{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T04:02:40Z","timestamp":1750910560942,"version":"3.41.0"},"reference-count":20,"publisher":"IEEE","license":[{"start":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T00:00:00Z","timestamp":1749340800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2025,6,8]],"date-time":"2025-06-08T00:00:00Z","timestamp":1749340800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,6,8]]},"DOI":"10.1109\/cec65147.2025.11043111","type":"proceedings-article","created":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T17:32:32Z","timestamp":1750786352000},"page":"1-8","source":"Crossref","is-referenced-by-count":0,"title":["Improved Genetic Algorithm Using Reinforcement Learning to Solve the Re-entrant Flexible Flow Shop Scheduling Problem"],"prefix":"10.1109","author":[{"given":"Xinzhuo","family":"Wang","sequence":"first","affiliation":[{"name":"Chinese Academy of Science,Shenyang Institute of Automation,Shenyang,China"}]},{"given":"Chang","family":"Liu","sequence":"additional","affiliation":[{"name":"Chinese Academy of Science,Shenyang Institute of Automation,Shenyang,China"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"Chinese Academy of Science,Shenyang Institute of Automation,Shenyang,China"}]},{"given":"Zhenghao","family":"Yu","sequence":"additional","affiliation":[{"name":"University of Chinese Academy of Science,Shenyang Institute of Automation,Shenyang,China"}]},{"given":"Shengxiang","family":"Yang","sequence":"additional","affiliation":[{"name":"De Montfort University,School of Computer Science and Informatics,Leicester,U.K"}]}],"member":"263","reference":[{"issue":"6","key":"ref1","first-page":"102","article-title":"Optimization study of joint scheduling for semiconductor reentrant flow shop based on digital twin simulation","volume":"41","author":"Liu","year":"2024","journal-title":"Journal of Machine Design"},{"issue":"2","key":"ref2","first-page":"122","article-title":"Multi-objective reentrant scheduling problem for semiconductor workshop","volume":"51","author":"Zhu","year":"2023","journal-title":"J. Huazhong Univ. of Sci. & Tech. (Natural Science Edition)"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1109\/ccdc49329.2020.9164464"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2022.108236"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120893"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2023.102605"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2024.109688"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/2747\/1\/012007"},{"key":"ref9","first-page":"1","article-title":"The Application of Hierarchical Optimization Algorithms in Reentrant Hybrid Flow Shop Scheduling","volume-title":"Control Engineering of China","author":"Ren","year":"2025"},{"key":"ref10","first-page":"1","article-title":"Research on the Optimization of Reentrant Hybrid Flow Shop Scheduling Considering Periodic Preventive Maintenance","author":"Fang","year":"2025"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1109\/cac59555.2023.10452002"},{"issue":"2","key":"ref12","first-page":"18","article-title":"An improved genetic algorithm for reentrant hybrid flow shop scheduling","author":"Xuan","year":"2019","journal-title":"Modern Manufacturing Engineering"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1109\/ICAACE61206.2024.10548778"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1016\/j.swevo.2024.101681"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-023-01147-8"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/emcsipi50001.2023.10241752"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2024.110263"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/1796296"},{"key":"ref19","first-page":"1","article-title":"Improved genetic algorithm incorporatin g deep reinforcement learning model for the vehicle routing problem with occasional drivers and scheduled lines","volume-title":"Computer Engineering","author":"Feng","year":"2024"},{"issue":"25","key":"ref20","first-page":"10848","article-title":"Improved genetic algorithm using reinforcement learning to solve flexible job shop scheduling problem","volume":"24","author":"Chen","year":"2024","journal-title":"Science Technology and Engineering"}],"event":{"name":"2025 IEEE Congress on Evolutionary Computation (CEC)","start":{"date-parts":[[2025,6,8]]},"location":"Hangzhou, China","end":{"date-parts":[[2025,6,12]]}},"container-title":["2025 IEEE Congress on Evolutionary Computation (CEC)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx8\/11042929\/11042912\/11043111.pdf?arnumber=11043111","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,25]],"date-time":"2025-06-25T05:43:57Z","timestamp":1750830237000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/11043111\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,8]]},"references-count":20,"URL":"https:\/\/doi.org\/10.1109\/cec65147.2025.11043111","relation":{},"subject":[],"published":{"date-parts":[[2025,6,8]]}}}