An effective multi-start iterated greedy algorithm to minimize makespan for the distributed permutation flowshop scheduling problem with preventive maintenance
•We study a distributed flowshop problem with preventive maintenance.•We present a novel multi-start iterated greedy algorithm.•An improved NEH with dropout is proposed to generate searching starting-point.•A destruction with the tournament selection is well designed.•The effective of the iterated g...
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| Published in: | Expert systems with applications Vol. 169; p. 114495 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier Ltd
01.05.2021
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •We study a distributed flowshop problem with preventive maintenance.•We present a novel multi-start iterated greedy algorithm.•An improved NEH with dropout is proposed to generate searching starting-point.•A destruction with the tournament selection is well designed.•The effective of the iterated greedy is proved by extensive experiments.
In recent years, distributed scheduling problems have been well studied for their close connection with multi-factory production networks. However, the maintenance operations that are commonly carried out on a system to restore it to a specific state are seldom taken into consideration. In this paper, we study a distributed permutation flowshop scheduling problem with preventive maintenance operation (PM/DPFSP). A multi-start iterated greedy (MSIG) algorithm is proposed to minimize the makespan. An improved heuristic is presented for the initialization and re-initialization by adding a dropout operation to NEH2 to generate solutions with a high level of quality and disperstiveness. A destruction phase with the tournament selection and a construction phase with an enhanced strategy are introduced to avoid local optima. A local search based on three effective operators is integrated into the MSIG to reinforce local neighborhood solution exploitation. In addition, a restart strategy is adpoted if a solution has not been improved in a certain number of consecutive iterations. We conducted extensive experiments to test the performance of the presented MSIG. The computational results indicate that the presented MSIG has many promising advantages in solving the PM/DPFSP under consideration. |
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| AbstractList | In recent years, distributed scheduling problems have been well studied for their close connection with multi-factory production networks. However, the maintenance operations that are commonly carried out on a system to restore it to a specific state are seldom taken into consideration. In this paper, we study a distributed permutation flowshop scheduling problem with preventive maintenance operation (PM/DPFSP). A multi-start iterated greedy (MSIG) algorithm is proposed to minimize the makespan. An improved heuristic is presented for the initialization and re-initialization by adding a dropout operation to NEH2 to generate solutions with a high level of quality and disperstiveness. A destruction phase with the tournament selection and a construction phase with an enhanced strategy are introduced to avoid local optima. A local search based on three effective operators is integrated into the MSIG to reinforce local neighborhood solution exploitation. In addition, a restart strategy is adpoted if a solution has not been improved in a certain number of consecutive iterations. We conducted extensive experiments to test the performance of the presented MSIG. The computational results indicate that the presented MSIG has many promising advantages in solving the PM/DPFSP under consideration. •We study a distributed flowshop problem with preventive maintenance.•We present a novel multi-start iterated greedy algorithm.•An improved NEH with dropout is proposed to generate searching starting-point.•A destruction with the tournament selection is well designed.•The effective of the iterated greedy is proved by extensive experiments. In recent years, distributed scheduling problems have been well studied for their close connection with multi-factory production networks. However, the maintenance operations that are commonly carried out on a system to restore it to a specific state are seldom taken into consideration. In this paper, we study a distributed permutation flowshop scheduling problem with preventive maintenance operation (PM/DPFSP). A multi-start iterated greedy (MSIG) algorithm is proposed to minimize the makespan. An improved heuristic is presented for the initialization and re-initialization by adding a dropout operation to NEH2 to generate solutions with a high level of quality and disperstiveness. A destruction phase with the tournament selection and a construction phase with an enhanced strategy are introduced to avoid local optima. A local search based on three effective operators is integrated into the MSIG to reinforce local neighborhood solution exploitation. In addition, a restart strategy is adpoted if a solution has not been improved in a certain number of consecutive iterations. We conducted extensive experiments to test the performance of the presented MSIG. The computational results indicate that the presented MSIG has many promising advantages in solving the PM/DPFSP under consideration. |
