{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T04:38:13Z","timestamp":1743050293012,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031568251"},{"type":"electronic","value":"9783031568268"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-3-031-56826-8_17","type":"book-chapter","created":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T05:01:44Z","timestamp":1712034104000},"page":"223-234","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Robust Human-Centered Assembly Line Scheduling with\u00a0Reinforcement Learning"],"prefix":"10.1007","author":[{"given":"Felix","family":"Grumbach","sequence":"first","affiliation":[]},{"given":"Arthur","family":"M\u00fcller","sequence":"additional","affiliation":[]},{"given":"Lukas","family":"Vollenkemper","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,3]]},"reference":[{"key":"17_CR1","doi-asserted-by":"publisher","unstructured":"Brammer, J., Lutz, B., Neumann, D.: Permutation flow shop scheduling with multiple lines and demand plans using reinforcement learning. Eur. J. Oper. Res. 299(1), 75\u201386 (2022). https:\/\/doi.org\/10.1016\/j.ejor.2021.08.007. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0377221721006743","DOI":"10.1016\/j.ejor.2021.08.007"},{"key":"17_CR2","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1016\/j.jmsy.2023.01.004","volume":"67","author":"C Destouet","year":"2023","unstructured":"Destouet, C., Tlahig, H., Bettayeb, B., Mazari, B.: Flexible job shop scheduling problem under industry 5.0: a survey on human reintegration, environmental consideration and resilience improvement. J. Manuf. Syst. 67, 155\u2013173 (2023). https:\/\/doi.org\/10.1016\/j.jmsy.2023.01.004","journal-title":"J. Manuf. Syst."},{"key":"17_CR3","doi-asserted-by":"publisher","first-page":"667","DOI":"10.1007\/s10845-022-02069-x","volume":"35","author":"F Grumbach","year":"2022","unstructured":"Grumbach, F., M\u00fcller, A., Reusch, P., Trojahn, S.: Robust-stable scheduling in dynamic flow shops based on deep reinforcement learning. J. Intell. Manuf. 35, 667\u2013686 (2022). https:\/\/doi.org\/10.1007\/s10845-022-02069-x","journal-title":"J. Intell. Manuf."},{"key":"17_CR4","doi-asserted-by":"publisher","unstructured":"Grumbach, F., M\u00fcller, A., Reusch, P., Trojahn, S.: Robustness prediction in dynamic production processes - a new surrogate measure based on regression machine learning. Processes 11(4) (2023). https:\/\/doi.org\/10.3390\/pr11041267. https:\/\/www.mdpi.com\/2227-9717\/11\/4\/1267","DOI":"10.3390\/pr11041267"},{"issue":"1","key":"17_CR5","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1049\/cim2.12042","volume":"4","author":"Z He","year":"2022","unstructured":"He, Z., et al.: Improved Q-learning algorithm for solving permutation flow shop scheduling problems. IET Collab. Intell. Manuf. 4(1), 35\u201344 (2022). https:\/\/doi.org\/10.1049\/cim2.12042","journal-title":"IET Collab. Intell. Manuf."},{"key":"17_CR6","doi-asserted-by":"publisher","unstructured":"Kayhan, B.M., Yildiz, G.: Reinforcement learning applications to machine scheduling problems: a comprehensive literature review (2021). https:\/\/doi.org\/10.1007\/s10845-021-01847-3","DOI":"10.1007\/s10845-021-01847-3"},{"issue":"15","key":"17_CR7","doi-asserted-by":"publisher","first-page":"5156","DOI":"10.1080\/00207543.2022.2098871","volume":"61","author":"J Leng","year":"2023","unstructured":"Leng, J., et al.: A multi-objective reinforcement learning approach for resequencing scheduling problems in automotive manufacturing systems. Int. J. Prod. Res. 61(15), 5156\u20135175 (2023). https:\/\/doi.org\/10.1080\/00207543.2022.2098871","journal-title":"Int. J. Prod. Res."