Energy aware semi-automatic assembly line balancing problem considering ergonomic risk and uncertain processing time
The semiautomated assembly line balancing problem (SAALBP) is a complex planning problem in which the production process comprises human workers, robots, and human-robot collaboration. With increasing awareness of sustainable practices in the manufacturing environment, this study addresses a sustain...
Uložené v:
| Vydané v: | Expert systems with applications Ročník 231; s. 120737 |
|---|---|
| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
30.11.2023
|
| Predmet: | |
| ISSN: | 0957-4174, 1873-6793 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | The semiautomated assembly line balancing problem (SAALBP) is a complex planning problem in which the production process comprises human workers, robots, and human-robot collaboration. With increasing awareness of sustainable practices in the manufacturing environment, this study addresses a sustainable SSALBP, where a mixed integer linear programming (MILP) model is formulated for the first time for this type of assembly systems. The model consists of three objectives and each of the objectives represents the three pillars of sustainability. The problem becomes more realistic by considering uncertainty in processing time and the influence of human workers’ age and skill in processing time. The uncertainty is adopted in the MILP model by using a chance-constraint programing approach. Since the problem is complex, Q-learning and Monte-Carlo simulation-assisted based genetic algorithm (GA) based memetic algorithm (MA) are proposed to solve the problem. The performance of MA is evaluated against the well-known non-dominated sorting GA-III (NSGA-III) and state-of-the-art algorithms for solving the problem. The experimental results show that MA surpasses its contest significantly in finding better Pareto-fronts. |
|---|---|
| AbstractList | The semiautomated assembly line balancing problem (SAALBP) is a complex planning problem in which the production process comprises human workers, robots, and human-robot collaboration. With increasing awareness of sustainable practices in the manufacturing environment, this study addresses a sustainable SSALBP, where a mixed integer linear programming (MILP) model is formulated for the first time for this type of assembly systems. The model consists of three objectives and each of the objectives represents the three pillars of sustainability. The problem becomes more realistic by considering uncertainty in processing time and the influence of human workers’ age and skill in processing time. The uncertainty is adopted in the MILP model by using a chance-constraint programing approach. Since the problem is complex, Q-learning and Monte-Carlo simulation-assisted based genetic algorithm (GA) based memetic algorithm (MA) are proposed to solve the problem. The performance of MA is evaluated against the well-known non-dominated sorting GA-III (NSGA-III) and state-of-the-art algorithms for solving the problem. The experimental results show that MA surpasses its contest significantly in finding better Pareto-fronts. |
| ArticleNumber | 120737 |
| Author | Janardhanan, Mukund Nilakantan Ponnambalam, S.G. Rahman, Humyun Fuad |
| Author_xml | – sequence: 1 givenname: Humyun Fuad surname: Rahman fullname: Rahman, Humyun Fuad email: frahman@cardiffmet.ac.uk organization: Cardiff School of Management, Cardiff Metropolitan University, Western Avenue, Cardiff CF5 2YB, UK – sequence: 2 givenname: Mukund Nilakantan surname: Janardhanan fullname: Janardhanan, Mukund Nilakantan email: mukund.janardhanan@leicester.ac.uk organization: School of Engineering, University of Leicester, Leicester LE1 7RH, UK – sequence: 3 givenname: S.G. orcidid: 0000-0003-4973-733X surname: Ponnambalam fullname: Ponnambalam, S.G. email: ponnambalam.g@vit.ac.in organization: School of Mechanical Engineering, Vellore Institute of Technology, Vellore 632014, India |
| BookMark | eNp9kMtqwzAQRUVJoUnaH-hKP2BXsmXLgW5KSB8Q6KZdi7E0DkptKUhqQ_6-Numqi6wGLnMu3LMgM-cdEnLPWc4Zrx_2OcYj5AUrypwXTJbyisx5I8uslqtyRuZsVclMcCluyCLGPWNcMibnJG0cht2JwhEC0oiDzeA7-QGS1RTiGLT9ifbWIW2hB6et29FD8G2PA9XeRWswTNnY4p0fRirY-EXBGfrtNIYE1k2Axhinv2QHvCXXHfQR7_7uknw-bz7Wr9n2_eVt_bTNdMlYytquMk23qrisO9GISpqu5oCaF4LXBRNCykpzMAy6pmoRBC-6VgjDEUtT10W5JMW5VwcfY8BOHYIdIJwUZ2rypvZq8qYmb-rsbYSaf5C2adThXQpg-8vo4xnFcdSPxaCitjhaMDagTsp4ewn_BU64jjI |
| CitedBy_id | crossref_primary_10_1016_j_cie_2023_109775 crossref_primary_10_1016_j_cie_2024_110795 crossref_primary_10_1016_j_ijpe_2024_109450 crossref_primary_10_1016_j_cie_2024_110254 crossref_primary_10_1016_j_simpat_2023_102839 crossref_primary_10_1016_j_jmsy_2025_02_009 crossref_primary_10_1111_itor_13610 crossref_primary_10_1016_j_cor_2024_106605 crossref_primary_10_1016_j_cie_2025_111154 crossref_primary_10_1016_j_cie_2025_111144 crossref_primary_10_1007_s10845_024_02443_x crossref_primary_10_1016_j_cie_2025_111113 crossref_primary_10_1016_j_jii_2024_100676 crossref_primary_10_46465_endustrimuhendisligi_1329111 crossref_primary_10_1016_j_eswa_2023_121221 crossref_primary_10_1016_j_swevo_2024_101762 |
| Cites_doi | 10.1109/TNSM.2022.3224158 10.1016/j.swevo.2021.101021 10.1016/j.cirp.2019.04.006 10.1016/S0007-8506(07)62494-9 10.1007/s00500-016-2240-9 10.1016/j.apm.2019.02.019 10.1115/1.4014559 10.1016/j.jmsy.2015.02.007 10.1080/00207543.2019.1629669 10.1007/s12541-018-0074-3 10.1016/j.cor.2007.09.005 10.1007/s001700050166 10.1108/AA-03-2019-0057 10.1016/j.jmsy.2021.10.006 10.1007/3-540-44719-9_20 10.1016/j.cie.2020.106768 10.1016/j.jclepro.2018.09.100 10.1016/j.cie.2008.09.027 10.1016/j.cor.2022.105775 10.1016/j.ijpe.2021.108188 10.1016/j.cie.2015.08.006 10.1016/j.cie.2012.03.017 10.1080/00207543.2021.2015081 10.1080/00207543.2021.1884767 10.1016/j.jclepro.2019.01.030 10.1007/s10845-020-01641-7 10.1023/A:1022602019183 10.1016/j.cor.2018.07.001 10.1007/s10845-014-0984-6 10.1080/00207543.2015.1074299 10.1007/s10845-020-01598-7 10.1007/s10479-008-0367-5 10.1016/j.ijpe.2021.108292 10.1016/j.eswa.2022.119359 10.1016/j.ejor.2006.10.010 10.1016/j.cie.2013.03.004 10.1016/j.ejor.2015.06.018 10.1007/s00521-014-1811-x 10.1109/TEVC.2013.2281535 10.1016/S1474-6670(17)37702-9 10.1109/4235.797969 10.1016/j.ijpe.2021.108151 10.1016/j.eswa.2021.116446 10.1016/j.cie.2020.106778 10.1016/j.cor.2021.105674 10.1109/SSCI.2016.7850225 10.1016/j.cie.2011.01.003 10.1016/j.jclepro.2014.11.041 10.1007/s12293-008-0004-5 10.1016/j.swevo.2021.100985 10.1016/j.asoc.2022.109764 10.1177/1063293X16666204 10.1016/j.omega.2004.12.006 10.1108/AA-07-2014-068 10.1016/j.cie.2021.107363 10.1109/CEC.2000.870296 10.1016/0003-6870(90)90202-9 10.1016/j.eswa.2019.112902 10.1016/j.jclepro.2016.06.131 10.1109/4235.996017 10.1016/j.ejor.2004.07.030 10.1016/j.ejor.2004.07.022 10.1016/j.ejor.2021.11.043 |