| ArticleNumber | 114495 |
| Author | Miao, Zhong-hua Gao, Liang Pan, Quan-ke Mao, Jia-yang |
| Author_xml | – sequence: 1 givenname: Jia-yang surname: Mao fullname: Mao, Jia-yang email: maojy1996@qq.com organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, PR China – sequence: 2 givenname: Quan-ke surname: Pan fullname: Pan, Quan-ke email: panquanke@shu.edu.cn organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, PR China – sequence: 3 givenname: Zhong-hua surname: Miao fullname: Miao, Zhong-hua email: zhhmiao@shu.edu.cn organization: School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, PR China – sequence: 4 givenname: Liang surname: Gao fullname: Gao, Liang email: gaoliang@mai.hust.edu.cn organization: The State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science & Technology, Wuhan, PR China |
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| Cites_doi | 10.1080/00207543.2014.948578 10.1016/j.engappai.2020.104016 10.1016/j.apm.2013.03.001 10.1016/j.engappai.2018.10.012 10.1016/j.eswa.2020.113678 10.1080/0305215X.2013.827673 10.1016/j.cor.2009.06.019 10.1057/jors.2015.50 10.1016/j.knosys.2019.104894 10.1016/0377-2217(93)90182-M 10.1016/j.cie.2016.07.027 10.1080/00207543.2011.644819 10.1016/j.swevo.2016.06.002 10.1088/1757-899X/646/1/012037 10.1016/j.asoc.2010.07.008 10.1016/j.amc.2018.12.067 10.1080/00207549308956713 10.1016/j.ijpe.2013.05.004 10.23919/CCC50068.2020.9189642 10.1016/j.ejor.2004.04.017 10.1080/00207543.2013.790571 10.1016/j.cor.2005.12.007 10.1016/j.ejor.2014.05.024 10.1016/j.cie.2020.106320 10.1016/j.ejor.2005.12.009 10.1016/j.engappai.2018.09.005 10.23919/CCC50068.2020.9188697 10.1016/j.ejor.2012.10.012 10.1016/0305-0483(83)90088-9 10.1016/j.eswa.2020.113675 10.1016/j.swevo.2020.100804 10.1016/j.cie.2010.05.016 10.1016/j.swevo.2020.100742 10.1016/j.cie.2018.03.014 10.1016/j.swevo.2020.100803 10.1016/j.ijpe.2011.01.004 10.1016/j.asoc.2015.11.034 10.1080/00207543.2012.677070 10.1016/j.eswa.2017.10.050 10.1109/TSMC.2017.2788879 10.1016/j.cor.2006.04.007 10.1016/j.cie.2017.07.020 10.1109/TSMC.2015.2416127 10.1016/j.omega.2018.03.004 10.1007/s10845-014-0890-y 10.1016/j.asoc.2020.106629 10.1016/j.asoc.2019.105492 |
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| Keywords | Iterated greedy algorithm Makespan Distributed permutation flowshop scheduling problem Heuristic methods Preventive maintenance |
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| References | Maccarthy, Liu (b0095) 1993; 31 Mao, J., Hu, X. L., Pan, Q. K., Miao, Z., He, C. & Tasgetiren, M. F. (2020a). An improved discrete artificial bee colony algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance. Chinese Control Conference, CCC, 2020-July, 1679–1684. Huang, Pan, Gao (b0060) 2020; 59 Wang, Yu (b0210) 2010; 59 Rausand M, & Høyland A. (2003). System Reliability Theory: Models, Statistical Methods, and Applications. [S.l.]: John Wiley & Sons. Taillard (b0185) 1993; 64 Naderi, Zandieh, Aminnayeri (b0135) 2011; 11 Nawaz, Enscore, Ham (b0140) 1983; 11 Behnamian, Ghomi (b0015) 2016; 27 Pan, Gao, Xin-Yu, Jose (b0150) 2019; 81 Wang, Wang, Liu, Xu (b0205) 2013; 145 Arroyo, Leung, Tavares (b0005) 2019; 77 Lin, Ying, Huang (b0080) 2013; 51 Naderi, Ruiz (b0130) 2010; 37 Wong, Chan, Chung (b0215) 2013; 51 Deng, Wang (b0025) 2017; 32 Gao, Chen (b0045) 2011; 4 Wang, Wang (b0195) 2020; 50 Meng, Pan (b0260) 2021; 60 Wang, Huang, Qin (b0190) 2016; 67 Martí, Resende, Ribeiro (b0110) 2013; 226 Xu, Yang (b0220) 2013; 37 Fernandez-Viagas, Perez-Gonzalez, Framinan (b0030) 2018; 118 Zhang, Tu, Wu (b0240) 2019; 349 Gao, Chen, Deng (b0050) 2013; 51 Huang, Pan, Chen (b0055) 2019; 646 Jing, Pan, Gao, Wang (b0070) 2020; 96 Ruiz, Carlos García-Díaz, Maroto (b0165) 2007; 34 Lin, Ying, Lu, Gupta (b0090) 2011; 130 Lei, Liu (b0075) 2020; 141 Naderi, Ruiz (b0125) 2014; 239 Huang, Pan, Miao, Gao (b0065) 2021; 97 Fernandez-Viagas, Framinan (b0035) 2015; 53 Lin, Ying (b0085) 2016; 99 Ruiz, Maroto (b0170) 2005; 165 Xu, Wang, Wang, Liu (b0225) 2014; 46 Meng, Pan, Wang (b0115) 2019; 184 Lu, Gao, Gong, Hu, Yan, Li (b0255) 2021; 60 Birolini (b0020) 2013 Osman, Potts (b0145) 1989; 124 Yang, Hsu, Kuo (b0230) 2008; 35 Zhang, Xing, Cao (b0235) 2018; 76 Ruiz, Stützle (b0180) 2007; 177 Wang, Wang (b0200) 2016; 46 Zou, Pan, Meng, Gao, Wang (b0250) 2020; 161 Bargaoui, Belkahla Driss, Ghédira (b0010) 2017; 111 Ruiz, Pan, Naderi (b0175) 2019; 83 Mao, J., Hu, X. L., Pan, Q. K., Miao, Z., He, C & Tasgetiren, M. F. (2020b). An iterated greedy algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance to minimize total flowtime. Chinese Control Conference, CCC, 2020-July, 1507–1512. Rifai, Nguyen, Dawal (b0160) 2016; 40 Fernandez-Viagas, Valente, Framinan (b0040) 2018; 94 Zhao, Zhao, Wang, Song (b0245) 2020; 160 Birolini (10.1016/j.eswa.2020.114495_b0020) 2013 Jing (10.1016/j.eswa.2020.114495_b0070) 2020; 96 Meng (10.1016/j.eswa.2020.114495_b0260) 2021; 60 Ruiz (10.1016/j.eswa.2020.114495_b0165) 2007; 34 Naderi (10.1016/j.eswa.2020.114495_b0125) 2014; 239 Fernandez-Viagas (10.1016/j.eswa.2020.114495_b0040) 2018; 94 Arroyo (10.1016/j.eswa.2020.114495_b0005) 2019; 77 Wang (10.1016/j.eswa.2020.114495_b0190) 2016; 67 Naderi (10.1016/j.eswa.2020.114495_b0130) 2010; 37 10.1016/j.eswa.2020.114495_b0100 Naderi (10.1016/j.eswa.2020.114495_b0135) 2011; 11 Taillard (10.1016/j.eswa.2020.114495_b0185) 1993; 64 Osman (10.1016/j.eswa.2020.114495_b0145) 1989; 124 Lei (10.1016/j.eswa.2020.114495_b0075) 2020; 141 Ruiz (10.1016/j.eswa.2020.114495_b0175) 2019; 83 Gao (10.1016/j.eswa.2020.114495_b0045) 2011; 4 Ruiz (10.1016/j.eswa.2020.114495_b0180) 2007; 177 Lu (10.1016/j.eswa.2020.114495_b0255) 2021; 60 Huang (10.1016/j.eswa.2020.114495_b0065) 2021; 97 Xu (10.1016/j.eswa.2020.114495_b0225) 2014; 46 Zhao (10.1016/j.eswa.2020.114495_b0245) 2020; 160 Pan (10.1016/j.eswa.2020.114495_b0150) 2019; 81 Lin (10.1016/j.eswa.2020.114495_b0090) 2011; 130 Wang (10.1016/j.eswa.2020.114495_b0205) 2013; 145 Martí (10.1016/j.eswa.2020.114495_b0110) 2013; 226 Yang (10.1016/j.eswa.2020.114495_b0230) 2008; 35 Zhang (10.1016/j.eswa.2020.114495_b0240) 2019; 349 Fernandez-Viagas (10.1016/j.eswa.2020.114495_b0035) 2015; 53 Huang (10.1016/j.eswa.2020.114495_b0055) 2019; 646 Wang (10.1016/j.eswa.2020.114495_b0200) 2016; 46 Xu (10.1016/j.eswa.2020.114495_b0220) 2013; 37 Fernandez-Viagas (10.1016/j.eswa.2020.114495_b0030) 2018; 118 Zou (10.1016/j.eswa.2020.114495_b0250) 2020; 161 Lin (10.1016/j.eswa.2020.114495_b0085) 2016; 99 Behnamian (10.1016/j.eswa.2020.114495_b0015) 2016; 27 Gao (10.1016/j.eswa.2020.114495_b0050) 2013; 51 Deng (10.1016/j.eswa.2020.114495_b0025) 2017; 32 Ruiz (10.1016/j.eswa.2020.114495_b0170) 2005; 165 Huang (10.1016/j.eswa.2020.114495_b0060) 2020; 59 10.1016/j.eswa.2020.114495_b0105 Wong (10.1016/j.eswa.2020.114495_b0215) 2013; 51 Rifai (10.1016/j.eswa.2020.114495_b0160) 2016; 40 Wang (10.1016/j.eswa.2020.114495_b0210) 2010; 59 Lin (10.1016/j.eswa.2020.114495_b0080) 2013; 51 Wang (10.1016/j.eswa.2020.114495_b0195) 2020; 50 Meng (10.1016/j.eswa.2020.114495_b0115) 2019; 184 Zhang (10.1016/j.eswa.2020.114495_b0235) 2018; 76 10.1016/j.eswa.2020.114495_b0155 Bargaoui (10.1016/j.eswa.2020.114495_b0010) 2017; 111 Maccarthy (10.1016/j.eswa.2020.114495_b0095) 1993; 31 Nawaz (10.1016/j.eswa.2020.114495_b0140) 1983; 11 |
| References_xml | – volume: 50 start-page: 1805 year: 2020 end-page: 1819 ident: b0195 article-title: A knowledge-based cooperative algorithm for energy-efficient scheduling of distributed flow-shop publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems – volume: 124 start-page: 309 year: 1989 end-page: 324 ident: b0145 article-title: Simulated Annealing for Permutation Flow-shop Scheduling. publication-title: Expert Systems with Applications – volume: 646 year: 2019 ident: b0055 article-title: A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem with sequence-dependent setup times publication-title: IOP Conference Series: Materials Science and Engineering – volume: 118 start-page: 464 year: 2018 end-page: 477 ident: b0030 article-title: The distributed permutation flow shop to minimise the total flowtime publication-title: Computers & Industrial Engineering – volume: 96 start-page: 106629 year: 2020 ident: b0070 article-title: An effective Iterated Greedy algorithm for the distributed permutation flowshop scheduling with due windows publication-title: Applied Soft Computing – volume: 130 start-page: 246 year: 2011 end-page: 254 ident: b0090 article-title: Applying multi-start simulated annealing to schedule a flowline manufacturing cell with sequence dependent family setup times publication-title: International Journal of Production Economics – volume: 51 start-page: 641 year: 2013 end-page: 651 ident: b0050 article-title: An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem publication-title: International Journal of Production Research – volume: 59 start-page: 100742 year: 2020 ident: b0060 article-title: An effective iterated greedy method for the distributed permutation flowshop scheduling problem with sequence-dependent setup times publication-title: Swarm and Evolutionary Computation – reference: Mao, J., Hu, X. L., Pan, Q. K., Miao, Z., He, C & Tasgetiren, M. F. (2020b). An iterated greedy algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance to minimize total flowtime. Chinese Control Conference, CCC, 2020-July, 1507–1512. – volume: 37 start-page: 7561 year: 2013 end-page: 7567 ident: b0220 article-title: Makespan minimization for two parallel machines scheduling with a periodic availability constraint: Mathematical programming model, average-case analysis, and anomalies publication-title: Applied Mathematical Modelling – volume: 83 start-page: 213 year: 2019 end-page: 222 ident: b0175 article-title: Iterated Greedy methods for the distributed permutation flowshop scheduling problem publication-title: Omega – volume: 51 start-page: 883 year: 2013 end-page: 896 ident: b0215 article-title: A joint production scheduling approach considering multiple resources and preventive maintenance tasks publication-title: International Journal of Production Research – volume: 60 start-page: 100804 year: 2021 ident: b0260 article-title: A distributed heterogeneous permutation flowshop scheduling problem with lot-streaming and carryover sequence-dependent setup time publication-title: Swarm and Evolutionary Computation – year: 2013 ident: b0020 article-title: Reliability engineering: Theory and practice – volume: 60 start-page: 100803 year: 2021 ident: b0255 article-title: Sustainable scheduling of distributed permutation flow-shop with non-identical factory using a knowledge-based multi-objective memetic optimization algorithm publication-title: Swarm and Evolutionary Computation – volume: 239 start-page: 323 year: 2014 end-page: 334 ident: b0125 article-title: A scatter search algorithm for the distributed permutation flowshop scheduling problem publication-title: European Journal of Operational Research – volume: 34 start-page: 3314 year: 2007 end-page: 3330 ident: b0165 article-title: Considering scheduling and preventive maintenance in the flowshop sequencing problem publication-title: Computers & Operations Research – reference: Rausand M, & Høyland A. (2003). System Reliability Theory: Models, Statistical Methods, and Applications. [S.l.]: John Wiley & Sons. – volume: 46 start-page: 139 year: 2016 end-page: 149 ident: b0200 article-title: An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems – volume: 27 start-page: 231 year: 2016 end-page: 249 ident: b0015 article-title: A survey of multi-factory scheduling publication-title: Journal of Intelligent Manufacturing – volume: 32 start-page: 121 year: 2017 end-page: 131 ident: b0025 article-title: A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem publication-title: Swarm and Evolutionary Computation – volume: 161 start-page: 113675 year: 2020 ident: b0250 article-title: An effective discrete artificial bee colony algorithm for multi-AGVs dispatching problem in a matrix manufacturing workshop publication-title: Expert Systems with Applications – volume: 67 start-page: 68 year: 2016 end-page: 82 ident: b0190 article-title: A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown publication-title: Journal of the Operational Research Society – volume: 77 start-page: 239 year: 2019 end-page: 254 ident: b0005 article-title: An iterated greedy algorithm for total flow time minimization in unrelated parallel batch machines with unequal job release times publication-title: Engineering Applications of Artificial Intelligence – volume: 11 start-page: 2094 year: 2011 end-page: 2101 ident: b0135 article-title: Incorporating periodic preventive maintenance into flexible flowshop scheduling problems publication-title: Applied Soft Computing – volume: 177 start-page: 2033 year: 2007 end-page: 2049 ident: b0180 article-title: A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem publication-title: European Journal of Operational Research – volume: 184 start-page: 104894 year: 2019 ident: b0115 article-title: A distributed permutation flowshop scheduling problem with the customer order constraint publication-title: Knowledge-Based Systems – volume: 141 start-page: 106320 year: 2020 ident: b0075 article-title: An artificial bee colony with division for distributed unrelated parallel machine scheduling with preventive maintenance publication-title: Computers & Industrial Engineering – volume: 40 start-page: 42 year: 2016 end-page: 57 ident: b0160 article-title: Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling publication-title: Applied Soft Computing – volume: 99 start-page: 202 year: 2016 end-page: 209 ident: b0085 article-title: Minimizing makespan for solving the distributed no-wait flowshop scheduling problem publication-title: Computers & Industrial Engineering – volume: 37 start-page: 754 year: 2010 end-page: 768 ident: b0130 article-title: The distributed permutation flowshop scheduling problem publication-title: Computers & Operations Research – volume: 51 start-page: 5029 year: 2013 end-page: 5038 ident: b0080 article-title: Minimising makespan in distributed permutation flowshops using a modified iterated greedy algorithm publication-title: International Journal of Production Research – volume: 76 start-page: 96 year: 2018 end-page: 107 ident: b0235 article-title: Discrete differential evolution algorithm for distributed blocking flowshop scheduling with makespan criterion publication-title: Engineering Applications of Artificial Intelligence – reference: Mao, J., Hu, X. L., Pan, Q. K., Miao, Z., He, C. & Tasgetiren, M. F. (2020a). An improved discrete artificial bee colony algorithm for the distributed permutation flowshop scheduling problem with preventive maintenance. Chinese Control Conference, CCC, 2020-July, 1679–1684. – volume: 31 start-page: 59 year: 1993 end-page: 79 ident: b0095 article-title: Addressing the gap in scheduling research: A review of optimization and heuristic methods in production scheduling publication-title: International Journal of Production Research – volume: 46 start-page: 1269 year: 2014 end-page: 1283 ident: b0225 article-title: An effective hybrid immune algorithm for solving the distributed permutation flow-shop scheduling problem publication-title: Engineering Optimization – volume: 160 start-page: 113678 year: 2020 ident: b0245 article-title: An ensemble discrete differential evolution for the distributed blocking flowshop scheduling with minimizing makespan criterion publication-title: Expert Systems with Applications – volume: 53 start-page: 1111 year: 2015 end-page: 1123 ident: b0035 article-title: A bounded-search iterated greedy algorithm for the distributed permutation flowshop scheduling problem publication-title: International Journal of Production Research – volume: 165 start-page: 479 year: 2005 end-page: 494 ident: b0170 article-title: A comprehensive review and evaluation of permutation flowshop heuristics publication-title: European Journal of Operational Research – volume: 81 start-page: 105492 year: 2019 ident: b0150 article-title: Effective constructive heuristics and meta-heuristics for the distributed assembly permutation flowshop scheduling problem publication-title: Applied Soft Computing – volume: 145 start-page: 387 year: 2013 end-page: 396 ident: b0205 article-title: An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem publication-title: International Journal of Production Economics – volume: 94 start-page: 58 year: 2018 end-page: 69 ident: b0040 article-title: Iterated-greedy-based algorithms with beam search initialization for the permutation flowshop to minimise total tardiness publication-title: Expert Systems with Applications – volume: 4 start-page: 497 year: 2011 end-page: 508 ident: b0045 article-title: A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem publication-title: International Journal of Computational Intelligence Systems – volume: 111 start-page: 239 year: 2017 end-page: 250 ident: b0010 article-title: A novel chemical reaction optimization for the distributed permutation flowshop scheduling problem with makespan criterion publication-title: Computers & Industrial Engineering – volume: 11 start-page: 91 year: 1983 end-page: 95 ident: b0140 article-title: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem publication-title: Omega – volume: 226 start-page: 1 year: 2013 end-page: 8 ident: b0110 article-title: Multi-start methods for combinatorial optimization publication-title: European Journal of Operational Research – volume: 35 start-page: 876 year: 2008 end-page: 883 ident: b0230 article-title: A two-machine flowshop scheduling problem with a separated maintenance constraint publication-title: Computers & Operations Research – volume: 97 start-page: 104016 year: 2021 ident: b0065 article-title: Effective constructive heuristics and discrete bee colony optimization for distributed flowshop with setup times publication-title: Engineering Applications of Artificial Intelligence – volume: 64 start-page: 278 year: 1993 end-page: 285 ident: b0185 article-title: Benchmarks for basic scheduling problems publication-title: European Journal of Operational Research – volume: 59 