J. Sched. – volume: 248 start-page: 758 year: 2016 end-page: 771 ident: bib11 article-title: Carbon-efficient scheduling of flow shops by multi-objective optimization publication-title: Eur. J. Oper. Res. – volume: 141 start-page: 137 year: 2013 end-page: 145 ident: bib6 article-title: Particle swarm optimization with cocktail decoding method for hybrid flow shop scheduling problems with multiprocessor tasks publication-title: Int. J. Prod. Econ. – volume: 29 start-page: 418 year: 2013 end-page: 429 ident: bib8 article-title: Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm publication-title: Robot. Cim-Int. Manuf – volume: 45 start-page: 42 year: 2014 end-page: 56 ident: bib40 article-title: A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation publication-title: Omega-Int. J. Manage. S. – volume: 52 start-page: 1495 year: 2014 end-page: 1508 ident: bib50 article-title: Two-stage hybrid flow shop scheduling with preventive maintenance using multi-objective tabu search method publication-title: Int. J. Prod. Res. – volume: 46 start-page: 1311 year: 2016 end-page: 1324 ident: bib25 article-title: An improved artificial bee colony algorithm for solving hybrid flexible flowshop with dynamic operation skipping publication-title: IEEE. T. Cyber. – volume: 65 start-page: 466 issue: 3 year: 2013 ident: 10.1016/j.jclepro.2018.02.004_bib2 article-title: Parallel tabu search algorithm for the hybrid flow shop problem publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2013.04.007 – volume: 250 start-page: 702 issue: 3 year: 2016 ident: 10.1016/j.jclepro.2018.02.004_bib37 article-title: An effective co-evolutionary artificial bee colony algorithm for steelmaking-continuous casting scheduling publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2015.10.007 – volume: 37 start-page: 1439 issue: 8 year: 2010 ident: 10.1016/j.jclepro.2018.02.004_bib44 article-title: Review and classification of hybrid flow shop scheduling problems from a production system and a solutions procedure perspective publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2009.11.001 – volume: 11 start-page: 712 issue: 6 year: 2007 ident: 10.1016/j.jclepro.2018.02.004_bib54 article-title: MOEA/D: a multiobjective evolutionary algorithm based on decomposition publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2007.892759 – volume: 10 start-page: 112 issue: 2 year: 1994 ident: 10.1016/j.jclepro.2018.02.004_bib4 article-title: Scheduling flexible flow shops with no setup effects publication-title: IEEE Trans. Robot. Autom. doi: 10.1109/70.282536 – volume: 316 start-page: 487 year: 2015 ident: 10.1016/j.jclepro.2018.02.004_bib20 article-title: Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm publication-title: Inf. Sci. doi: 10.1016/j.ins.2014.10.009 – volume: 13 start-page: 932 issue: 2 year: 2016 ident: 10.1016/j.jclepro.2018.02.004_bib26 article-title: Hybrid fruit fly optimization algorithm for the realistic hybrid flowshop rescheduling problem in steelmaking systems publication-title: IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2015.2425404 – volume: 205 start-page: 1 year: 2010 ident: 10.1016/j.jclepro.2018.02.004_bib45 article-title: The hybrid flow shop scheduling problem publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2009.09.024 – volume: 52 start-page: 5735 issue: 19 year: 2014 ident: 10.1016/j.jclepro.2018.02.004_bib52 article-title: Bi-objective reentrant hybrid flowshop scheduling: an iterated Pareto greedy algorithm publication-title: Int. J. Prod. Res. doi: 10.1080/00207543.2014.910627 – volume: 45 start-page: 42 year: 2014 ident: 10.1016/j.jclepro.2018.02.004_bib40 article-title: A novel discrete artificial bee colony algorithm for the hybrid flowshop scheduling problem with makespan minimisation publication-title: Omega-Int. J. Manage. S. doi: 10.1016/j.omega.2013.12.004 – volume: 26 