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Its complexity is further increased by dynamic disruptions, i.e., new job arrivals, and multi-objective optimization demands. To achieve fast and high-quality scheduling under dynamic environments with multi-objective optimization, deep reinforcement learning (DRL) has received growing attention. However, DRL tailored to RHFSP-BPM remains scarce, and existing DRLs often rely on handcrafted features while failing to leverage domain knowledge, thus limiting their effectiveness. To supplement these gaps, a graph-based knowledge-integrated deep reinforcement learning (GKI-DRL) method is proposed for RHFSP-BPM. First, a disjunctive graph with reentry arcs and a Markov decision process are constructed to represent reentrancy and batch operations. On this basis, a dual-agent framework is developed to decouple objective selection and scheduling execution, with a weighted batching policy designed to handle batch decisions effectively. Furthermore, a knowledge-integrated message-passing mechanism is embedded into the graph neural network, enabling heuristic-aware decision-making. The effectiveness of the proposed method and its core improvements are validated based on numerous dynamic RHFSP-BPM instances through ablation studies and comparisons with composite dispatching rules and existing DRL approaches.<\/jats:p>","DOI":"10.1115\/1.4071085","type":"journal-article","created":{"date-parts":[[2026,2,10]],"date-time":"2026-02-10T14:08:34Z","timestamp":1770732514000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":0,"title":["Graph-Based Knowledge-Integrated Deep Reinforcement Learning for Dynamic Multi-Objective Reentrant Hybrid Flow-Shop Scheduling Problem With Batch Processing Machines"],"prefix":"10.1115","volume":"26","author":[{"given":"Xiaoyu","family":"Ren","sequence":"first","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00hn7w693","id-type":"ROR","asserted-by":"publisher"}],"name":"Southwest Jiaotong University School of Mechanical Engineering, , \u00a0 , ;","place":["Chengdu, China, 610031"]},{"name":"Sichuan Provincial Engineering Research Center of Equipment Intelligent Manufacturing of Locomotive and Rolling Stock, Southwest Jiaotong University, Chengdu\u00a0610031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lihan","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00hn7w693","id-type":"ROR","asserted-by":"publisher"}],"name":"Southwest Jiaotong University School of Mechanical Engineering, , \u00a0 , ;","place":["Chengdu, China, 610031"]},{"name":"Sichuan Provincial Engineering Research Center of Equipment Intelligent Manufacturing of Locomotive and Rolling Stock, Southwest Jiaotong University, Chengdu\u00a0610031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00hn7w693","id-type":"ROR","asserted-by":"publisher"}],"name":"Southwest Jiaotong University School of Mechanical Engineering, , \u00a0 , ;","place":["Chengdu, China, 610031"]},{"name":"Sichuan Provincial Engineering Research Center of Equipment Intelligent Manufacturing of Locomotive and Rolling Stock, Southwest Jiaotong University, Chengdu\u00a0610031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haojie","family":"Chen","sequence":"additional","affiliation":[{"id":[{"id":"https:\/\/ror.org\/00hn7w693","id-type":"ROR","asserted-by":"publisher"}],"name":"Southwest Jiaotong University School of Mechanical Engineering, , \u00a0 , ;","place":["Chengdu, China, 610031"]},{"name":"Sichuan Provincial Engineering Research Center of Equipment Intelligent Manufacturing of Locomotive and Rolling Stock, Southwest Jiaotong University, Chengdu\u00a0610031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"33","published-online":{"date-parts":[[2026,5,5]]},"reference":[{"key":"2026050512594718400_CIT0001","doi-asserted-by":"publisher","first-page":"120893","DOI":"10.1016\/j.eswa.2023.120893","article-title":"Improved MOEA\/D With Local Search for Solving Multi-stage Distributed Reentrant Hybrid Flow Shop Scheduling Problem","volume":"232","author":"Wu","year":"2023","journal-title":"Expert Syst. 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