start-page: 436 year: 2010 end-page: 447 ident: b0210 article-title: An effective heuristic for flexible job-shop scheduling problem with maintenance activities publication-title: Computers & Industrial Engineering – volume: 349 start-page: 359 year: 2019 end-page: 366 ident: b0240 article-title: A multi-start iterated greedy algorithm for the minimum weight vertex cover P3 problem publication-title: Applied Mathematics and Computation – volume: 53 start-page: 1111 issue: 4 year: 2015 ident: 10.1016/j.eswa.2020.114495_b0035 article-title: A bounded-search iterated greedy algorithm for the distributed permutation flowshop scheduling problem publication-title: International Journal of Production Research doi: 10.1080/00207543.2014.948578 – volume: 97 start-page: 104016 year: 2021 ident: 10.1016/j.eswa.2020.114495_b0065 article-title: Effective constructive heuristics and discrete bee colony optimization for distributed flowshop with setup times publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2020.104016 – volume: 37 start-page: 7561 issue: 14–15 year: 2013 ident: 10.1016/j.eswa.2020.114495_b0220 article-title: Makespan minimization for two parallel machines scheduling with a periodic availability constraint: Mathematical programming model, average-case analysis, and anomalies publication-title: Applied Mathematical Modelling doi: 10.1016/j.apm.2013.03.001 – volume: 77 start-page: 239 year: 2019 ident: 10.1016/j.eswa.2020.114495_b0005 article-title: An iterated greedy algorithm for total flow time minimization in unrelated parallel batch machines with unequal job release times publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2018.10.012 – volume: 160 start-page: 113678 year: 2020 ident: 10.1016/j.eswa.2020.114495_b0245 article-title: An ensemble discrete differential evolution for the distributed blocking flowshop scheduling with minimizing makespan criterion publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113678 – volume: 46 start-page: 1269 issue: 9 year: 2014 ident: 10.1016/j.eswa.2020.114495_b0225 article-title: An effective hybrid immune algorithm for solving the distributed permutation flow-shop scheduling problem publication-title: Engineering Optimization doi: 10.1080/0305215X.2013.827673 – volume: 37 start-page: 754 issue: 4 year: 2010 ident: 10.1016/j.eswa.2020.114495_b0130 article-title: The distributed permutation flowshop scheduling problem publication-title: Computers & Operations Research doi: 10.1016/j.cor.2009.06.019 – volume: 67 start-page: 68 issue: 1 year: 2016 ident: 10.1016/j.eswa.2020.114495_b0190 article-title: A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown publication-title: Journal of the Operational Research Society doi: 10.1057/jors.2015.50 – volume: 184 start-page: 104894 year: 2019 ident: 10.1016/j.eswa.2020.114495_b0115 article-title: A distributed permutation flowshop scheduling problem with the customer order constraint publication-title: Knowledge-Based Systems doi: 10.1016/j.knosys.2019.104894 – volume: 64 start-page: 278 issue: 2 year: 1993 ident: 10.1016/j.eswa.2020.114495_b0185 article-title: Benchmarks for basic scheduling problems publication-title: European Journal of Operational Research doi: 10.1016/0377-2217(93)90182-M – volume: 99 start-page: 202 year: 2016 ident: 10.1016/j.eswa.2020.114495_b0085 article-title: Minimizing makespan for solving the distributed no-wait flowshop scheduling problem publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2016.07.027 – volume: 51 start-page: 641 issue: 3 year: 2013 ident: 10.1016/j.eswa.2020.114495_b0050 article-title: An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem publication-title: International Journal of Production Research doi: 10.1080/00207543.2011.644819 – volume: 32 start-page: 121 year: 2017 ident: 10.1016/j.eswa.2020.114495_b0025 article-title: A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2016.06.002 – volume: 646 issue: 1 year: 2019 ident: 10.1016/j.eswa.2020.114495_b0055 article-title: A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem with sequence-dependent setup times publication-title: IOP Conference Series: Materials Science and Engineering doi: 10.1088/1757-899X/646/1/012037 – volume: 11 start-page: 2094 issue: 2 year: 2011 ident: 10.1016/j.eswa.2020.114495_b0135 article-title: Incorporating periodic preventive maintenance into flexible flowshop scheduling problems publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2010.07.008 – volume: 349 start-page: 359 year: 2019 ident: 10.1016/j.eswa.2020.114495_b0240 article-title: A multi-start iterated greedy algorithm for the minimum weight vertex cover P3 problem publication-title: Applied Mathematics and Computation doi: 