{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:18:43Z","timestamp":1783037923680,"version":"3.54.6"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819699001","type":"print"},{"value":"9789819699018","type":"electronic"}],"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\/978-981-96-9901-8_31","type":"book-chapter","created":{"date-parts":[[2025,7,23]],"date-time":"2025-07-23T07:58:28Z","timestamp":1753257508000},"page":"375-386","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Dueling DQN Framework for Solving Multi-objective Flexible Job Shop Scheduling with Urgent Job Insertions and Machine Breakdowns"],"prefix":"10.1007","author":[{"given":"Zhao-Meng","family":"Wu","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zi-Qi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Qian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rong","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"issue":"2","key":"31_CR1","doi-asserted-by":"publisher","first-page":"82","DOI":"10.3390\/info15020082","volume":"15","author":"Y-H Chang","year":"2024","unstructured":"Chang, Y.-H., Liu, C.-H., You, S.-D.: Scheduling for the flexible job-shop problem with a dynamic number of machines using deep reinforcement learning. Information 15(2), 82 (2024)","journal-title":"Information"},{"key":"31_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2022.108387","volume":"171","author":"T Coito","year":"2022","unstructured":"Coito, T., Firme, B., Martins, M.: Integration of industrial IoT architectures for dynamic scheduling. Comput. Ind. Eng. 171, 108387 (2022)","journal-title":"Comput. Ind. Eng."},{"key":"31_CR3","doi-asserted-by":"publisher","first-page":"180","DOI":"10.1016\/j.jmsy.2024.03.002","volume":"74","author":"C Fan","year":"2024","unstructured":"Fan, C., Wang, W., Tian, J.: Flexible job shop scheduling with stochastic machine breakdowns by an improved tuna swarm optimization algorithm. J. Manuf. Syst. 74, 180\u2013197 (2024)","journal-title":"J. Manuf. Syst."},{"key":"31_CR4","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1016\/j.mfglet.2022.12.009","volume":"36","author":"KCW Lim","year":"2023","unstructured":"Lim, K.C.W., Wong, L.-P., Chin, J.-F.: Hyper-heuristic for flexible job shop scheduling problem with stochastic job arrivals. Manufact. Lett. 36, 5\u20138 (2023)","journal-title":"Manufact. Lett."},{"key":"31_CR5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119359","volume":"215","author":"K-X Sun","year":"2023","unstructured":"Sun, K.-X., Zheng, D.-B., Song, H.-H.: Hybrid genetic algorithm with variable neighborhood search for flexible job shop scheduling problem in a machining system. Expert Syst. Appl. 215, 119359 (2023)","journal-title":"Expert Syst. Appl."},{"key":"31_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108487","volume":"133","author":"S Lu","year":"2024","unstructured":"Lu, S., Wang, Y., Kong, M., Wang, W., Tan, W.: A double deep q-network framework for a flexible job shop scheduling problem with dynamic job arrivals and urgent job insertions. Eng. Appl. Artif. Intell. 133, 108487 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"11","key":"31_CR7","doi-asserted-by":"publisher","first-page":"6836","DOI":"10.1109\/TSMC.2023.3287655","volume":"53","author":"C-L Liu","year":"2023","unstructured":"Liu, C.-L., Huang, T.H.: Dynamic job-shop scheduling problems using graph neural network and deep reinforcement learning. IEEE Trans. Syst. Man Cybern. Syst. 53(11), 6836\u20136848 (2023)","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"31_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2023.101414","volume":"83","author":"X-L Chen","year":"2023","unstructured":"Chen, X.-L., Li, J.-Q., Xu, Y.: Q-learning based multi-objective immune algorithm for fuzzy flexible job shop scheduling problem considering dynamic disruptions. Swarm Evol. Comput. 83, 101414 (2023)","journal-title":"Swarm Evol. Comput."},{"key":"31_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2023.106294","volume":"159","author":"R Liu","year":"2023","unstructured":"Liu, R., Piplani, R., Toro, C.: A deep multi-agent reinforcement learning approach to solve dynamic job shop scheduling problem. Comput. Oper. Res. 159, 106294 (2023)","journal-title":"Comput. Oper. Res."},{"issue":"1109","key":"31_CR10","first-page":"1","volume":"10","author":"Y Liu","year":"2024","unstructured":"Liu, Y., Zhang, F., Sun, Y., Zhang, M.: Evolutionary trainer-based deep q-network for dynamic flexible job shop scheduling. IEEE Trans. Evol. Comput. 10(1109), 1 (2024)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"31_CR11","doi-asserted-by":"publisher","first-page":"4","DOI":"10.3390\/pr10040760","volume":"10","author":"J Chang","year":"2022","unstructured":"Chang, J., Yu, D., Hu, Y., He, W., Yu, H.: Deep reinforcement learning for dynamic flexible job shop scheduling with random job arrival. Processes 10, 4 (2022)","journal-title":"Processes"},{"issue":"13","key":"31_CR12","doi-asserted-by":"publisher","first-page":"4049","DOI":"10.1080\/00207543.2022.2058432","volume":"60","author":"R Liu","year":"2022","unstructured":"Liu, R., Piplani, R., Toro, C.: Deep reinforcement learning for dynamic scheduling of a flexible job shop. Int. J. Prod. Res. 60(13), 4049\u20134069 (2022)","journal-title":"Int. J. Prod. Res."},{"key":"31_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2023.101037","volume":"25","author":"N Mishra","year":"2024","unstructured":"Mishra, N., Hafizul Islam, S.K., Zeadally, S.: A survey on security and cryptographic perspective of industrial-Internet-of-Things. Internet Things 25, 101037 (2024)","journal-title":"Internet Things"},{"key":"31_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.108699","volume":"134","author":"L Zhang","year":"2024","unstructured":"Zhang, L., Yan, Y., Hu, Y.: Dynamic flexible scheduling with transportation constraints by multi-agent reinforcement learning. Eng. Appl. Artif. Intell. 134, 108699 (2024)","journal-title":"Eng. Appl. Artif. Intell."},{"key":"31_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2021.102283","volume":"74","author":"Y Li","year":"2022","unstructured":"Li, Y., Gu, W., Yuan, M., Tang, Y.: Real-time data-driven dynamic scheduling for flexible job shop with insufficient transportation resources using hybrid deep q network. Robot. Comput. Integr. Manufact. 74, 102283 (2022)","journal-title":"Robot. Comput. Integr. Manufact."},{"key":"31_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107489","volume":"159","author":"S Luo","year":"2021","unstructured":"Luo, S., Zhang, L., Fan, Y.: Dynamic multi-objective scheduling for flexible job shop by deep reinforcement learning. Comput. Ind. Eng. 159, 107489 (2021)","journal-title":"Comput. Ind. Eng."},{"key":"31_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2025.112787","volume":"171","author":"E Yuan","year":"2025","unstructured":"Yuan, E., Wang, L., Song, S., Cheng, S., Fan, W.: Dynamic scheduling for multi-objective flexible job shop via deep reinforcement learning. Appl. Soft Comput. 171, 112787 (2025)","journal-title":"Appl. Soft Comput."}],"container-title":["Lecture Notes in Computer Science","Advanced Intelligent Computing Technology and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-9901-8_31","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T23:46:58Z","timestamp":1783036018000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-9901-8_31"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819699001","9789819699018"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-9901-8_31","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"24 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Ningbo","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 July 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 July 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icic2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.ic-icc.cn\/icg\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}