},{"issue":"5","key":"17_CR8","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1080\/07408179408966626","volume":"26","author":"JV Leon","year":"1994","unstructured":"Leon, J.V., Wu, D.S., Storer, R.H.: Robustness measures and robust scheduling for job shops. IIE Trans. 26(5), 32\u201343 (1994). https:\/\/doi.org\/10.1080\/07408179408966626","journal-title":"IIE Trans."},{"key":"17_CR9","first-page":"707","volume":"10","author":"VI Levenshtein","year":"1966","unstructured":"Levenshtein, V.I., et al.: Binary codes capable of correcting deletions, insertions, and reversals. Sov. Phys. Dokl. 10, 707\u2013710 (1966)","journal-title":"Sov. Phys. Dokl."},{"issue":"5","key":"17_CR10","doi-asserted-by":"publisher","first-page":"2684","DOI":"10.1109\/TSMC.2022.3219380","volume":"53","author":"H Li","year":"2023","unstructured":"Li, H., Gao, K., Duan, P.Y., Li, J.Q., Zhang, L.: An improved artificial bee colony algorithm with Q-learning for solving permutation flow-shop scheduling problems. IEEE Trans. Syst. Man Cybern. Syst. 53(5), 2684\u20132693 (2023). https:\/\/doi.org\/10.1109\/TSMC.2022.3219380","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"17_CR11","unstructured":"Liang, E., et al.: RLlib: abstractions for distributed reinforcement learning. In: 35th International Conference on Machine Learning, ICML 2018, pp. 4768\u20134780 (2018)"},{"issue":"2","key":"17_CR12","doi-asserted-by":"publisher","first-page":"1853","DOI":"10.1109\/TII.2023.3282313","volume":"20","author":"Z Pan","year":"2023","unstructured":"Pan, Z., Wang, L., Dong, C.X., Chen, J.F.: A knowledge-guided end-to-end optimization framework based on reinforcement learning for flow shop scheduling. IEEE Trans. Ind. Inform. 20(2), 1853\u20131861 (2023). https:\/\/doi.org\/10.1109\/TII.2023.3282313","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"4","key":"17_CR13","doi-asserted-by":"publisher","first-page":"983","DOI":"10.1109\/TETCI.2021.3098354","volume":"7","author":"Z Pan","year":"2023","unstructured":"Pan, Z., Wang, L., Wang, J., Lu, J.: Deep reinforcement learning based optimization algorithm for permutation flow-shop scheduling. IEEE Trans. Emerg. Top. Comput. Intell. 7(4), 983\u2013994 (2023). https:\/\/doi.org\/10.1109\/TETCI.2021.3098354","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"17_CR14","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-2361-4","volume-title":"Scheduling","author":"ML Pinedo","year":"2012","unstructured":"Pinedo, M.L.: Scheduling, 4th edn. Springer, New York (2012). https:\/\/doi.org\/10.1007\/978-1-4614-2361-4","edition":"4"},{"issue":"3","key":"17_CR15","doi-asserted-by":"publisher","first-page":"2787","DOI":"10.1016\/j.aej.2021.01.030","volume":"60","author":"J Ren","year":"2021","unstructured":"Ren, J., Ye, C., Yang, F.: Solving flow-shop scheduling problem with a reinforcement learning algorithm that generalizes the value function with neural network. Alex. Eng. J. 60(3), 2787\u20132800 (2021). https:\/\/doi.org\/10.1016\/j.aej.2021.01.030","journal-title":"Alex. Eng. J."},{"issue":"1\u20134","key":"17_CR16","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1007\/s00170-016-9347-0","volume":"90","author":"FL Rossi","year":"2016","unstructured":"Rossi, F.L., Nagano, M.S., Sagawa, J.K.: An effective constructive heuristic for permutation flow shop scheduling problem with total flow time criterion. Int. J. Adv. Manuf. Technol. 90(1\u20134), 93\u2013107 (2016). https:\/\/doi.org\/10.1007\/s00170-016-9347-0","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"17_CR17","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)"},{"issue":"21","key":"17_CR18","doi-asserted-by":"publisher","first-page":"6531","DOI":"10.1007\/s00500-016-2245-4","volume":"21","author":"XN Shen","year":"2016","unstructured":"Shen, X.N., Han, Y., Fu, J.Z.: Robustness measures and robust scheduling for multi-objective stochastic flexible job shop scheduling problems. Soft. Comput. 