start-page: 873 issue: 5 year: 2015 ident: 10.1016/j.jclepro.2018.02.004_bib23 article-title: A heuristic-search genetic algorithm for multi-stage hybrid flow shop scheduling with single processing machines and batch processing machines publication-title: J. Intell. Manuf. doi: 10.1007/s10845-014-0874-y – volume: 46 start-page: 1311 issue: 6 year: 2016 ident: 10.1016/j.jclepro.2018.02.004_bib25 article-title: An improved artificial bee colony algorithm for solving hybrid flexible flowshop with dynamic operation skipping publication-title: IEEE. T. Cyber. doi: 10.1109/TCYB.2015.2444383 – volume: 5 start-page: 15 issue: 1 year: 2013 ident: 10.1016/j.jclepro.2018.02.004_bib31 article-title: Solving hybrid flow shop scheduling problems using bat algorithm publication-title: Int. J. Logist. Econ. Glob. – volume: 81 start-page: 82 year: 2016 ident: 10.1016/j.jclepro.2018.02.004_bib48 article-title: Energy-efficient dynamic scheduling for a flexible flow shop using an improved particle swarm optimization publication-title: Comput. Ind. doi: 10.1016/j.compind.2015.10.001 – volume: 11 start-page: 91 issue: 1 year: 1983 ident: 10.1016/j.jclepro.2018.02.004_bib35 article-title: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem publication-title: Omega doi: 10.1016/0305-0483(83)90088-9 – volume: 24 start-page: 63 year: 2014 ident: 10.1016/j.jclepro.2018.02.004_bib21 article-title: A hybrid variable neighborhood search for solving the hybrid flow shop scheduling problem publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2014.07.005 – volume: 18 start-page: 577 issue: 4 year: 2014 ident: 10.1016/j.jclepro.2018.02.004_bib9 article-title: An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, part I: solving problems with box constraints publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2013.2281535 – volume: 13 start-page: 3729 issue: 8 year: 2013 ident: 10.1016/j.jclepro.2018.02.004_bib7 article-title: An immunoglobulin-based artificial immune system for solving the hybrid flow shop problem publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2013.03.006 – volume: 28 start-page: 307 issue: 3 year: 2015 ident: 10.1016/j.jclepro.2018.02.004_bib51 article-title: An order-based estimation of distribution algorithm for stochastic hybrid flow-shop scheduling problem publication-title: Int. J. Comput. Integrated Manuf. doi: 10.1080/0951192X.2014.880803 – volume: 65 start-page: 76 year: 2016 ident: 10.1016/j.jclepro.2018.02.004_bib19 article-title: Hybrid flow shop scheduling with not-all-machines options via local search with controlled deterioration publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2015.05.010 – volume: 107 start-page: 389 issue: 2 year: 1998 ident: 10.1016/j.jclepro.2018.02.004_bib41 article-title: Branch and bound crossed with GA to solve hybrid flowshops publication-title: Eur. J. Oper. Res. doi: 10.1016/S0377-2217(97)00333-0 – volume: 179 start-page: 239 year: 2016 ident: 10.1016/j.jclepro.2018.02.004_bib46 article-title: Hybrid flow shop batching and scheduling with a bi-criteria objective publication-title: Int. J. Prod. Econ. doi: 10.1016/j.ijpe.2016.06.005 – volume: 38 start-page: 1111 issue: 3 year: 2014 ident: 10.1016/j.jclepro.2018.02.004_bib22 article-title: A discrete artificial bee colony algorithm for the multi-objective flexible job-shop scheduling problem with maintenance activities publication-title: Appl. Math. Model. doi: 10.1016/j.apm.2013.07.038 – volume: 109 start-page: 321 issue: 2 year: 1998 ident: 10.1016/j.jclepro.2018.02.004_bib43 article-title: A hybrid three-stage flowshop problem: efficient heuristics to minimize makespan publication-title: Eur. J. Oper. Res. doi: 10.1016/S0377-2217(98)00060-5 – volume: 248 start-page: 744 issue: 3 year: 2016 ident: 10.1016/j.jclepro.2018.02.004_bib15 article-title: Energy-efficient scheduling in manufacturing companies: a