{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T22:16:59Z","timestamp":1773958619371,"version":"3.50.1"},"reference-count":46,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T00:00:00Z","timestamp":1742774400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72372019"],"award-info":[{"award-number":["72372019"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["2024\u2010MS\u2010176"],"award-info":[{"award-number":["2024\u2010MS\u2010176"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Computational Intelligence"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>As manufacturing faces evolving customer demands, the integration of Industrial Internet of Things (IIoT) networks is crucial for enhancing production flexibility. In this context, the Seru Production System (SPS) has emerged as a highly adaptable production mode and emphasizes the strategic assignment of cross\u2010trained workers, particularly in hybrid configurations combining divisional and rotating serus. This paper proposes a novel bi\u2010objective mathematical model incorporating learning effects to minimize makespan and balance workloads among workers. With the development of Artificial Intelligence Generated Content (AIGC) empowered big models, new breakthroughs have emerged in industrial manufacturing decision\u2010making. These models utilize deep learning for foundational content processing and leverage reinforcement learning to optimize strategies. This process provides robust support for achieving efficient decision optimization. Building on the concepts of AIGC big models training, this study employs reinforcement learning to refine the results of multi\u2010objective genetic algorithms, thereby improving the solution capability of the bi\u2010objective model. Experimental results demonstrate that the proposed algorithm effectively provides optimal strategies for tuning crossover and mutation operations. Additionally, numerical experiments offer insights into the formation of hybrid SPS configurations.<\/jats:p>","DOI":"10.1111\/coin.70048","type":"journal-article","created":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T06:30:45Z","timestamp":1742970645000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Reinforcement Learning Driven Cross\u2010Trained Worker Assignment Approach Based on Big Models: A Study for A Hybrid Seru Production System Considering Learning Effect"],"prefix":"10.1111","volume":"41","author":[{"given":"Taixin","family":"Li","sequence":"first","affiliation":[{"name":"School of Data Science and Artificial Intelligence Dongbei University of Finance and Economics  Dalian China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenxi","family":"Ye","sequence":"additional","affiliation":[{"name":"School of Accountancy Central University of Finance and Economics  Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lang","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Management Shenzhen University  Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Management Science and Engineering Dongbei University of Finance and Economics  Dalian China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chengxiao","family":"Yu","sequence":"additional","affiliation":[{"name":"Pengcheng Laboratory  Shenzhen China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,3,24]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2017.1403664"},{"issue":"2","key":"e_1_2_11_3_1","first-page":"134","article-title":"Minimizing the Maximum Tardiness and Makespan Criteria in a Job Shop Scheduling Problem With Sequence Dependent Setup Times","volume":"11","author":"Heydari M.","year":"2018","journal-title":"International Journal of Industrial and Systems Engineering"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.4018\/jsds.2012010104"},{"key":"e_1_2_11_5_1","volume-title":"Encyclopedia in Operations Management","author":"Yin Y.","year":"2005"},{"key":"e_1_2_11_6_1","first-page":"27","article-title":"The Evolution of Seru Production Systems Throughout Canon","volume":"2","author":"Yin Y.","year":"2008","journal-title":"Operations Management Education Review"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2021.107552"},{"key":"e_1_2_11_8_1","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.jom.2017.01.003","article-title":"Lessons From Seru Production on Manufacturing Competitively in a High Cost Environment","volume":"49","author":"Yin Y.","year":"2017","journal-title":"Journal of Operations Management"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2023.101940"},{"issue":"7540","key":"e_1_2_11_10_1","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1038\/nature14236","article-title":"Human\u2010Level Control Through Deep Reinforcement Learning","volume":"518","author":"Mnih V.","year":"2015","journal-title":"Nature"},{"key":"e_1_2_11_11_1","doi-asserted-by":"crossref","first-page":"104213","DOI":"10.1016\/j.compind.2024.104213","article-title":"Wasserstein Distributionally Robust Learning for Predicting the Cycle Time of Printed Circuit Board Production","volume":"164","author":"Liu F.","year":"2025","journal-title":"Computers in Industry"},{"key":"e_1_2_11_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/JAS.2023.123618"},{"key":"e_1_2_11_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/nature14539"},{"key":"e_1_2_11_14_1","first-page":"1","article-title":"Online AI\u2010Generated Content Request Scheduling With Deep Reinforcement Learning","author":"Feng C.","year":"2024","journal-title":"IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS)"},{"key":"e_1_2_11_15_1","doi-asserted-by":"crossref","first-page":"127489","DOI":"10.1109\/ACCESS.2024.3452190","article-title":"An Energy\u2010Efficient Dynamic Offloading Algorithm for Edge Computing Based on Deep Reinforcement Learning","volume":"12","author":"Zhu K.","year":"2024","journal-title":"IEEE Access"},{"key":"e_1_2_11_16_1","article-title":"A Comprehensive Survey of AI\u2010Generated Content (AIGC): A History of Generative AI From GAN to ChatGPT","author":"Gao Y. 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