{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T04:07:16Z","timestamp":1780373236427,"version":"3.54.1"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032272416","type":"print"},{"value":"9783032272423","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-27242-3_3","type":"book-chapter","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T03:27:12Z","timestamp":1780370832000},"page":"36-53","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Scalability in\u00a0Distributed Flexible Flowshop Scheduling: A Hybrid RL-CP Approach"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9235-5340","authenticated-orcid":false,"given":"Ioannis","family":"Avgerinos","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christos","family":"Katrinakis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-7110-5147","authenticated-orcid":false,"given":"Andreas","family":"Ktenidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2111-8424","authenticated-orcid":false,"given":"Aggelos Ioannis","family":"Lagos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9243-7327","authenticated-orcid":false,"given":"Ioannis","family":"Mourtos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3898-2324","authenticated-orcid":false,"given":"Georgios","family":"Zois","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,1]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Armstrong, E., Garraffa, M., O\u2019Sullivan, B., Simonis, H.: The hybrid flexible flowshop with transportation times. In: 27th International Conference on Principles and Practice of Constraint Programming (CP 2021) 210, pp. 1\u201318 (2021)","DOI":"10.1007\/978-3-031-08011-1_1"},{"key":"3_CR2","doi-asserted-by":"crossref","unstructured":"Cappart, Q., Moisan, T., Rousseau, L.-M., Pr\u00e9mont-Schwarz, I., Cire, A.A.: Combining reinforcement learning and constraint programming for combinatorial optimization. In: Proceedings of the AAAI Conference on Artificial Intelligence Vol. 35, no. (5), pp. 3677\u20133687 (2021)","DOI":"10.1609\/aaai.v35i5.16484"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Chalumeau, F., Coulon, I., Cappart, Q., Rousseau, L-M.: SeaPearl: a constraint programming solver guided by reinforcement learning. In: International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2021), pp. 392\u2013409 (2021)","DOI":"10.1007\/978-3-030-78230-6_25"},{"key":"3_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110309","volume":"264","author":"H Gholami","year":"2023","unstructured":"Gholami, H., Sun, H.: Toward automated algorithm configuration for distributed hybrid flow shop scheduling with multiprocessor tasks. Knowl.-Based Syst. 264, 110309 (2023)","journal-title":"Knowl.-Based Syst."},{"key":"3_CR5","unstructured":"IBM ILOG CPLEX Optimization Studio Documentation: State functions. https:\/\/www.ibm.com\/docs\/en\/icos\/22.1.2?topic=scheduling-state-functions. Last updated 19 Aug 2025"},{"issue":"5","key":"3_CR6","doi-asserted-by":"publisher","first-page":"2864","DOI":"10.1109\/TCYB.2021.3121542","volume":"53","author":"Y Jiang","year":"2023","unstructured":"Jiang, Y., Cao, Z., Zhang, J.: Learning to solve 3-D bin packing problem via deep reinforcement learning and constraint programming. IEEE Trans. Cybern. 53(5), 2864\u20132875 (2023)","journal-title":"IEEE Trans. Cybern."},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Lee, J., Kao, H.-A., Yang, S.: Service innovation and smart analytics for industry 4.0 and big data environment. Procedia CIRP 16, 3\u20138 (2014)","DOI":"10.1016\/j.procir.2014.02.001"},{"issue":"4","key":"3_CR8","doi-asserted-by":"publisher","first-page":"6550","DOI":"10.1109\/TASE.2023.3327792","volume":"21","author":"R Li","year":"2023","unstructured":"Li, R., Gong, W., Wang, L., Lu, C., Pan, Z., Zhang, X.: Double DQN-based coevolution for green distributed heterogeneous hybrid flowshop scheduling with multiple priorities of jobs. IEEE Trans. Autom. Sci. Eng. 21(4), 6550\u20136562 (2023)","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"issue":"13","key":"3_CR9","doi-asserted-by":"publisher","first-page":"3880","DOI":"10.1080\/00207543.2020.1753897","volume":"59","author":"Y Li","year":"2021","unstructured":"Li, Y., et al.: A discrete artificial bee colony algorithm for distributed hybrid flowshop scheduling problem with sequence-dependent setup times. Int. J. Prod. Res. 59(13), 3880\u20133899 (2021)","journal-title":"Int. J. Prod. Res."},{"issue":"7","key":"3_CR10","first-page":"216","volume":"9","author":"Y Li","year":"2025","unstructured":"Li, Y., Yu, C.: Flexible job shop scheduling with job precedence constraints: a deep reinforcement learning approach. J. Manufact. Mater. Process. 9(7), 216 (2025)","journal-title":"J. Manufact. Mater. Process."},{"key":"3_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2022.101058","volume":"71","author":"L Meng","year":"2022","unstructured":"Meng, L., Gao, K., Ren, Y., Zhang, B., Sang, H., Chaoyong, Z.: Novel MILP and CP models for distributed hybrid flowshop scheduling problem with sequence-dependent setup times. Swarm Evol. Comput. 71, 101058 (2022)","journal-title":"Swarm Evol. Comput."},{"issue":"24","key":"3_CR12","doi-asserted-by":"publisher","first-page":"5767","DOI":"10.1016\/j.apm.2014.04.012","volume":"38","author":"B Naderi","year":"2014","unstructured":"Naderi, B., Gohari, S., Yazdani, M.: Hybrid flexible flowshop problems: models and solution methods. Appl. Math. Model. 38(24), 5767\u20135780 (2014)","journal-title":"Appl. Math. Model."},{"key":"3_CR13","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.amc.2017.01.004","volume":"303","author":"Q-K Pan","year":"2017","unstructured":"Pan, Q.