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To maintain the stability of the scheduling for dynamic flexible job shops with machine breakdown and new job arrival, this article proposes a robust scheduling method that is designed with a flexible network structure and dual-action chained cooperative decision-making mechanism based on deep reinforcement learning (FD-DRL). First, a flexible neural network structure is innovatively constructed, which embeds the feature vector into operation nodes to design a dynamic production state extraction method with graph neural networks (GNN). Second, the dual-action chained cooperative decision-making mechanism is established for agents, who consider the new and remaining operations overall to maximize the utilization of machine idle time. Finally, through training and verification, the effectiveness and advancement of the proposed FD-DRL method are verified by comparing with heuristic\/meta-heuristics and the static model of deep reinforcement learning (DRL).<\/jats:p>","DOI":"10.1115\/1.4070035","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T11:39:55Z","timestamp":1759750795000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":1,"title":["Robust Scheduling Based on Deep Reinforcement Learning for Flexible Job Shop With Machine Breakdown and New Job Arrival"],"prefix":"10.1115","volume":"26","author":[{"given":"Xuemei","family":"Gan","sequence":"first","affiliation":[{"name":"Guizhou University School of Mechanical Engineering, , \u00a0 ,","place":["Guiyang, China, 550025"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Zuo","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00wk2mp56","id-type":"ROR","asserted-by":"publisher"}],"name":"Beihang University School of Automation Science and Electrical Engineering, , \u00a0 ,","place":["Beijing, China, 100191"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guanci","family":"Yang","sequence":"additional","affiliation":[{"name":"Guizhou University Key Laboratory of Advanced Manufacturing Technology, , \u00a0 ,","place":["Guiyang, China, 550025"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ansi","family":"Zhang","sequence":"additional","affiliation":[{"name":"Guizhou University State Key Laboratory of Public Big Data, , \u00a0 ,","place":["Guiyang, China, 550025"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fei","family":"Tao","sequence":"additional","affiliation":[{"name":"Beihang University School of Automation Science and Electrical Engineering, , \u00a0 ,","place":["Beijing, China, 100191"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"issue":"5","key":"2025112712343477200_CIT0001","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1007\/s00170-020-05398-4","article-title":"Hybrid Frameworks for Flexible Job Shop Scheduling","volume":"108","author":"Bharti","year":"2020","journal-title":"Int. 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