21(21), 6531\u20136554 (2016). https:\/\/doi.org\/10.1007\/s00500-016-2245-4","journal-title":"Soft. Comput."},{"key":"17_CR19","volume-title":"Reinforcement Learning: An Introduction","author":"RS Sutton","year":"2018","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. The MIT Press, Cambridge (2018)"},{"issue":"2","key":"17_CR20","doi-asserted-by":"publisher","first-page":"278","DOI":"10.1016\/0377-2217(93)90182-M","volume":"64","author":"E Taillard","year":"1993","unstructured":"Taillard, E.: Benchmarks for basic scheduling problems. Eur. J. Oper. Res. 64(2), 278\u2013285 (1993)","journal-title":"Eur. J. Oper. Res."},{"key":"17_CR21","doi-asserted-by":"publisher","unstructured":"Towers, M., et al.: Gymnasium (2023). https:\/\/doi.org\/10.5281\/zenodo.8127026. https:\/\/zenodo.org\/record\/8127025","DOI":"10.5281\/zenodo.8127026"},{"key":"17_CR22","doi-asserted-by":"publisher","unstructured":"Vollenkemper, L., Grumbach, F., Kohlhase, M., Reusch, P.: Humanzentrierte ablaufplanung von montagelinien\/human-centered scheduling in assembly lines - plug and play: efficient algorithms minimize stress in flow shops. wt Werkstattstechnik Online 113(04), 158\u2013163 (2023). https:\/\/doi.org\/10.37544\/1436-4980-2023-04-58","DOI":"10.37544\/1436-4980-2023-04-58"},{"key":"17_CR23","doi-asserted-by":"publisher","first-page":"100196","DOI":"10.1016\/j.orp.2021.100196","volume":"8","author":"H Yamashiro","year":"2021","unstructured":"Yamashiro, H., Nonaka, H.: Estimation of processing time using machine learning and real factory data for optimization of parallel machine scheduling problem. Oper. Res. Perspect. 8, 100196 (2021). https:\/\/doi.org\/10.1016\/j.orp.2021.100196","journal-title":"Oper. Res. Perspect."},{"issue":"3","key":"17_CR24","doi-asserted-by":"publisher","first-page":"210","DOI":"10.3390\/machines10030210","volume":"10","author":"Q Yan","year":"2022","unstructured":"Yan, Q., Wu, W., Wang, H.: Deep reinforcement learning for distributed flow shop scheduling with flexible maintenance. Machines 10(3), 210 (2022). https:\/\/doi.org\/10.3390\/machines10030210","journal-title":"Machines"},{"key":"17_CR25","doi-asserted-by":"publisher","unstructured":"Zhao, F., Hu, X., Wang, L., Xu, T., Zhu, N., Jonrinaldi: A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem. Int. J. Prod. Res. 61(9), 2854\u20132872 (2023). https:\/\/doi.org\/10.1080\/00207543.2022.2070786","DOI":"10.1080\/00207543.2022.2070786"}],"container-title":["Lecture Notes in Logistics","Dynamics in Logistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-56826-8_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,4,2]],"date-time":"2024-04-02T05:04:53Z","timestamp":1712034293000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-56826-8_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031568251","9783031568268"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-56826-8_17","relation":{},"ISSN":["2194-8917","2194-8925"],"issn-type":[{"type":"print","value":"2194-8917"},{"type":"electronic","value":"2194-8925"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"3 April 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"LDIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Dynamics in Logistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bremen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 February 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 February 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ldic2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.uni-bremen.de\/ldic-conference\/about-ldic","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}