| ContentType | Journal Article |
| Copyright | 2023 Elsevier Ltd |
| Copyright_xml | – notice: 2023 Elsevier Ltd |
| DBID | AAYXX CITATION |
| DOI | 10.1016/j.eswa.2023.120737 |
| DatabaseName | CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1873-6793 |
| ExternalDocumentID | 10_1016_j_eswa_2023_120737 S0957417423012393 |
| GroupedDBID | --K --M .DC .~1 0R~ 13V 1B1 1RT 1~. 1~5 4.4 457 4G. 5GY 5VS 7-5 71M 8P~ 9JN 9JO AAAKF AABNK AACTN AAEDT AAEDW AAIAV AAIKJ AAKOC AALRI AAOAW AAQFI AARIN AAXUO AAYFN ABBOA ABFNM ABMAC ABMVD ABUCO ABYKQ ACDAQ ACGFS ACHRH ACNTT ACRLP ACZNC ADBBV ADEZE ADTZH AEBSH AECPX AEKER AENEX AFKWA AFTJW AGHFR AGJBL AGUBO AGUMN AGYEJ AHHHB AHJVU AHZHX AIALX AIEXJ AIKHN AITUG AJOXV ALEQD ALMA_UNASSIGNED_HOLDINGS AMFUW AMRAJ AOUOD APLSM AXJTR BJAXD BKOJK BLXMC BNSAS CS3 DU5 EBS EFJIC EFLBG EO8 EO9 EP2 EP3 F5P FDB FIRID FNPLU FYGXN G-Q GBLVA GBOLZ HAMUX IHE J1W JJJVA KOM LG9 LY1 LY7 M41 MO0 N9A O-L O9- OAUVE OZT P-8 P-9 P2P PC. PQQKQ Q38 ROL RPZ SDF SDG SDP SDS SES SEW SPC SPCBC SSB SSD SSL SST SSV SSZ T5K TN5 ~G- 29G 9DU AAAKG AAQXK AATTM AAXKI AAYWO AAYXX ABJNI ABKBG ABUFD ABWVN ABXDB ACLOT ACNNM ACRPL ACVFH ADCNI ADJOM ADMUD ADNMO AEIPS AEUPX AFJKZ AFPUW AGQPQ AIGII AIIUN AKBMS AKRWK AKYEP ANKPU APXCP ASPBG AVWKF AZFZN CITATION EFKBS EJD FEDTE FGOYB G-2 HLZ HVGLF HZ~ R2- SBC SET WUQ XPP ZMT ~HD |
| ID | FETCH-LOGICAL-c300t-bf5d8f95176f48457df61aec124162044775c1ad0af85bea412fb44d1ee3d6623 |
| ISICitedReferencesCount | 19 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001024851200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 0957-4174 |
| IngestDate | Sat Nov 29 07:06:41 EST 2025 Tue Nov 18 22:29:01 EST 2025 Fri Feb 23 02:37:33 EST 2024 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Ergonomic risks Chance constraint programming Memetic algorithm Energy Semi-automated assembly line Cycle time |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c300t-bf5d8f95176f48457df61aec124162044775c1ad0af85bea412fb44d1ee3d6623 |
| ORCID | 0000-0003-4973-733X |
| ParticipantIDs | crossref_primary_10_1016_j_eswa_2023_120737 crossref_citationtrail_10_1016_j_eswa_2023_120737 elsevier_sciencedirect_doi_10_1016_j_eswa_2023_120737 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-11-30 |
| PublicationDateYYYYMMDD | 2023-11-30 |
| PublicationDate_xml | – month: 11 year: 2023 text: 2023-11-30 day: 30 |
| PublicationDecade | 2020 |
| PublicationTitle | Expert systems with applications |
| PublicationYear | 2023 |
| Publisher | Elsevier Ltd |
| Publisher_xml | – name: Elsevier Ltd |
| References | Chutima, P., Khotsaenlee, A. J. C., & Research, O. (2022). Multi-objective parallel adjacent U-shaped assembly line balancing collaborated by robots and normal and disabled workers. GUROBI. (2023). Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Koltai, Dimény, Gallina, Gaal, Sepe (b0150) 2021; 242 Rahman, H. F., Sarker, R., & Essam, D. (2015a). A genetic algorithm for permutation flow shop scheduling under make to stock production system. Sun, Zheng, Song, Cheng, Lang, Yuan, Wang (b0305) 2023; 215 Ozdemir, Sarigol, AlMutairi, AlMeea, Murad, Naqi, AlNasser (b0210) 2021; 239 Westkämper, E., Spingler, J. C., & Beumelburg, K. J. I. P. V. (2003). Skill Oriented Planning of Semi Automated Assembly Systems. 105674. Scholl, A., & Becker, C. J. E. J. o. O. R. (2006). State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. Deb, Pratap, Agarwal, Meyarivan (b0085) 2002; 6 Salveson, M. E. J. T. J. o. I. E. (1955). The assembly line balancing problem. 18-25. Zhang, Z., Tang, Q., & Zhang, L. J. J. o. C. P. (2019). Mathematical model and grey wolf optimization for low-carbon and low-noise U-shaped robotic assembly line balancing problem. Tang, Q., Meng, K., Cheng, L., Zhang, Z. J. S., & Computation, E. (2022). An improved multi-objective multifactorial evolutionary algorithm for assembly line balancing problem considering regular production and preventive maintenance scenarios. Rahman, Chakrabortty, Elsawah, Ryan (b0235) 2022; 193 (4), 989–1007. 508–522. Qin, S., Pi, D., Shao, Z., Xu, Y. J. I. T. o. N., & Management, S. (2022). A Discrete Interval-based Multi-objective Memetic Algorithm for Scheduling Workflow with Uncertainty in Cloud Environment. Triki, H., Mellouli, A., & Masmoudi, F. J. J. o. I. M. (2017). A multi-objective genetic algorithm for assembly line resource assignment and balancing problem of type 2 (ALRABP-2). Chen, Yang, Li, Wang (b0050) 2020; 149 , . 311-325. Savino, M. M., & Mazza, A. J. A. A. (2015). Kanban-driven parts feeding within a semi-automated O-shaped assembly line: a case study in the automotive industry. Li, Z., Tang, Q., & Zhang, L. J. J. o. C. P. (2016). Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm. Rubinovitz, J., Bukchin, J., & Lenz, E. J. C. a. (1993). RALB–A heuristic algorithm for design and balancing of robotic assembly lines. Boysen, N., Schulze, P., & Scholl, A. J. E. J. o. O. R. (2021). Assembly line balancing: What happened in the last fifteen years?. Zhang, Xu, Zhang (b0335) 2020; 149 Manzini, Demeulemeester, Urgo, Manufacturing (b0180) 2022; 73 Zhong, Y.