{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T08:12:24Z","timestamp":1778746344637,"version":"3.51.4"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T00:00:00Z","timestamp":1753833600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100013141","name":"Jilin Provincial Key Research and Development Plan Project","doi-asserted-by":"publisher","award":["20220402019GH"],"award-info":[{"award-number":["20220402019GH"]}],"id":[{"id":"10.13039\/501100013141","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1007\/s10845-025-02648-8","type":"journal-article","created":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T15:09:54Z","timestamp":1753888194000},"page":"2395-2411","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automated guided vehicles intelligent scheduling in dynamic environments for automobile manufacturing stamping production"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7245-0663","authenticated-orcid":false,"given":"Yanjuan","family":"Hu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changhua","family":"Yin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenpeng","family":"Cheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fumei","family":"Zhong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,30]]},"reference":[{"issue":"5","key":"2648_CR1","doi-asserted-by":"publisher","first-page":"1373","DOI":"10.1080\/00207543.2021.2023777","volume":"61","author":"L Cai","year":"2023","unstructured":"Cai, L., Li, W., Luo, Y., & He, L. (2023). Real-time scheduling simulation optimisation of job shop in a production-logistics collaborative environment. International Journal of Production Research, 61(5), 1373\u20131393. https:\/\/doi.org\/10.1080\/00207543.2021.2023777","journal-title":"International Journal of Production Research"},{"issue":"3","key":"2648_CR2","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1109\/TASE.2022.3183610","volume":"20","author":"R Chai","year":"2023","unstructured":"Chai, R., Liu, D., Liu, T., Tsourdos, A., Xia, Y., & Chai, S. (2023). Deep learning-based trajectory planning and control for autonomous ground vehicle parking maneuver. IEEE Transactions on Automation Science and Engineering, 20(3), 1633\u20131647. https:\/\/doi.org\/10.1109\/TASE.2022.3183610","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"issue":"6","key":"2648_CR3","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1080\/0951192X.2021.1992669","volume":"35","author":"K Chang","year":"2022","unstructured":"Chang, K., Park, S. H., & Baek, J.-G. (2022). AGV dispatching algorithm based on deep Q-network in CNC machines environment. International Journal of Computer Integrated Manufacturing, 35(6), 662\u2013677. https:\/\/doi.org\/10.1080\/0951192X.2021.1992669","journal-title":"International Journal of Computer Integrated Manufacturing"},{"key":"2648_CR4","doi-asserted-by":"publisher","first-page":"106229","DOI":"10.1016\/j.engappai.2023.106229","volume":"123","author":"J Chol","year":"2023","unstructured":"Chol, J., & Gun, C. R. (2023). Multi-agent based scheduling method for tandem automated guided vehicle systems. Engineering Applications of Artificial Intelligence, 123, 106229. https:\/\/doi.org\/10.1016\/j.engappai.2023.106229","journal-title":"Engineering Applications of Artificial Intelligence"},{"issue":"11","key":"2648_CR5","doi-asserted-by":"publisher","first-page":"11253","DOI":"10.1109\/JSEN.2023.3237206","volume":"23","author":"J Cui","year":"2023","unstructured":"Cui, J., Yuan, L., He, L., Xiao, W., Ran, T., & Zhang, J. (2023). Multi-input autonomous driving based on deep reinforcement learning with double bias experience replay. IEEE Sensors Journal, 23(11), 11253\u201311261. https:\/\/doi.org\/10.1109\/JSEN.2023.3237206","journal-title":"IEEE Sensors