review and research framework publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2015.07.017 – volume: 129 start-page: 565 year: 2016 ident: 10.1016/j.jclepro.2018.02.004_bib5 article-title: An efficient greedy insertion heuristic for energy-conscious single machine scheduling problem under time-of-use electricity tariffs publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2016.03.150 – volume: 34 start-page: 1 issue: 1 year: 2000 ident: 10.1016/j.jclepro.2018.02.004_bib3 article-title: An exact method for solving the multi-processor flow-shop publication-title: Rairo-Oper. Res. doi: 10.1051/ro:2000103 – volume: 29 start-page: 418 year: 2013 ident: 10.1016/j.jclepro.2018.02.004_bib8 article-title: Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm publication-title: Robot. Cim-Int. Manuf doi: 10.1016/j.rcim.2013.04.001 – volume: 19 start-page: 445 issue: 3 year: 2015 ident: 10.1016/j.jclepro.2018.02.004_bib1 article-title: A decomposition-based evolutionary algorithm for many objective optimization publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2014.2339823 – volume: 97 start-page: 157 year: 2016 ident: 10.1016/j.jclepro.2018.02.004_bib47 article-title: Hybrid genetic algorithm and tabu search for finite capacity material requirement planning system in flexible flow shop with assembly operations publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2016.05.006 – volume: 8 start-page: 323 issue: 4 year: 2005 ident: 10.1016/j.jclepro.2018.02.004_bib36 article-title: A genetic algorithm for hybrid flow-shop scheduling with multiprocessor tasks publication-title: J. Sched. doi: 10.1007/s10951-005-1640-y – volume: 12 start-page: 1755 issue: 6 year: 2012 ident: 10.1016/j.jclepro.2018.02.004_bib27 article-title: An approach using particle swarm optimization and bottleneck heuristic to solve hybrid flow shop scheduling problem publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2012.01.011 – volume: 248 start-page: 758 year: 2016 ident: 10.1016/j.jclepro.2018.02.004_bib11 article-title: Carbon-efficient scheduling of flow shops by multi-objective optimization publication-title: Eur. J. Oper. Res. doi: 10.1016/j.ejor.2015.05.019 – volume: 39 start-page: 359 year: 1988 ident: 10.1016/j.jclepro.2018.02.004_bib16 article-title: Two-stage, hybrid flow shop scheduling problem publication-title: J. Oper. Res. Soc. doi: 10.1057/jors.1988.63 – volume: 87 start-page: 518 year: 2015 ident: 10.1016/j.jclepro.2018.02.004_bib18 article-title: A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry publication-title: Comput. Ind. Eng. doi: 10.1016/j.cie.2015.06.002 – volume: 25 start-page: 207 issue: 2 year: 2013 ident: 10.1016/j.jclepro.2018.02.004_bib34 article-title: No-wait two stage hybrid flow shop scheduling with genetic and adaptive imperialist competitive algorithms publication-title: J. Exp. Theor. Artif. Intell. doi: 10.1080/0952813X.2012.682752 – volume: 18 start-page: 301 issue: 2 year: 2014 ident: 10.1016/j.jclepro.2018.02.004_bib32 article-title: A discrete firefly algorithm for the multi-objective hybrid flowshop scheduling problems publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2013.2240304 – volume: 19 start-page: 694 issue: 5 year: 2015 ident: 10.1016/j.jclepro.2018.02.004_bib24 article-title: An evolutionary many-objective optimization algorithm based on dominance and decomposition publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/TEVC.2014.2373386 – volume: 181 start-page: 2455 year: 2011 ident: 10.1016/j.jclepro.2018.02.004_bib39 article-title: A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem publication-title: Inf. Sci. doi: 10.1016/j.ins.2009.12.025 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.jclepro.2018.02.004_bib10 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Trans. Evol. Comput. doi: 10.1109/4235.996017 – volume: 144 start-page: 228 