{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,11]],"date-time":"2025-09-11T19:44:56Z","timestamp":1757619896220,"version":"3.44.0"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031789243"},{"type":"electronic","value":"9783031789250"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-3-031-78925-0_29","type":"book-chapter","created":{"date-parts":[[2025,7,26]],"date-time":"2025-07-26T08:07:45Z","timestamp":1753517265000},"page":"295-306","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Reinforcement Learning Method for\u00a0Integrated Production Scheduling and\u00a0Distribution"],"prefix":"10.1007","author":[{"given":"Matheus de Freitas","family":"Araujo","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thiago Henrique","family":"Nogueira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9 Elias Claudio","family":"Arroyo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julio C\u00e9sar","family":"Alves","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,27]]},"reference":[{"key":"29_CR1","doi-asserted-by":"publisher","unstructured":"Alves, J.C., Mateus, G.R.: Deep reinforcement learning and optimization approach for multi-echelon supply chain with uncertain demands. In: International Conference on Computational Logistics, pp. 584\u2013599. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-59747-4_38","DOI":"10.1007\/978-3-030-59747-4_38"},{"issue":"6","key":"29_CR2","doi-asserted-by":"publisher","first-page":"4135","DOI":"10.1016\/j.asoc.2011.02.032","volume":"11","author":"C Blum","year":"2011","unstructured":"Blum, C., Puchinger, J., Raidl, G.R., Roli, A.: Hybrid metaheuristics in combinatorial optimization: a survey. Appl. Soft Comput. 11(6), 4135\u20134151 (2011)","journal-title":"Appl. Soft Comput."},{"key":"29_CR3","doi-asserted-by":"crossref","unstructured":"Chen, Z.L.: Integrated production and distribution operations. In: Handbook of Quantitative Supply Chain Analysis, pp. 711\u2013745 (2004)","DOI":"10.1007\/978-1-4020-7953-5_17"},{"issue":"1","key":"29_CR4","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1287\/opre.1080.0688","volume":"58","author":"ZL Chen","year":"2010","unstructured":"Chen, Z.L.: Integrated production and outbound distribution scheduling: review and extensions. Oper. Res. 58(1), 130\u2013148 (2010)","journal-title":"Oper. Res."},{"key":"29_CR5","unstructured":"IBM ILOG CPLEX: V12. 1: User\u2019s manual for CPLEX. Int. Bus. Mach. Corporation 46(53), 157 (2009)"},{"key":"29_CR6","unstructured":"Felix, G.P., Arroyo, J.E.C.: Heur\u00cdsticas para o sequenciamento da produ\u00c7\u00c3o e roteamento de ve\u00cdculos com frota heterogE\u0302nea. LII Simp\u00f3sio Brasileiro de Pesquisa Operacional (2020)"},{"key":"29_CR7","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1007\/978-3-031-35510-3_13","volume-title":"Intelligent Systems Design and Applications: 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022) Held December 12-14, 2022 - Volume 4","author":"M de Freitas Araujo","year":"2023","unstructured":"de Freitas Araujo, M., Arroyo, J.E.C., Nogueira, T.H.: Heuristics assisted by\u00a0machine learning for\u00a0the\u00a0integrated production planning and\u00a0distribution problem. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds.) Intelligent Systems Design and Applications: 22nd International Conference on Intelligent Systems Design and Applications (ISDA 2022) Held December 12-14, 2022 - Volume 4, pp. 120\u2013131. Springer Nature Switzerland, Cham (2023). https:\/\/doi.org\/10.1007\/978-3-031-35510-3_13"},{"issue":"3","key":"29_CR8","doi-asserted-by":"publisher","first-page":"449","DOI":"10.1016\/S0377-2217(00)00100-4","volume":"130","author":"P Hansen","year":"2001","unstructured":"Hansen, P., Mladenovi\u0107, N.: Variable neighborhood search: principles and applications. Eur. J. Oper. Res. 130(3), 449\u2013467 (2001). https:\/\/doi.org\/10.1016\/S0377-2217(00)00100-4","journal-title":"Eur. J. Oper. Res."},{"key":"29_CR9","doi-asserted-by":"publisher","first-page":"23","DOI":"10.1016\/j.compind.2017.04.002","volume":"89","author":"E Hofmann","year":"2017","unstructured":"Hofmann, E., R\u00fcsch, M.: Industry 4.0 and the current status as well as future prospects on logistics. Comput. Ind. 89, 23\u201334 (2017). https:\/\/doi.org\/10.1016\/j.compind.2017.04.002","journal-title":"Comput. Ind."