Journal"},{"issue":"2","key":"2648_CR6","doi-asserted-by":"publisher","first-page":"409","DOI":"10.1016\/j.ejor.2023.05.017","volume":"314","author":"S Dauz\u00e8re-P\u00e9r\u00e8s","year":"2024","unstructured":"Dauz\u00e8re-P\u00e9r\u00e8s, S., Ding, J., Shen, L., & Tamssaouet, K. (2024). The flexible job shop scheduling problem: A review. European Journal of Operational Research, 314(2), 409\u2013432. https:\/\/doi.org\/10.1016\/j.ejor.2023.05.017","journal-title":"European Journal of Operational Research"},{"issue":"4","key":"2648_CR7","doi-asserted-by":"publisher","first-page":"1744","DOI":"10.1109\/TII.2017.2749520","volume":"14","author":"G Demesure","year":"2018","unstructured":"Demesure, G., Defoort, M., Bekrar, A., Trentesaux, D., & Djema\u00ef, M. (2018). Decentralized Motion Planning and Scheduling of AGVs in an FMS. IEEE Transactions on Industrial Informatics, 14(4), 1744\u20131752. https:\/\/doi.org\/10.1109\/TII.2017.2749520","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2648_CR8","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.jmsy.2021.01.009","volume":"59","author":"B Farooq","year":"2021","unstructured":"Farooq, B., Bao, J., Raza, H., Sun, Y., & Ma, Q. (2021). Flow-shop path planning for multi-automated guided vehicles in intelligent textile spinning cyber-physical production systems dynamic environment. Journal of Manufacturing Systems, 59, 98\u2013116. https:\/\/doi.org\/10.1016\/j.jmsy.2021.01.009","journal-title":"Journal of Manufacturing Systems"},{"issue":"4","key":"2648_CR9","doi-asserted-by":"publisher","first-page":"4287","DOI":"10.1007\/s40747-022-00948-7","volume":"9","author":"Y Gu","year":"2023","unstructured":"Gu, Y., Zhu, Z., Lv, J., Shi, L., Hou, Z., & Xu, S. (2023). DM-DQN: dueling munchausen deep Q network for robot path planning. Complex & Intelligent Systems, 9(4), 4287\u20134300. https:\/\/doi.org\/10.1007\/s40747-022-00948-7","journal-title":"Complex & Intelligent Systems"},{"key":"2648_CR10","doi-asserted-by":"publisher","first-page":"106749","DOI":"10.1016\/j.cie.2020.106749","volume":"149","author":"H Hu","year":"2020","unstructured":"Hu, H., Jia, X., He, Q., Fu, S., & Liu, K. (2020). Deep reinforcement learning based AGVs real-time scheduling with mixed rule for flexible shop floor in industry 4.0. Computers & Industrial Engineering, 149, 106749. https:\/\/doi.org\/10.1016\/j.cie.2020.106749","journal-title":"Computers & Industrial Engineering"},{"issue":"1","key":"2648_CR11","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1080\/00207543.2021.1998695","volume":"61","author":"H Hu","year":"2023","unstructured":"Hu, H., Yang, X., Xiao, S., & Wang, F. (2023). Anti-conflict AGV path planning in automated container terminals based on multi-agent reinforcement learning. International Journal of Production Research, 61(1), 65\u201380. https:\/\/doi.org\/10.1080\/00207543.2021.1998695","journal-title":"International Journal of Production Research"},{"issue":"11","key":"2648_CR12","doi-asserted-by":"publisher","first-page":"3534","DOI":"10.1080\/00207543.2021.1925772","volume":"60","author":"Z Jiang","year":"2022","unstructured":"Jiang, Z., Yuan, S., Ma, J., & Wang, Q. (2022). The evolution of production scheduling from Industry 3.0 through Industry 4.0. International Journal of Production Research, 60(11), 3534\u20133554. https:\/\/doi.org\/10.1080\/00207543.2021.1925772","journal-title":"International Journal of Production Research"},{"key":"2648_CR13","doi-asserted-by":"publisher","first-page":"103","DOI":"10.1016\/j.jmsy.2019.12.001","volume":"54","author":"S Kumar","year":"2020","unstructured":"Kumar, S., Manjrekar, V., Singh, V., & Kumar Lad, B. (2020). Integrated yet distributed operations planning approach: A next generation manufacturing planning system. Journal of