year: 2017 ident: 10.1016/j.jclepro.2018.02.004_bib29 article-title: Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2017.01.011 – volume: 46 start-page: 3010 issue: 16 year: 2015 ident: 10.1016/j.jclepro.2018.02.004_bib17 article-title: Multi-objective two-stage multiprocessor flow shop scheduling–a subgroup particle swarm optimisation approach publication-title: Int. J. Syst. Sci. doi: 10.1080/00207721.2014.886742 – volume: 86 start-page: 133 issue: 2 year: 2003 ident: 10.1016/j.jclepro.2018.02.004_bib28 article-title: A case study in a two-stage hybrid flow shop with setup time and dedicated machines publication-title: Int. J. Prod. Econ. doi: 10.1016/S0925-5273(03)00011-2 – volume: 146 start-page: 423 year: 2013 ident: 10.1016/j.jclepro.2018.02.004_bib30 article-title: Hybrid flow shop scheduling considering machine electricity consumption cost publication-title: Int. J. Prod. Econ. doi: 10.1016/j.ijpe.2013.01.028 – year: 2015 ident: 10.1016/j.jclepro.2018.02.004_bib42 article-title: An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time publication-title: J. Intell. Manuf. – volume: 63 start-page: 37 year: 2014 ident: 10.1016/j.jclepro.2018.02.004_bib55 article-title: Energy-conscious flow shop scheduling under time-of-use electricity tariffs publication-title: CIRP Ann. - Manuf. Technol. doi: 10.1016/j.cirp.2014.03.011 – volume: 11 start-page: 3056 issue: 3 year: 2011 ident: 10.1016/j.jclepro.2018.02.004_bib13 article-title: An efficient genetic algorithm for hybrid flow shop scheduling with multiprocessor task problems publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2010.12.006 – volume: 112 start-page: 3361 year: 2016 ident: 10.1016/j.jclepro.2018.02.004_bib53 article-title: Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2015.09.097 – volume: 13 start-page: 1138 year: 2016 ident: 10.1016/j.jclepro.2018.02.004_bib12 article-title: Parallel machine scheduling under time-of-use electricity prices: new models and optimization approaches publication-title: IEEE Trans. Autom. Sci. Eng. doi: 10.1109/TASE.2015.2495328 – volume: 52 start-page: 1495 issue: 5 year: 2014 ident: 10.1016/j.jclepro.2018.02.004_bib50 article-title: Two-stage hybrid flow shop scheduling with preventive maintenance using multi-objective tabu search method publication-title: Int. J. Prod. Res. doi: 10.1080/00207543.2013.847983 – volume: 62 start-page: 184 year: 2015 ident: 10.1016/j.jclepro.2018.02.004_bib14 article-title: Local optimization of dynamic programs with guaranteed satisfaction of path constraints publication-title: Automatica doi: 10.1016/j.automatica.2015.09.013 – volume: 36 start-page: 2498 year: 2009 ident: 10.1016/j.jclepro.2018.02.004_bib38 article-title: A novel differential evolution algorithm for bi-criteria no-wait flow shop scheduling problems publication-title: Comput. Oper. Res. doi: 10.1016/j.cor.2008.10.008 – volume: 45 start-page: 483 issue: 4 year: 2013 ident: 10.1016/j.jclepro.2018.02.004_bib49 article-title: A hybrid water flow algorithm for multi-objective flexible flow shop scheduling problems publication-title: Eng. Optim. doi: 10.1080/0305215X.2012.685072 – volume: 19 start-page: 93 year: 2014 ident: 10.1016/j.jclepro.2018.02.004_bib33 article-title: Improved cuckoo search algorithm for hybrid flow shop scheduling problems to minimize makespan publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2014.02.005 – volume: 141 start-page: 137 issue: 1 year: 2013 ident: 10.1016/j.jclepro.2018.02.004_bib6 article-title: Particle swarm optimization with cocktail decoding method for hybrid flow shop scheduling problems with multiprocessor tasks publication-title: Int. J. Prod. Econ. doi: 10.1016/j.ijpe.2012.05.015 |
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| Title | Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions |
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