-g., & Ai, B. J. S. C. (2017). A modified ant colony optimization algorithm for multi-objective assembly line balancing. Mukund, Ponnambalam, Jawahar, Kanagaraj (b0185) 2015; 26 101021. (2), 503–512. 744-756. Gong, Chiong, Deng, Han, Zhang, Lin, Li (b0110) 2020; 141 (4), 613-624. Deb, K., Pratap, A., & Meyarivan, T. (2001). Constrained test problems for multi-objective evolutionary optimization. International conference on evolutionary multi-criterion optimization. Deb, Jain (b0080) 2013; 18 Xu, Lu, Vogel-Heuser, Wang (b0330) 2021; 61 Rahman, Sarker, Essam (b0260) 2015; 247 Abdous, M.-A., Delorme, X., Battini, D., Sgarbossa, F., & Berger-Douce, S. J. I. J. o. P. R. (2022). Assembly Line Balancing Problem with ergonomics: a new fatigue and recovery model. 1–14. Hasan, Sarker, Essam, Cornforth (b0120) 2009; 1 Li, Gong, Lu, Wang (b0160) 2022 (22), 111–116. Berti, Finco, Battaïa, Delorme (b0020) 2021; 237 Chen, Chen, Chen, Lin (b0045) 2022; 108240 Dalle Mura, Dini (b0075) 2019; 68 Ponnambalam, Aravindan, Mogileeswar Naidu (b0220) 2000; 16 Hazır, Dolgui (b0125) 2013; 65 Samouei, P., & Ashayeri, J. J. A. M. M. (2019). Developing optimization & robust models for a mixed-model assembly line balancing problem with semi-automated operations. (1), 30-40. 116446. (2), 674–693. 105775. Van Veldhuizen, D. A., & Lamont, G. B. (2000). On measuring multiobjective evolutionary algorithm performance. Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512). Gao, J., Sun, L., Wang, L., Gen, M. J. C., & Engineering, I. (2009). An efficient approach for type II robotic assembly line balancing problems. 866-883. Battini, D., Delorme, X., Dolgui, A., Persona, A., & Sgarbossa, F. J. I. J. o. P. R. (2016). Ergonomics in assembly line balancing based on energy expenditure: a multi-objective model. Rahman, Janardhanan, Nielsen (b0250) 2020 (Supplement C), 12-24. https://doi.org/https://doi.org/10.1016/j.cie.2015.08.006. (1), 497-500. Ruiz, Maroto, Alcaraz (b0275) 2006; 34 Cheshmehgaz, H. R., Haron, H., Kazemipour, F., Desa, M. I. J. C., & Engineering, I. (2012). Accumulated risk of body postures in assembly line balancing problem and modeling through a multi-criteria fuzzy-genetic algorithm. (4), 257-271. Campana, Iori, Moreira (b0040) 2022; 60 249-261. Rahman, H. F., Chakrabortty, R. K., Elsawah, S., & Ryan, M. J. J. E. S. w. A. (2022b). Energy-efficient project scheduling with supplier selection in manufacturing projects. He, Chaudhry, Lei, Baohua (b0130) 2009; 168 (22), 6881-6894. Jiang, S., Yang, S., & Li, M. (2016). On the use of hypervolume for diversity measurement of Pareto front approximations. 2016 IEEE symposium series on computational intelligence (SSCI). (3), 666-693. 46–54. Boysen, N., Fliedner, M., & Scholl, A. J. E. j. o. o. r. (2007). A classification of assembly line balancing problems. Akbar, M., & Irohara, T. J. J. o. c. p. (2018). Scheduling for sustainable manufacturing: A review. Mukund Nilakantan, J., Ponnambalam, S. G., Jawahar, N., Kanagaraj, G. J. N. C., & Applications. (2015). Bio-inspired search algorithms to solve robotic assembly line balancing problems. Techn. Hochsch., Inst. für Betriebswirtschaftslehre. Li, Z., Janardhanan, M. N., & Ponnambalam, S. J. J. o. I. M. (2021). Cost-oriented robotic assembly line balancing problem with setup times: multi-objective algorithms. Ogan, D., & Azizoglu, M. J. J. o. M. S. (2015). A branch and bound method for the line balancing problem in U-shaped assembly lines with equipment requirements. Nourmohammadi, A., Fathi, M., & Ng, A. H. (2022). Balancing and scheduling assembly lines with human-robot collaboration tasks. Rahman, Servranckx, Chakrabortty, Vanhoucke, El Sawah (b0265) 2022; 131 Chutima (b0065) 2022; 33 (2), 371–385. Rahman, Chakrabortty, Ryan (b0245) 2021; 157 259-275. Gao, Zhang, Zhang, Li (b0100) 2011; 60 (3), 824–845. Nilakantan, J. M., Huang, G. Q., & Ponnambalam, S. G. J. J. o. C. P. (2015). An investigation on minimizing cycle time and total energy consumption in robotic assembly line systems. (6), 1379–1393. Price (b0225) 1990; 21 Pereira, J., Ritt, M., Vásquez, Ó. C. J. C., & Research, O. (2018). A memetic algorithm for the cost-oriented robotic assembly line balancing problem. Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. Zhang, Tang, Chica, Li (b0345) 2023 Hu, Di Paolo (b0135) 2009; 36 Bryton (b0035) 1954 Scholl, A. (1995). Cheng, Tang, Zhang, Zhang (b0055) 2022; 69 Levitin, Rubinovitz, Shnits (b0155) 2006; 168 Zhang, L., Zhang, B., Bao, H., Huang, H. J. I. J. o. P. E., & Manufacturing. (2018). Optimization of cutting parameters for minimizing environmental impact: considering energy efficiency, noise emission and economic dimension. Jack. (2023). https://resources.sw.siemens.com/en-US/download-tecnomatix-jack-student-download. Zhong, Y.-g. J. C. E. (2017). Hull mixed-model assembly line balancing using a multi-objective genetic algorithm simulated annealing optimization approach. (3), 1065–1080. Liu, Liu, Deng (b0175) 2020; 58 Rahman (10.1016/j.eswa.2023.120737_b0260) 2015; 247 Bryton (10.1016/j.eswa.2023.120737_b0035) 1954 Koltai (10.1016/j.eswa.2023.120737_b0150) 2021; 242 Rahman (10.1016/j.eswa.2023.120737_b0245) 2021; 157 Hazır (10.1016/j.eswa.2023.120737_b0125) 2013; 65 10.1016/j.eswa.2023.120737_b0070 Deb (10.1016/j.eswa.2023.120737_b0080) 2013; 18 10.1016/j.eswa.2023.120737_b0190 Xu (10.1016/j.eswa.2023.120737_b0330) 2021; 61 Hasan (10.1016/j.eswa.2023.120737_b0120) 2009; 1 10.1016/j.eswa.2023.120737_b0270 10.1016/j.eswa.2023.120737_b0030 10.1016/j.eswa.2023.120737_b0195 Sun (10.1016/j.eswa.2023.120737_b0305) 2023; 215 10.1016/j.eswa.2023.120737_b0230 10.1016/j.eswa.2023.120737_b0350 Dalle Mura (10.1016/j.eswa.2023.120737_b0075) 2019; 68 Deb (10.1016/j.eswa.2023.120737_b0085) 2002; 6 10.1016/j.eswa.2023.120737_b0355 10.1016/j.eswa.2023.120737_b0310 10.1016/j.eswa.2023.120737_b0115 Ruiz (10.1016/j.eswa.2023.120737_b0275) 2006; 34 10.1016/j.eswa.2023.120737_b0315 10.1016/j.eswa.2023.120737_b0090 10.1016/j.eswa.2023.120737_b0280 10.1016/j.eswa.2023.120737_b0360 10.1016/j.eswa.2023.120737_b0285 10.1016/j.eswa.2023.120737_b0240 