Manufacturing Systems, 54, 103\u2013122. https:\/\/doi.org\/10.1016\/j.jmsy.2019.12.001","journal-title":"Journal of Manufacturing Systems"},{"issue":"7","key":"2648_CR14","doi-asserted-by":"publisher","first-page":"1720","DOI":"10.1109\/JAS.2023.123300","volume":"11","author":"Y Lin","year":"2024","unstructured":"Lin, Y., Hue, G., Wang, L., Li, Q., & Zhu, J. (2024). A Multi-AGV routing planning method based on deep reinforcement learning and recurrent neural network. IEEE\/CAA Journal of Automatica Sinica, 11(7), 1720\u20131722. https:\/\/doi.org\/10.1109\/JAS.2023.123300","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"2648_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.simpat.2021.102430","volume":"116","author":"J L\u00f3pez","year":"2022","unstructured":"L\u00f3pez, J., Zalama, E., & G\u00f3mez-Garc\u00eda-Bermejo, J. (2022). A simulation and control framework for AGV based transport systems. Simulation Modelling Practice and Theory, 116, Article 102430. https:\/\/doi.org\/10.1016\/j.simpat.2021.102430","journal-title":"Simulation Modelling Practice and Theory"},{"key":"2648_CR16","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1016\/j.jmsy.2017.03.009","volume":"44","author":"S Lu","year":"2017","unstructured":"Lu, S., Xu, C., Zhong, R. Y., & Wang, L. (2017). A RFID-enabled positioning system in automated guided vehicle for smart factories. Journal of Manufacturing Systems, 44, 179\u2013190. https:\/\/doi.org\/10.1016\/j.jmsy.2017.03.009","journal-title":"Journal of Manufacturing Systems"},{"key":"2648_CR17","doi-asserted-by":"publisher","first-page":"127170","DOI":"10.1016\/j.neucom.2023.127170","volume":"576","author":"A Ly","year":"2024","unstructured":"Ly, A., Dazeley, R., Vamplew, P., Cruz, F., & Aryal, S. (2024). Elastic step DQN: A novel multi-step algorithm to alleviate overestimation in deep Q-networks. Neurocomputing, 576, 127170. https:\/\/doi.org\/10.1016\/j.neucom.2023.127170","journal-title":"Neurocomputing"},{"key":"2648_CR18","doi-asserted-by":"publisher","first-page":"121947","DOI":"10.1016\/j.eswa.2023.121947","volume":"238","author":"T Mraihi","year":"2024","unstructured":"Mraihi, T., Driss, O. B., & EL-Haouzi, H. B. (2024). Distributed permutation flow shop scheduling problem with worker flexibility: Review, trends and model proposition. Expert Systems with Applications, 238, 121947. https:\/\/doi.org\/10.1016\/j.eswa.2023.121947","journal-title":"Expert Systems with Applications"},{"key":"2648_CR19","doi-asserted-by":"publisher","first-page":"109976","DOI":"10.1016\/j.cie.2024.109976","volume":"189","author":"J Mumtaz","year":"2024","unstructured":"Mumtaz, J., Minhas, K. A., Rauf, M., Yue, L., & Chen, Y. (2024). Solving line balancing and AGV scheduling problems for intelligent decisions using a genetic-artificial bee colony algorithm. Computers & Industrial Engineering, 189, 109976. https:\/\/doi.org\/10.1016\/j.cie.2024.109976","journal-title":"Computers & Industrial Engineering"},{"key":"2648_CR20","doi-asserted-by":"publisher","first-page":"109678","DOI":"10.1016\/j.cie.2023.109678","volume":"187","author":"N Singh","year":"2024","unstructured":"Singh, N., Akcay, A., Dang, Q.-V., Martagan, T., & Adan, I. (2024). Dispatching AGVs with battery constraints using deep reinforcement learning. Computers & Industrial Engineering, 187, 109678. https:\/\/doi.org\/10.1016\/j.cie.2023.109678","journal-title":"Computers & Industrial Engineering"},{"issue":"1","key":"2648_CR21","doi-asserted-by":"publisher","first-page":"341","DOI":"10.1109\/TITS.2022.3215776","volume":"24","author":"PZH Sun","year":"2023","unstructured":"Sun, P. Z. H., You, J., Qiu, S., Wu, E. Q., Xiong, P., Song, A., et al. (2023). AGV-based vehicle transportation in automated container terminals: A survey. IEEE Transactions on