{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:23:27Z","timestamp":1777706607981,"version":"3.51.4"},"reference-count":53,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,3,20]],"date-time":"2024-03-20T00:00:00Z","timestamp":1710892800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems: Applications in Engineering and Technology"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:p>\n                    The main focus of this paper is to solve the optimization problem of minimizing the maximum completion time in the flexible job-shop scheduling problem. In order to optimize this objective, random sampling is employed to extract a subset of states, and the mutation operator of the genetic algorithm is used to increase the diversity of sample chromosomes. Additionally, 5-tuple are defined as the state space, and a 4-tuple is designed as the action space. A suitable reward function is also developed. To solve the problem, four reinforcement learning algorithms (Double-Q-learning algorithm, Q-learning algorithm, SARS algorithm, and SARSA(\n                    <jats:italic>\u03bb<\/jats:italic>\n                    ) algorithm) are utilized. This approach effectively extracts states and avoids the curse of dimensionality problem that occurs when using reinforcement learning algorithms. Finally, experimental results using an international benchmark demonstrate the effectiveness of the proposed solution model.\n                  <\/jats:p>","DOI":"10.3233\/jifs-236981","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T12:01:29Z","timestamp":1711108889000},"page":"46-60","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["Research on flexible job-shop scheduling problem based on variation-reinforcement learning"],"prefix":"10.1177","volume":"49","author":[{"given":"Changshun","family":"Shao","sequence":"first","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, Jilin, China"},{"name":"Chongqing Research Institute of Changchun University of Science and Technology, Chongqing, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenglin","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, Jilin, China"},{"name":"Chongqing Research Institute of Changchun University of Science and Technology, Chongqing, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianyin","family":"Tang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zheng","family":"Li","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, Jilin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, Jilin, China"},{"name":"Chongqing Research Institute of Changchun University of Science and Technology, Chongqing, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, Jilin, China"},{"name":"Chongqing Research Institute of Changchun University of Science and Technology, Chongqing, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jingsong","family":"Duan","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electrical Engineering, Changchun University of Science and Technology, Changchun, Jilin, China"},{"name":"Chongqing Research Institute of Changchun University of Science and Technology, Chongqing, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2024,3,20]]},"reference":[{"key":"e_1_3_2_2_1","doi-asserted-by":"publisher","DOI":"10.4995\/ijpme.2017.6618"},{"key":"e_1_3_2_3_1","doi-asserted-by":"publisher","DOI":"10.1080\/00207543.2014.962113"},{"key":"e_1_3_2_4_1","doi-asserted-by":"publisher","DOI":"10.1007\/s40092-018-0280-8"},{"key":"e_1_3_2_5_1","first-page":"106778","article-title":"A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem,&","volume":"149","author":"Chen R.","year":"2020","unstructured":"ChenR., et al., A self-learning genetic algorithm based on reinforcement learning for flexible job-shop scheduling problem,&, Industrial Engineering149 (2020), 106778.","journal-title":"Industrial Engineering"},{"key":"e_1_3_2_6_1","doi-asserted-by":"publisher","DOI":"10.3390\/pr10040760"},{"key":"e_1_3_2_7_1","first-page":"1309","article-title":"Event scheduling algorithm of phased array radar based on branch and bound method","volume":"6","author":"Yi D.","year":"2019","unstructured":"YiD., Event scheduling algorithm of phased array radar based on branch and bound method, Acta Electronica Sinica6 (2019), 1309\u20131314.","journal-title":"Acta Electronica Sinica"},{"key":"e_1_3_2_8_1","unstructured":"DuY. et al. 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