},{"issue":"3","key":"29_CR10","doi-asserted-by":"publisher","first-page":"905","DOI":"10.1007\/s10845-021-01847-3","volume":"34","author":"BM Kayhan","year":"2023","unstructured":"Kayhan, B.M., Yildiz, G.: Reinforcement learning applications to machine scheduling problems: a comprehensive literature review. J. Intell. Manuf. 34(3), 905\u2013929 (2023). https:\/\/doi.org\/10.1007\/s10845-021-01847-3","journal-title":"J. Intell. Manuf."},{"issue":"1","key":"29_CR11","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1007\/s10732-019-09420-1","volume":"26","author":"L Liu","year":"2020","unstructured":"Liu, L., Li, W., Li, K., Zou, X.: A coordinated production and transportation scheduling problem with minimum sum of order delivery times. J. Heuristics 26(1), 33\u201358 (2020). https:\/\/doi.org\/10.1007\/s10732-019-09420-1","journal-title":"J. Heuristics"},{"key":"29_CR12","doi-asserted-by":"crossref","unstructured":"Nahhas, A., Kharitonov, A., Turowski, K.: Deep reinforcement learning techniques for solving hybrid flow shop scheduling problems: proximal policy optimization (PPO) and asynchronous advantage actor-critic (A3C) (2022)","DOI":"10.24251\/HICSS.2022.206"},{"issue":"11","key":"29_CR13","doi-asserted-by":"publisher","first-page":"1877","DOI":"10.1016\/j.cor.2009.06.014","volume":"37","author":"SU Ngueveu","year":"2010","unstructured":"Ngueveu, S.U., Prins, C., Calvo, R.W.: An effective memetic algorithm for the cumulative capacitated vehicle routing problem. Comput. Oper. Res. 37(11), 1877\u20131885 (2010)","journal-title":"Comput. Oper. Res."},{"key":"29_CR14","unstructured":"Raffin, A., Hill, A., Gleave, A., Kanervisto, A., Ernestus, M., Dormann, N.: Stable-baselines3: reliable reinforcement learning implementations. J. Mach. Learn. Res. 22(268), 1\u20138 (2021). http:\/\/jmlr.org\/papers\/v22\/20-1364.html"},{"issue":"3","key":"29_CR15","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1016\/j.cor.2011.05.005","volume":"39","author":"GM Ribeiro","year":"2012","unstructured":"Ribeiro, G.M., Laporte, G.: An adaptive large neighborhood search heuristic for the cumulative capacitated vehicle routing problem. Comput. Oper. Res. 39(3), 728\u2013735 (2012)","journal-title":"Comput. Oper. Res."},{"key":"29_CR16","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., Klimov, O.: Proximal policy optimization algorithms. arXiv preprint arXiv:1707.06347 (2017)"},{"key":"29_CR17","doi-asserted-by":"publisher","first-page":"643","DOI":"10.1016\/j.cie.2018.11.003","volume":"127","author":"M Tamannaei","year":"2019","unstructured":"Tamannaei, M., Rasti-Barzoki, M.: Mathematical programming and solution approaches for minimizing tardiness and transportation costs in the supply chain scheduling problem. Comput. Ind. Eng. 127, 643\u2013656 (2019)","journal-title":"Comput. Ind. Eng."},{"key":"29_CR18","unstructured":"Zar, J.H.: Biostatistical Analysis, 4th. New Jersey, USA, p. 929 (1999)"},{"key":"29_CR19","doi-asserted-by":"crossref","unstructured":"Zhu, J., Wang, H., Zhang, T.: A deep reinforcement learning approach to the flexible flowshop scheduling problem with makespan minimization. In: 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS), pp. 1220\u20131225. IEEE (2020)","DOI":"10.1109\/DDCLS49620.2020.9275080"}],"container-title":["Lecture Notes in Networks and Systems","Hybrid Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-78925-0_29","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T01:29:55Z","timestamp":1757294995000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-78925-0_29"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9783031789243","9783031789250"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-78925-0_29","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"27 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"HIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Hybrid Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Vilnius","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Lithuania","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"his2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.mirlabs.net\/his23\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}