10.1016/j.eswa.2023.120737_b0320 10.1016/j.eswa.2023.120737_b0165 10.1016/j.eswa.2023.120737_b0200 10.1016/j.eswa.2023.120737_b0365 10.1016/j.eswa.2023.120737_b0005 10.1016/j.eswa.2023.120737_b0205 10.1016/j.eswa.2023.120737_b0325 Chutima (10.1016/j.eswa.2023.120737_b0065) 2022; 33 Chen (10.1016/j.eswa.2023.120737_b0050) 2020; 149 Zhang (10.1016/j.eswa.2023.120737_b0345) 2023 Gao (10.1016/j.eswa.2023.120737_b0100) 2011; 60 Zhang (10.1016/j.eswa.2023.120737_b0335) 2020; 149 Rahman (10.1016/j.eswa.2023.120737_b0250) 2020 10.1016/j.eswa.2023.120737_b0290 10.1016/j.eswa.2023.120737_b0170 Cheng (10.1016/j.eswa.2023.120737_b0055) 2022; 69 10.1016/j.eswa.2023.120737_b0095 10.1016/j.eswa.2023.120737_b0010 Hu (10.1016/j.eswa.2023.120737_b0135) 2009; 36 10.1016/j.eswa.2023.120737_b0295 Chen (10.1016/j.eswa.2023.120737_b0045) 2022; 108240 10.1016/j.eswa.2023.120737_b0255 Li (10.1016/j.eswa.2023.120737_b0160) 2022 10.1016/j.eswa.2023.120737_b0015 Rahman (10.1016/j.eswa.2023.120737_b0235) 2022; 193 Mukund (10.1016/j.eswa.2023.120737_b0185) 2015; 26 10.1016/j.eswa.2023.120737_b0215 Price (10.1016/j.eswa.2023.120737_b0225) 1990; 21 Berti (10.1016/j.eswa.2023.120737_b0020) 2021; 237 Campana (10.1016/j.eswa.2023.120737_b0040) 2022; 60 Rahman (10.1016/j.eswa.2023.120737_b0265) 2022; 131 Liu (10.1016/j.eswa.2023.120737_b0175) 2020; 58 Ponnambalam (10.1016/j.eswa.2023.120737_b0220) 2000; 16 Gong (10.1016/j.eswa.2023.120737_b0110) 2020; 141 He (10.1016/j.eswa.2023.120737_b0130) 2009; 168 10.1016/j.eswa.2023.120737_b0060 10.1016/j.eswa.2023.120737_b0140 Levitin (10.1016/j.eswa.2023.120737_b0155) 2006; 168 Ozdemir (10.1016/j.eswa.2023.120737_b0210) 2021; 239 10.1016/j.eswa.2023.120737_b0340 10.1016/j.eswa.2023.120737_b0025 10.1016/j.eswa.2023.120737_b0300 10.1016/j.eswa.2023.120737_b0145 Manzini (10.1016/j.eswa.2023.120737_b0180) 2022; 73 10.1016/j.eswa.2023.120737_b0105 |
| References_xml | – volume: 247 start-page: 488 year: 2015 end-page: 503 ident: b0260 article-title: A real-time order acceptance and scheduling approach for permutation flow shop problems publication-title: European Journal of Operational Research – volume: 65 start-page: 261 year: 2013 end-page: 267 ident: b0125 article-title: Assembly line balancing under uncertainty: Robust optimization models and exact solution method publication-title: Computers & Industrial Engineering – reference: , 46–54. – reference: Salveson, M. E. J. T. J. o. I. E. (1955). The assembly line balancing problem. 18-25. – reference: , 259-275. – reference: , 105775. – reference: Li, Z., Janardhanan, M. N., & Ponnambalam, S. J. J. o. I. M. (2021). Cost-oriented robotic assembly line balancing problem with setup times: multi-objective algorithms. – volume: 36 start-page: 245 year: 2009 end-page: 259 ident: b0135 article-title: An efficient genetic algorithm with uniform crossover for air traffic control publication-title: Computers & Operations Research – reference: Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. – reference: Qin, S., Pi, D., Shao, Z., Xu, Y. J. I. T. o. N., & Management, S. (2022). A Discrete Interval-based Multi-objective Memetic Algorithm for Scheduling Workflow with Uncertainty in Cloud Environment. – volume: 131 year: 2022 ident: b0265 article-title: Manufacturing project scheduling considering human factors to minimize total cost and carbon footprints publication-title: Applied Soft Computing – volume: 21 start-page: 311 year: 1990 end-page: 317 ident: b0225 article-title: Calculating relaxation allowances for construction operatives—Part 1: Metabolic cost publication-title: Applied ergonomics – reference: Pereira, J., Ritt, M., Vásquez, Ó. C. J. C., & Research, O. (2018). A memetic algorithm for the cost-oriented robotic assembly line balancing problem. – volume: 16 start-page: 341 year: 2000 end-page: 352 ident: b0220 article-title: A multi-objective genetic algorithm for solving assembly line balancing problem publication-title: The International Journal of Advanced Manufacturing Technology – reference: Rahman, H. F., Sarker, R., & Essam, D. (2015a). A genetic algorithm for permutation flow shop scheduling under make to stock production system. – reference: Abdous, M.-A., Delorme, X., Battini, D., Sgarbossa, F., & Berger-Douce, S. J. I. J. o. P. R. (2022). Assembly Line Balancing Problem with ergonomics: a new fatigue and recovery model. 1–14. – reference: Mukund Nilakantan, J., Ponnambalam, S. G., Jawahar, N., Kanagaraj, G. J. N. C., & Applications. (2015). Bio-inspired search algorithms to solve robotic assembly line balancing problems. – volume: 34 start-page: 461 year: 2006 end-page: 476 ident: b0275 article-title: Two new robust genetic algorithms for the flowshop scheduling problem publication-title: Omega – volume: 149 year: 2020 ident: b0335 article-title: Developing mathematical model and optimization algorithm for designing energy efficient semi-automated assembly line publication-title: Computers & Industrial Engineering – year: 2022 ident: b0160 article-title: A learning-based memetic algorithm for energy-efficient flexible job shop scheduling with type-2 fuzzy processing time publication-title: IEEE transactions on evolutionary computation – reference: Zhong, Y.-g., & Ai, B. J. S. C. (2017). A modified ant colony optimization algorithm for multi-objective assembly line balancing. – reference: Savino, M. M., & Mazza, A. J. A. A. (2015). Kanban-driven parts feeding within a semi-automated O-shaped assembly line: a case study in the automotive industry. – volume: 157 year: 2021 ident: b0245 article-title: Scheduling project with stochastic durations and time-varying resource requests: A metaheuristic approach publication-title: Computers & Industrial Engineering – volume: 149 year: 2020 ident: b0050 article-title: A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem publication-title: Computers & Industrial Engineering – reference: Zhong, Y.