10.1016/j.amc.2018.12.067 – volume: 31 start-page: 59 issue: 1 year: 1993 ident: 10.1016/j.eswa.2020.114495_b0095 article-title: Addressing the gap in scheduling research: A review of optimization and heuristic methods in production scheduling publication-title: International Journal of Production Research doi: 10.1080/00207549308956713 – volume: 145 start-page: 387 issue: 1 year: 2013 ident: 10.1016/j.eswa.2020.114495_b0205 article-title: An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2013.05.004 – ident: 10.1016/j.eswa.2020.114495_b0105 doi: 10.23919/CCC50068.2020.9189642 – volume: 165 start-page: 479 issue: 2 year: 2005 ident: 10.1016/j.eswa.2020.114495_b0170 article-title: A comprehensive review and evaluation of permutation flowshop heuristics publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2004.04.017 – volume: 51 start-page: 5029 issue: 16 year: 2013 ident: 10.1016/j.eswa.2020.114495_b0080 article-title: Minimising makespan in distributed permutation flowshops using a modified iterated greedy algorithm publication-title: International Journal of Production Research doi: 10.1080/00207543.2013.790571 – volume: 34 start-page: 3314 issue: 11 year: 2007 ident: 10.1016/j.eswa.2020.114495_b0165 article-title: Considering scheduling and preventive maintenance in the flowshop sequencing problem publication-title: Computers & Operations Research doi: 10.1016/j.cor.2005.12.007 – volume: 239 start-page: 323 issue: 2 year: 2014 ident: 10.1016/j.eswa.2020.114495_b0125 article-title: A scatter search algorithm for the distributed permutation flowshop scheduling problem publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2014.05.024 – volume: 141 start-page: 106320 year: 2020 ident: 10.1016/j.eswa.2020.114495_b0075 article-title: An artificial bee colony with division for distributed unrelated parallel machine scheduling with preventive maintenance publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2020.106320 – volume: 177 start-page: 2033 issue: 3 year: 2007 ident: 10.1016/j.eswa.2020.114495_b0180 article-title: A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2005.12.009 – year: 2013 ident: 10.1016/j.eswa.2020.114495_b0020 – volume: 76 start-page: 96 year: 2018 ident: 10.1016/j.eswa.2020.114495_b0235 article-title: Discrete differential evolution algorithm for distributed blocking flowshop scheduling with makespan criterion publication-title: Engineering Applications of Artificial Intelligence doi: 10.1016/j.engappai.2018.09.005 – ident: 10.1016/j.eswa.2020.114495_b0100 doi: 10.23919/CCC50068.2020.9188697 – volume: 226 start-page: 1 issue: 1 year: 2013 ident: 10.1016/j.eswa.2020.114495_b0110 article-title: Multi-start methods for combinatorial optimization publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2012.10.012 – volume: 11 start-page: 91 issue: 1 year: 1983 ident: 10.1016/j.eswa.2020.114495_b0140 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: 161 start-page: 113675 year: 2020 ident: 10.1016/j.eswa.2020.114495_b0250 article-title: An effective discrete artificial bee colony algorithm for multi-AGVs dispatching problem in a matrix manufacturing workshop publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2020.113675 – volume: 60 start-page: 100804 year: 2021 ident: 10.1016/j.eswa.2020.114495_b0260 article-title: A distributed heterogeneous permutation flowshop scheduling problem with lot-streaming and carryover sequence-dependent setup time publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2020.100804 – volume: 59 start-page: 436 issue: 3 year: 2010 ident: 10.1016/j.eswa.2020.114495_b0210 article-title: An effective heuristic for flexible job-shop scheduling problem with maintenance activities publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2010.05.016 – volume: 59 start-page: 100742 year: 2020 ident: 10.1016/j.eswa.2020.114495_b0060 article-title: An effective iterated greedy method for the distributed permutation flowshop scheduling problem with sequence-dependent setup times publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2020.100742 – volume: 118 start-page: 464 year: 2018 ident: 10.1016/j.eswa.2020.114495_b0030 article-title: The distributed permutation flow shop to minimise the total flowtime publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2018.03.014 – volume: 124 start-page: 309 year: 1989 ident: 10.1016/j.eswa.2020.114495_b0145 article-title: Simulated Annealing for Permutation Flow-shop Scheduling. Omega, 17(6), 551–557. Pan Q, Gao L, Wang L, Liang JJ, & Li X. (2019). Effective Heuristics and Metaheuristics to Minimize Total Flowtime for the Distributed Permutation Flowshop Problem publication-title: Expert Systems with Applications – volume: 60 