-K., Gao, L., Li, X.-Y., Gao, K.-Z.: Effective metaheuristics for scheduling a hybrid flowshop with sequence-dependent setup times. Appl. Math. Comput. 303, 89\u2013112 (2017)","journal-title":"Appl. Math. Comput."},{"key":"3_CR14","doi-asserted-by":"publisher","first-page":"50","DOI":"10.1016\/j.cor.2016.11.022","volume":"80","author":"Q-K Pan","year":"2017","unstructured":"Pan, Q.-K., Ruiz, R., Alfaro-Fern\u00e1ndez, P.: Iterated search methods for earliness and tardiness minimization in hybrid flowshops with due windows. Comput. Oper. Res. 80, 50\u201360 (2017)","journal-title":"Comput. Oper. Res."},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Pugliese, V., Ferreira, O., Faria, F.: Hybrid flow shop scheduling through reinforcement learning: A systematic literature review. In: Proceedings of the 40th ACM\/SIGAPP Symposium on Applied Computing, pp. 1240\u20131249 (2025)","DOI":"10.1145\/3672608.3707903"},{"issue":"1","key":"3_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ejor.2009.09.024","volume":"205","author":"R Ruiz","year":"2010","unstructured":"Ruiz, R., V\u00e1zquez-Rodr\u00edguez, J.A.: The hybrid flow shop scheduling problem. Eur. J. Oper. Res. 205(1), 1\u201318 (2010)","journal-title":"Eur. J. Oper. Res."},{"key":"3_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2020.105527","volume":"194","author":"W Shao","year":"2020","unstructured":"Shao, W., Shao, Z., Pi, D.: Modeling and multi-neighborhood iterated greedy algorithm for distributed hybrid flow shop scheduling problem. Knowl.-Based Syst. 194, 105527 (2020)","journal-title":"Knowl.-Based Syst."},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Smit, I.G., Wu, Y., Troubil, P., Zhang, Y., Nuijten, W.P.M.: Neural combinatorial optimization for stochastic flexible job shop scheduling problems. In: Proceedings of the 39th AAAI Conference on Artificial Intelligence, Vol. 39, no. 25, pp. 26678\u201326687 (2025)","DOI":"10.1609\/aaai.v39i25.34870"},{"key":"3_CR19","doi-asserted-by":"publisher","first-page":"990","DOI":"10.1016\/j.jmsy.2024.10.018","volume":"77","author":"X Sun","year":"2024","unstructured":"Sun, X., Shen, W., Fan, J., Vogel-Heuser, B., Zhang, C.: An improved non-dominated sorting genetic algorithm II for distributed heterogeneous hybrid flow-shop scheduling with blocking constraints. J. Manuf. Syst. 77, 990\u20131008 (2024)","journal-title":"J. Manuf. Syst."},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Tassel, P., Gebser, M., Schekotihin, K.: An end-to-end reinforcement learning approach for job-shop scheduling problems based on constraint programming. In: Proceedings of the International Conference on Automated Planning and Scheduling, Vol. 33, no. (1), pp. 614\u2013622 (2023)","DOI":"10.1609\/icaps.v33i1.27243"},{"issue":"2","key":"3_CR21","doi-asserted-by":"publisher","first-page":"113","DOI":"10.23919\/CSMS.2022.0002","volume":"2","author":"B Xi","year":"2022","unstructured":"Xi, B., Lei, D.: Q-learning-based teaching-learning optimization for distributed two-stage hybrid flow shop scheduling with fuzzy processing time. Complex Syst. Model. Simul. 2(2), 113\u2013129 (2022)","journal-title":"Complex Syst. Model. Simul."},{"key":"3_CR22","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.eswa.2017.09.032","volume":"92","author":"K-C Ying","year":"2018","unstructured":"Ying, K.-C., Lin, S.-W.: Minimizing makespan for the distributed hybrid flowshop scheduling problem with multiprocessor tasks. Expert Syst. Appl. 92, 132\u2013141 (2018)","journal-title":"Expert Syst. Appl."},{"key":"3_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2025.107155","volume":"183","author":"W Zhang","year":"2025","unstructured":"Zhang, W., Bao, X., Geng, H., Zhang, G., Gen, M.: Graph neural network and expert-guided deep reinforcement learning for solving flexible job-shop scheduling problem. Comput. Oper. Res. 183, 107155 (2025)","journal-title":"Comput. Oper. Res."},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, W., Li, C., Gen, M., Yang, W., Zhang, G.: A multiobjective memetic algorithm with particle swarm optimization and Q-learning-based local search for energy-efficient distributed heterogeneous hybrid flow-shop scheduling problem. Expert Syst. Appl. 237(C), 121570 (2024)","DOI":"10.1016\/j.eswa.2023.121570"},{"issue":"3","key":"3_CR25","doi-asserted-by":"publisher","first-page":"3573","DOI":"10.32604\/cmc.2024.055244","volume":"80","author":"Q Zhu","year":"2024","unstructured":"Zhu, Q., Gao, K., Huang, W., Ma, Z., Slowik, A.: Q-learning-assisted meta-heuristics for scheduling distributed hybrid flow shop problems. Comput. Mater. Continua 80(3), 3573 (2024)","journal-title":"Comput. Mater. Continua"}],"container-title":["Lecture Notes in Computer Science","Integration of Constraint Programming, Artificial Intelligence, and Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-27242-3_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T03:27:22Z","timestamp":1780370842000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-27242-3_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032272416","9783032272423"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-27242-3_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"1 June 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CPAIOR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Rabat","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Morocco","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 May 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cpaior2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/cpaior2026\/home","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}