-g. J. C. E. (2017). Hull mixed-model assembly line balancing using a multi-objective genetic algorithm simulated annealing optimization approach. – reference: Boysen, N., Fliedner, M., & Scholl, A. J. E. j. o. o. r. (2007). A classification of assembly line balancing problems. – reference: , 101021. – reference: Zhang, Z., Tang, Q., & Zhang, L. J. J. o. C. P. (2019). Mathematical model and grey wolf optimization for low-carbon and low-noise U-shaped robotic assembly line balancing problem. – reference: , 311-325. – reference: Samouei, P., & Ashayeri, J. J. A. M. M. (2019). Developing optimization & robust models for a mixed-model assembly line balancing problem with semi-automated operations. – reference: Tang, Q., Meng, K., Cheng, L., Zhang, Z. J. S., & Computation, E. (2022). An improved multi-objective multifactorial evolutionary algorithm for assembly line balancing problem considering regular production and preventive maintenance scenarios. – reference: , – reference: , 744-756. – reference: (4), 257-271. – reference: Deb, K., Pratap, A., & Meyarivan, T. (2001). Constrained test problems for multi-objective evolutionary optimization. International conference on evolutionary multi-criterion optimization. – reference: Rubinovitz, J., Bukchin, J., & Lenz, E. J. C. a. (1993). RALB–A heuristic algorithm for design and balancing of robotic assembly lines. – reference: Zhang, L., Zhang, B., Bao, H., Huang, H. J. I. J. o. P. E., & Manufacturing. (2018). Optimization of cutting parameters for minimizing environmental impact: considering energy efficiency, noise emission and economic dimension. – reference: Chutima, P., Khotsaenlee, A. J. C., & Research, O. (2022). Multi-objective parallel adjacent U-shaped assembly line balancing collaborated by robots and normal and disabled workers. – reference: (Supplement C), 12-24. https://doi.org/https://doi.org/10.1016/j.cie.2015.08.006. – reference: (3), 666-693. – reference: (2), 674–693. – reference: Nourmohammadi, A., Fathi, M., & Ng, A. H. (2022). Balancing and scheduling assembly lines with human-robot collaboration tasks. – reference: Cheshmehgaz, H. R., Haron, H., Kazemipour, F., Desa, M. I. J. C., & Engineering, I. (2012). Accumulated risk of body postures in assembly line balancing problem and modeling through a multi-criteria fuzzy-genetic algorithm. – reference: (4), 989–1007. – volume: 69 year: 2022 ident: b0055 article-title: Multi-objective Q-learning-based hyper-heuristic with Bi-criteria selection for energy-aware mixed shop scheduling publication-title: Swarm and Evolutionary Computation – volume: 242 year: 2021 ident: b0150 article-title: An analysis of task assignment and cycle times when robots are added to human-operated assembly lines, using mathematical programming models publication-title: International Journal of Production Economics – reference: GUROBI. (2023). – volume: 168 start-page: 169 year: 2009 end-page: 179 ident: b0130 article-title: Stochastic vendor selection problem: Chance-constrained model and genetic algorithms publication-title: Annals of Operations Research – reference: Scholl, A., & Becker, C. J. E. J. o. O. R. (2006). State-of-the-art exact and heuristic solution procedures for simple assembly line balancing. – reference: Van Veldhuizen, D. A., & Lamont, G. B. (2000). On measuring multiobjective evolutionary algorithm performance. Proceedings of the 2000 Congress on Evolutionary Computation. CEC00 (Cat. No. 00TH8512). – reference: Boysen, N., Schulze, P., & Scholl, A. J. E. J. o. O. R. (2021). Assembly line balancing: What happened in the last fifteen years?. – reference: Westkämper, E., Spingler, J. C., & Beumelburg, K. J. I. P. V. (2003). Skill Oriented Planning of Semi Automated Assembly Systems. – reference: (22), 111–116. – reference: Nilakantan, J. M., Huang, G. Q., & Ponnambalam, S. G. J. J. o. C. P. (2015). An investigation on minimizing cycle time and total energy consumption in robotic assembly line systems. – reference: (1), 497-500. – volume: 239 year: 2021 ident: b0210 article-title: Fuzzy multi-objective model for assembly line balancing with ergonomic risks consideration publication-title: International Journal of Production Economics – reference: Jack. (2023). https://resources.sw.siemens.com/en-US/download-tecnomatix-jack-student-download. – reference: , 508–522. – reference: (22), 6881-6894. – reference: (2), 503–512. – reference: Scholl, A. (1995). – volume: 1 start-page: 69 year: 2009 end-page: 83 ident: b0120 article-title: Memetic algorithms for solving job-shop scheduling problems publication-title: Memetic Computing – volume: 141 year: 2020 ident: b0110 article-title: Energy-efficient flexible flow shop scheduling with worker flexibility publication-title: Expert Systems with Applications – reference: , 105674. – volume: 26 start-page: 1379 year: 2015 end-page: 1393 ident: b0185 article-title: Bio-inspired search algorithms to solve robotic assembly line balancing problems publication-title: Neural Computing and Applications – year: 2020 ident: b0250 article-title: An integrated approach for line balancing and AGV scheduling towards smart assembly systems publication-title: Assembly Automation – reference: Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. – volume: 73 year: 2022 ident: b0180 publication-title: A predictive–reactive approach for the sequencing of assembly operations in an automated assembly line. – reference: (2), 371–385. – reference: Akbar, M., & Irohara, T. J. J. o. c. p. (2018). Scheduling for sustainable manufacturing: A review. – reference: (3), 1065–1080. – reference: Battini, D., Delorme, X., Dolgui, A., Persona, A., & Sgarbossa, F. J. I. J. o. P. R. (2016). Ergonomics in assembly line balancing based on energy expenditure: a multi-objective model. – reference: , 866-883. – year: 2023 ident: b0345 article-title: Reinforcement Learning-Based Multiobjective Evolutionary Algorithm for Mixed-Model Multimanned Assembly Line Balancing Under Uncertain Demand publication-title: IEEE Transactions on Cybernetics. – reference: (6), 1379–1393. – reference: , 249-261. – volume: 33 start-page: 1 year: 