start-page: 100803 year: 2021 ident: 10.1016/j.eswa.2020.114495_b0255 article-title: Sustainable scheduling of distributed permutation flow-shop with non-identical factory using a knowledge-based multi-objective memetic optimization algorithm publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2020.100803 – volume: 130 start-page: 246 issue: 2 year: 2011 ident: 10.1016/j.eswa.2020.114495_b0090 article-title: Applying multi-start simulated annealing to schedule a flowline manufacturing cell with sequence dependent family setup times publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2011.01.004 – volume: 40 start-page: 42 year: 2016 ident: 10.1016/j.eswa.2020.114495_b0160 article-title: Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2015.11.034 – volume: 51 start-page: 883 issue: 3 year: 2013 ident: 10.1016/j.eswa.2020.114495_b0215 article-title: A joint production scheduling approach considering multiple resources and preventive maintenance tasks publication-title: International Journal of Production Research doi: 10.1080/00207543.2012.677070 – volume: 94 start-page: 58 year: 2018 ident: 10.1016/j.eswa.2020.114495_b0040 article-title: Iterated-greedy-based algorithms with beam search initialization for the permutation flowshop to minimise total tardiness publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2017.10.050 – volume: 4 start-page: 497 issue: 4 year: 2011 ident: 10.1016/j.eswa.2020.114495_b0045 article-title: A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem publication-title: International Journal of Computational Intelligence Systems – volume: 50 start-page: 1805 issue: 5 year: 2020 ident: 10.1016/j.eswa.2020.114495_b0195 article-title: A knowledge-based cooperative algorithm for energy-efficient scheduling of distributed flow-shop publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems doi: 10.1109/TSMC.2017.2788879 – volume: 35 start-page: 876 issue: 3 year: 2008 ident: 10.1016/j.eswa.2020.114495_b0230 article-title: A two-machine flowshop scheduling problem with a separated maintenance constraint publication-title: Computers & Operations Research doi: 10.1016/j.cor.2006.04.007 – ident: 10.1016/j.eswa.2020.114495_b0155 – volume: 111 start-page: 239 year: 2017 ident: 10.1016/j.eswa.2020.114495_b0010 article-title: A novel chemical reaction optimization for the distributed permutation flowshop scheduling problem with makespan criterion publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2017.07.020 – volume: 46 start-page: 139 issue: 1 year: 2016 ident: 10.1016/j.eswa.2020.114495_b0200 article-title: An estimation of distribution algorithm-based memetic algorithm for the distributed assembly permutation flow-shop scheduling problem publication-title: IEEE Transactions on Systems, Man, and Cybernetics: Systems doi: 10.1109/TSMC.2015.2416127 – volume: 83 start-page: 213 year: 2019 ident: 10.1016/j.eswa.2020.114495_b0175 article-title: Iterated Greedy methods for the distributed permutation flowshop scheduling problem publication-title: Omega doi: 10.1016/j.omega.2018.03.004 – volume: 27 start-page: 231 issue: 1 year: 2016 ident: 10.1016/j.eswa.2020.114495_b0015 article-title: A survey of multi-factory scheduling publication-title: Journal of Intelligent Manufacturing doi: 10.1007/s10845-014-0890-y – volume: 96 start-page: 106629 year: 2020 ident: 10.1016/j.eswa.2020.114495_b0070 article-title: An effective Iterated Greedy algorithm for the distributed permutation flowshop scheduling with due windows publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2020.106629 – volume: 81 start-page: 105492 year: 2019 ident: 10.1016/j.eswa.2020.114495_b0150 article-title: Effective constructive heuristics and meta-heuristics for the distributed assembly permutation flowshop scheduling problem publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2019.105492 |
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| Snippet | •We study a distributed flowshop problem with preventive maintenance.•We present a novel multi-start iterated greedy algorithm.•An improved NEH with dropout is... In recent years, distributed scheduling problems have been well studied for their close connection with multi-factory production networks. However, the... |
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| SubjectTerms | Distributed permutation flowshop scheduling problem Greedy algorithms Heuristic methods Iterated greedy algorithm Job shops Makespan Permutations Preventive maintenance Scheduling |
| Title | An effective multi-start iterated greedy algorithm to minimize makespan for the distributed permutation flowshop scheduling problem with preventive maintenance |
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