Intelligent Transportation Systems, 24(1), 341\u2013356. https:\/\/doi.org\/10.1109\/TITS.2022.3215776","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"issue":"4","key":"2648_CR22","doi-asserted-by":"publisher","first-page":"1275","DOI":"10.1080\/00207543.2024.2373426","volume":"63","author":"H Tang","year":"2025","unstructured":"Tang, H., Huang, J., Ren, C., Shao, Y., & Lu, J. (2025). Integrated scheduling of multi-objective lot-streaming hybrid flowshop with AGV based on deep reinforcement learning. International Journal of Production Research, 63(4), 1275\u20131303. https:\/\/doi.org\/10.1080\/00207543.2024.2373426","journal-title":"International Journal of Production Research"},{"key":"2648_CR23","doi-asserted-by":"publisher","first-page":"101776","DOI":"10.1016\/j.aei.2022.101776","volume":"54","author":"S Yang","year":"2022","unstructured":"Yang, S., Wang, J., & Xu, Z. (2022). Real-time scheduling for distributed permutation flowshops with dynamic job arrivals using deep reinforcement learning. Advanced Engineering Informatics, 54, 101776. https:\/\/doi.org\/10.1016\/j.aei.2022.101776","journal-title":"Advanced Engineering Informatics"},{"key":"2648_CR24","doi-asserted-by":"publisher","first-page":"110436","DOI":"10.1016\/j.asoc.2023.110436","volume":"143","author":"E Yuan","year":"2023","unstructured":"Yuan, E., Cheng, S., Wang, L., Song, S., & Wu, F. (2023). Solving job shop scheduling problems via deep reinforcement learning. Applied Soft Computing, 143, 110436. https:\/\/doi.org\/10.1016\/j.asoc.2023.110436","journal-title":"Applied Soft Computing"},{"issue":"12","key":"2648_CR25","doi-asserted-by":"publisher","first-page":"8999","DOI":"10.1109\/TII.2022.3178410","volume":"18","author":"L Zhang","year":"2022","unstructured":"Zhang, L., Yang, C., Yan, Y., & Hu, Y. (2022). Distributed real-time scheduling in cloud manufacturing by deep reinforcement learning. IEEE Transactions on Industrial Informatics, 18(12), 8999\u20139007. https:\/\/doi.org\/10.1109\/TII.2022.3178410","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"2648_CR26","doi-asserted-by":"publisher","first-page":"109718","DOI":"10.1016\/j.cie.2023.109718","volume":"186","author":"M Zhang","year":"2023","unstructured":"Zhang, M., Wang, L., Qiu, F., & Liu, X. (2023). Dynamic scheduling for flexible job shop with insufficient transportation resources via graph neural network and deep reinforcement learning. Computers & Industrial Engineering, 186, 109718. https:\/\/doi.org\/10.1016\/j.cie.2023.109718","journal-title":"Computers & Industrial Engineering"},{"key":"2648_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2021.103511","volume":"132","author":"Y Zhang","year":"2021","unstructured":"Zhang, Y., Tang, D., Zhu, H., Li, S., & Nie, Q. (2021). A flexible configuration method of distributed manufacturing resources in the context of social manufacturing. Computers in Industry, 132, Article 103511. https:\/\/doi.org\/10.1016\/j.compind.2021.103511","journal-title":"Computers in Industry"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-025-02648-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-025-02648-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-025-02648-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T07:42:21Z","timestamp":1778744541000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-025-02648-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,30]]},"references-count":27,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2026,6]]}},"alternative-id":["2648"],"URL":"https:\/\/doi.org\/10.1007\/s10845-025-02648-8","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,30]]},"assertion":[{"value":"5 May 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 June 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}