2022 end-page: 34 ident: b0065 article-title: A comprehensive review of robotic assembly line balancing problem publication-title: Journal of Intelligent Manufacturing – reference: . – reference: . Techn. Hochsch., Inst. für Betriebswirtschaftslehre. – volume: 61 start-page: 530 year: 2021 end-page: 535 ident: b0330 article-title: Industry 4.0 and Industry 5.0—Inception, conception and perception publication-title: Journal of Manufacturing Systems – volume: 215 year: 2023 ident: b0305 article-title: Hybrid genetic algorithm with variable neighborhood search for flexible job shop scheduling problem in a machining system publication-title: Expert Systems with Applications – volume: 60 start-page: 2193 year: 2022 end-page: 2211 ident: b0040 article-title: Mathematical models and heuristic methods for the assembly line balancing problem with hierarchical worker assignment publication-title: International Journal of Production Research – reference: Ogan, D., & Azizoglu, M. J. J. o. M. S. (2015). A branch and bound method for the line balancing problem in U-shaped assembly lines with equipment requirements. – volume: 193 year: 2022 ident: b0235 article-title: Energy-efficient project scheduling with supplier selection in manufacturing projects publication-title: Expert Systems with Applications – volume: 60 start-page: 699 year: 2011 end-page: 705 ident: b0100 article-title: An efficient memetic algorithm for solving the job shop scheduling problem publication-title: Computers & Industrial Engineering – reference: , 116446. – volume: 6 start-page: 182 year: 2002 end-page: 197 ident: b0085 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Transactions on Evolutionary Computation – reference: Rahman, H. F., Chakrabortty, R. K., Elsawah, S., & Ryan, M. J. J. E. S. w. A. (2022b). Energy-efficient project scheduling with supplier selection in manufacturing projects. – reference: Triki, H., Mellouli, A., & Masmoudi, F. J. J. o. I. M. (2017). A multi-objective genetic algorithm for assembly line resource assignment and balancing problem of type 2 (ALRABP-2). – volume: 108240 year: 2022 ident: b0045 article-title: Multi-Project Scheduling with Multi-Skilled Workforce Assignment Considering Uncertainty and Learning Effect for Large-Scale Equipment Manufacturer publication-title: Computers & Industrial Engineering – volume: 168 start-page: 811 year: 2006 end-page: 825 ident: b0155 article-title: A genetic algorithm for robotic assembly line balancing publication-title: European Journal of Operational Research – reference: (1), 30-40. – reference: Gao, J., Sun, L., Wang, L., Gen, M. J. C., & Engineering, I. (2009). An efficient approach for type II robotic assembly line balancing problems. – reference: . – volume: 18 start-page: 577 year: 2013 end-page: 601 ident: b0080 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 Transactions on Evolutionary Computation – volume: 237 year: 2021 ident: b0020 article-title: Ageing workforce effects in Dual-Resource Constrained job-shop scheduling publication-title: International Journal of Production Economics – year: 1954 ident: b0035 article-title: Balancing of a continuous production line – volume: 68 start-page: 1 year: 2019 end-page: 4 ident: b0075 article-title: Designing assembly lines with humans and collaborative robots: A genetic approach publication-title: CIRP Annals – reference: (4), 613-624. – reference: Jiang, S., Yang, S., & Li, M. (2016). On the use of hypervolume for diversity measurement of Pareto front approximations. 2016 IEEE symposium series on computational intelligence (SSCI). – reference: Li, Z., Tang, Q., & Zhang, L. J. J. o. C. P. (2016). Minimizing energy consumption and cycle time in two-sided robotic assembly line systems using restarted simulated annealing algorithm. – volume: 58 start-page: 3090 year: 2020 end-page: 3109 ident: b0175 article-title: Modelling, analysis and improvement of an integrated chance-constrained model for level of repair analysis and spare parts supply control publication-title: International Journal of Production Research – reference: (3), 824–845. – ident: 10.1016/j.eswa.2023.120737_b0230 doi: 10.1109/TNSM.2022.3224158 – ident: 10.1016/j.eswa.2023.120737_b0310 doi: 10.1016/j.swevo.2021.101021 – year: 2022 ident: 10.1016/j.eswa.2023.120737_b0160 article-title: A learning-based memetic algorithm for energy-efficient flexible job shop scheduling with type-2 fuzzy processing time – volume: 68 start-page: 1 issue: 1 year: 2019 ident: 10.1016/j.eswa.2023.120737_b0075 article-title: Designing assembly lines with humans and collaborative robots: A genetic approach publication-title: CIRP Annals doi: 10.1016/j.cirp.2019.04.006 – ident: 10.1016/j.eswa.2023.120737_b0270 doi: 10.1016/S0007-8506(07)62494-9 – ident: 10.1016/j.eswa.2023.120737_b0355 doi: 10.1007/s00500-016-2240-9 – ident: 10.1016/j.eswa.2023.120737_b0285 doi: 10.1016/j.apm.2019.02.019 – ident: 10.1016/j.eswa.2023.120737_b0280 doi: 10.1115/1.4014559 – ident: 10.1016/j.eswa.2023.120737_b0205 doi: 10.1016/j.jmsy.2015.02.007 – volume: 58 start-page: 3090 issue: 10 year: 2020 ident: 10.1016/j.eswa.2023.120737_b0175 article-title: Modelling, analysis and improvement of an integrated chance-constrained model for level of repair analysis and spare parts supply control publication-title: International Journal of Production Research doi: 10.1080/00207543.2019.1629669 – ident: 10.1016/j.eswa.2023.120737_b0340 doi: 10.1007/s12541-018-0074-3 – volume: 36 start-page: 245 issue: 1 year: 2009 ident: 10.1016/j.eswa.2023.120737_b0135 article-title: An efficient genetic algorithm with uniform crossover for air traffic control publication-title: Computers & Operations Research doi: 10.1016/j.cor.2007.09.005 – volume: 16 start-page: 341 issue: 5 year: 2000 ident: 10.1016/j.eswa.2023.120737_b0220 article-title: A multi-objective genetic algorithm for solving assembly line balancing problem publication-title: The International Journal of Advanced Manufacturing Technology doi: 10.1007/s001700050166 – ident: 10.1016/j.eswa.2023.120737_b0295 – year: 2020 ident: 10.1016/j.eswa.2023.120737_b0250 article-title: An integrated approach for line balancing and AGV scheduling towards smart assembly systems publication-title: Assembly Automation doi: 10.1108/AA-03-2019-0057 – volume: 61 start-page: 530 year: 2021 ident: 10.1016/j.eswa.2023.120737_b0330 article-title: Industry 4.0 and Industry 5.0—Inception, conception and perception publication-title: Journal of Manufacturing Systems doi: 10.1016/j.jmsy.2021.10.006 – ident: 10.1016/j.eswa.2023.120737_b0090 doi: 10.1007/3-540-44719-9_20 – ident: 10.1016/j.eswa.2023.120737_b0140 – volume: 149 year: 2020 ident: 10.1016/j.eswa.2023.120737_b0335 article-title: Developing mathematical model and optimization algorithm for designing energy efficient semi-automated assembly line publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2020.106768 – volume: 73 year: 2022 ident: 10.1016/j.eswa.2023.120737_b0180 publication-title: A predictive–reactive approach for the sequencing of assembly operations in an automated assembly line. – ident: 10.1016/j.eswa.2023.120737_b0010 doi: 10.1016/j.jclepro.2018.09.100 – ident: 10.1016/j.eswa.2023.120737_b0095 doi: 10.1016/j.cie.2008.09.027 – ident: 10.1016/j.eswa.2023.120737_b0070 doi: 10.1016/j.cor.2022.105775 – volume: 239 year: 2021 ident: 10.1016/j.eswa.2023.120737_b0210 article-title: Fuzzy multi-objective model for assembly line balancing with ergonomic risks consideration publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2021.108188 – ident: 10.1016/j.eswa.2023.120737_b0255 doi: 10.1016/j.cie.2015.08.006 – ident: 10.1016/j.eswa.2023.120737_b0060 doi: 10.1016/j.cie.2012.03.017 – ident: 10.1016/j.eswa.2023.120737_b0005 doi: 10.1080/00207543.2021.2015081 – volume: 60 start-page: 2193 issue: 7 year: 2022 ident: 10.1016/j.eswa.2023.120737_b0040 article-title: Mathematical models and heuristic methods for the assembly line balancing problem with hierarchical worker assignment publication-title: International Journal of Production Research doi: 10.1080/00207543.2021.1884767 – ident: 10.1016/j.eswa.2023.120737_b0350 doi: 10.1016/j.jclepro.2019.01.030 – volume: 33 start-page: 1 issue: 1 year: 2022 ident: 10.1016/j.eswa.2023.120737_b0065 article-title: A comprehensive review of robotic assembly line balancing problem publication-title: Journal of Intelligent Manufacturing doi: 10.1007/s10845-020-01641-7 – ident: 10.1016/j.eswa.2023.120737_b0105 doi: 10.1023/A:1022602019183 – ident: 10.1016/j.eswa.2023.120737_b0215 doi: 10.1016/j.cor.2018.07.001 – ident: 10.1016/j.eswa.2023.120737_b0315 doi: 10.1007/s10845-014-0984-6 – ident: 10.1016/j.eswa.2023.120737_b0015 doi: 10.1080/00207543.2015.1074299 – ident: 10.1016/j.eswa.2023.120737_b0165 doi: 10.1007/s10845-020-01598-7 – volume: 168 start-page: 169 issue: 1 year: 2009 ident: 10.1016/j.eswa.2023.120737_b0130 article-title: Stochastic vendor selection problem: Chance-constrained model and genetic algorithms publication-title: Annals of Operations Research doi: 10.1007/s10479-008-0367-5 – volume: 242 year: 2021 ident: 10.1016/j.eswa.2023.120737_b0150 article-title: An analysis of task assignment and cycle times when robots are added to human-operated assembly lines, using mathematical programming models publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2021.108292 – volume: 215 year: 2023 ident: 10.1016/j.eswa.2023.120737_b0305 article-title: Hybrid genetic algorithm with variable neighborhood search for flexible job shop scheduling problem in a machining system publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2022.119359 – ident: 10.1016/j.eswa.2023.120737_b0025 doi: 10.1016/j.ejor.2006.10.010 – volume: 65 start-page: 261 issue: 2 year: 2013 ident: 10.1016/j.eswa.2023.120737_b0125 article-title: Assembly line balancing under uncertainty: Robust optimization models and exact solution method publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2013.03.004 – volume: 247 start-page: 488 issue: 2 year: 2015 ident: 10.1016/j.eswa.2023.120737_b0260 article-title: A real-time order acceptance and scheduling approach for permutation flow shop problems publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2015.06.018 – year: 2023 ident: 10.1016/j.eswa.2023.120737_b0345 article-title: Reinforcement Learning-Based Multiobjective Evolutionary Algorithm for Mixed-Model Multimanned Assembly Line Balancing Under Uncertain Demand publication-title: IEEE Transactions on Cybernetics. – volume: 26 start-page: 1379 issue: 6 year: 2015 ident: 10.1016/j.eswa.2023.120737_b0185 article-title: Bio-inspired search algorithms to solve robotic assembly line balancing problems publication-title: Neural Computing and Applications doi: 10.1007/s00521-014-1811-x – volume: 18 start-page: 577 issue: 4 year: 2013 ident: 10.1016/j.eswa.2023.120737_b0080 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 Transactions on Evolutionary Computation doi: 10.1109/TEVC.2013.2281535 – ident: 10.1016/j.eswa.2023.120737_b0325 doi: 10.1016/S1474-6670(17)37702-9 – ident: 10.1016/j.eswa.2023.120737_b0365 doi: 10.1109/4235.797969 – volume: 237 year: 2021 ident: 10.1016/j.eswa.2023.120737_b0020 article-title: Ageing workforce effects in Dual-Resource Constrained job-shop scheduling publication-title: International Journal of Production Economics doi: 10.1016/j.ijpe.2021.108151 – ident: 10.1016/j.eswa.2023.120737_b0240 doi: 10.1016/j.eswa.2021.116446 – volume: 149 year: 2020 ident: 10.1016/j.eswa.2023.120737_b0050 article-title: A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2020.106778 – ident: 10.1016/j.eswa.2023.120737_b0190 doi: 10.1007/s00521-014-1811-x – ident: 10.1016/j.eswa.2023.120737_b0200 doi: 10.1016/j.cor.2021.105674 – ident: 10.1016/j.eswa.2023.120737_b0145 doi: 10.1109/SSCI.2016.7850225 – volume: 60 start-page: 699 issue: 4 year: 2011 ident: 10.1016/j.eswa.2023.120737_b0100 article-title: An efficient memetic algorithm for solving the job shop scheduling problem publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2011.01.003 – ident: 10.1016/j.eswa.2023.120737_b0195 doi: 10.1016/j.jclepro.2014.11.041 – volume: 1 start-page: 69 issue: 1 year: 2009 ident: 10.1016/j.eswa.2023.120737_b0120 article-title: Memetic algorithms for solving job-shop scheduling problems publication-title: Memetic Computing doi: 10.1007/s12293-008-0004-5 – volume: 69 year: 2022 ident: 10.1016/j.eswa.2023.120737_b0055 article-title: Multi-objective Q-learning-based hyper-heuristic with Bi-criteria selection for energy-aware mixed shop scheduling publication-title: Swarm and Evolutionary Computation doi: 10.1016/j.swevo.2021.100985 – volume: 131 year: 2022 ident: 10.1016/j.eswa.2023.120737_b0265 article-title: Manufacturing project scheduling considering human factors to minimize total cost and carbon footprints publication-title: Applied Soft Computing doi: 10.1016/j.asoc.2022.109764 – ident: 10.1016/j.eswa.2023.120737_b0360 doi: 10.1177/1063293X16666204 – ident: 10.1016/j.eswa.2023.120737_b0115 – volume: 34 start-page: 461 issue: 5 year: 2006 ident: 10.1016/j.eswa.2023.120737_b0275 article-title: Two new robust genetic algorithms for the flowshop scheduling problem publication-title: Omega doi: 10.1016/j.omega.2004.12.006 – ident: 10.1016/j.eswa.2023.120737_b0290 doi: 10.1108/AA-07-2014-068 – volume: 157 year: 2021 ident: 10.1016/j.eswa.2023.120737_b0245 article-title: Scheduling project with stochastic durations and time-varying resource requests: A metaheuristic approach publication-title: Computers & Industrial Engineering doi: 10.1016/j.cie.2021.107363 – volume: 108240 year: 2022 ident: 10.1016/j.eswa.2023.120737_b0045 article-title: Multi-Project Scheduling with Multi-Skilled Workforce Assignment Considering Uncertainty and Learning Effect for Large-Scale Equipment Manufacturer publication-title: Computers & Industrial Engineering – ident: 10.1016/j.eswa.2023.120737_b0320 doi: 10.1109/CEC.2000.870296 – volume: 21 start-page: 311 issue: 4 year: 1990 ident: 10.1016/j.eswa.2023.120737_b0225 article-title: Calculating relaxation allowances for construction operatives—Part 1: Metabolic cost publication-title: Applied ergonomics doi: 10.1016/0003-6870(90)90202-9 – volume: 141 year: 2020 ident: 10.1016/j.eswa.2023.120737_b0110 article-title: Energy-efficient flexible flow shop scheduling with worker flexibility publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2019.112902 – ident: 10.1016/j.eswa.2023.120737_b0170 doi: 10.1016/j.jclepro.2016.06.131 – volume: 193 year: 2022 ident: 10.1016/j.eswa.2023.120737_b0235 article-title: Energy-efficient project scheduling with supplier selection in manufacturing projects publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2021.116446 – year: 1954 ident: 10.1016/j.eswa.2023.120737_b0035 – volume: 6 start-page: 182 issue: 2 year: 2002 ident: 10.1016/j.eswa.2023.120737_b0085 article-title: A fast and elitist multiobjective genetic algorithm: NSGA-II publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/4235.996017 – volume: 168 start-page: 811 issue: 3 year: 2006 ident: 10.1016/j.eswa.2023.120737_b0155 article-title: A genetic algorithm for robotic assembly line balancing publication-title: European Journal of Operational Research doi: 10.1016/j.ejor.2004.07.030 – ident: 10.1016/j.eswa.2023.120737_b0300 doi: 10.1016/j.ejor.2004.07.022 – ident: 10.1016/j.eswa.2023.120737_b0030 doi: 10.1016/j.ejor.2021.11.043 |
| SSID | ssj0017007 |
| Score | 2.510528 |
| Snippet | The semiautomated assembly line balancing problem (SAALBP) is a complex planning problem in which the production process comprises human workers, robots, and... |
| SourceID | crossref elsevier |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 120737 |
| SubjectTerms | Chance constraint programming Cycle time Energy Ergonomic risks Memetic algorithm Semi-automated assembly line |
| Title | Energy aware semi-automatic assembly line balancing problem considering ergonomic risk and uncertain processing time |
| URI | https://dx.doi.org/10.1016/j.eswa.2023.120737 |
| Volume | 231 |
| WOSCitedRecordID | wos001024851200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVESC databaseName: Elsevier SD Freedom Collection Journals 2021 customDbUrl: eissn: 1873-6793 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0017007 issn: 0957-4174 databaseCode: AIEXJ dateStart: 19950101 isFulltext: true titleUrlDefault: https://www.sciencedirect.com providerName: Elsevier |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtZ3JbtswEEAJN-mhl-5F0g089GbI0EKZ1DEInC5ojQBNC98EbkKcOLIRS1n-JR_boUhKcpAGzaEXwSAoStY8DUfkLAh9kqEihWbwfqtkHBARZwFnQgVjCeYG4SwUTSLt39_pdMpms-xwMLjxsTAXC1qW7OoqW_1XUUMbCNuEzj5A3O2g0AC_QehwBLHD8Z8EP7HRfPzS-HSt9dk84HW1dJlZ19AgFtfDxrgUxq1R2nD0pqyM8UFvyneaNhjFxixb93OzwA5zoPUgGK5sfEETazXfzHfQJE-uXIpoHzzX2ybvdpaO3eorIHVdl8ODmqvWoYeXgO4xd9WTf9SnNVx_Ol_wU0ChI_pwWZb8zPyRhuufo8-j_jpGnPj8ib0FSRqQyNbs8bo5dlOE1a5RDAqJ3qn47RrEyUivL002qTgZdZ03s2zfmv1an0Tv7naSmzFyM0Zux3iEtmOaZqD2t_e-Tmbf2l0qGtpwfH_nLijL-g_evpO7DZ-eMXP0HD11XyF4z9LzAg10-RI98xU-sFP4r1BlYcINTHgTJuxhwgYm3MKEHUy4BxNuYcIGJgww4RYm3MGEDUyv0a-DydH-l8DV6QhkEoZVIIpUsQJMdTouCCMpVcU44lqC6RiZcgeE0lRGXIW8YKnQnERxIQhRkdaJGoP9_QZtlctS7yCcCQFPT-hEZNBDxiylIsqYEprIJCvELor8Q8ylS2Jvaqks8r-LbxcN23NWNoXLvb1TL5vcGaHWuMwBtXvOe_ugq7xDT7p34D3aqs5r_QE9lhfVfH3-0XH2B86Nr5w |
| linkProvider | Elsevier |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Energy+aware+semi-automatic+assembly+line+balancing+problem+considering+ergonomic+risk+and+uncertain+processing+time&rft.jtitle=Expert+systems+with+applications&rft.au=Rahman%2C+Humyun+Fuad&rft.au=Janardhanan%2C+Mukund+Nilakantan&rft.au=Ponnambalam%2C+S.G.&rft.date=2023-11-30&rft.issn=0957-4174&rft.volume=231&rft.spage=120737&rft_id=info:doi/10.1016%2Fj.eswa.2023.120737&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_eswa_2023_120737 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0957-4174&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0957-4174&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0957-4174&client=summon |