{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,20]],"date-time":"2026-04-20T14:53:14Z","timestamp":1776696794903,"version":"3.51.2"},"reference-count":27,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T00:00:00Z","timestamp":1696809600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T00:00:00Z","timestamp":1696809600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Evol. Intel."],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1007\/s12065-023-00885-5","type":"journal-article","created":{"date-parts":[[2023,10,9]],"date-time":"2023-10-09T11:01:46Z","timestamp":1696849306000},"page":"371-383","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Optimization of job shop scheduling problem based on deep reinforcement learning"],"prefix":"10.1007","volume":"17","author":[{"given":"Dongping","family":"Qiao","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lvqi","family":"Duan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"HongLei","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanqiu","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,9]]},"reference":[{"issue":"4","key":"885_CR1","doi-asserted-by":"publisher","first-page":"1063","DOI":"10.1007\/s10845-011-0520-x","volume":"23","author":"S Meeran","year":"2011","unstructured":"Meeran S, Morshed MS (2011) A hybrid genetic tabu search algorithm for solving job shop scheduling problems: a case study. J Intell Manuf 23(4):1063\u20131078. https:\/\/doi.org\/10.1007\/s10845-011-0520-x","journal-title":"J Intell Manuf"},{"issue":"2","key":"885_CR2","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/j.ejor.2021.03.026","volume":"295","author":"MM Ahmadian","year":"2021","unstructured":"Ahmadian MM, Khatami M, Salehipour A, Cheng TCE (2021) Four decades of research on the open-shop scheduling problem to minimize the makespan. Eur J Oper Res 295(2):399\u2013426. https:\/\/doi.org\/10.1016\/j.ejor.2021.03.026","journal-title":"Eur J Oper Res"},{"key":"885_CR3","unstructured":"Wang Y, Zhao Y, Liu W (2021) Application of data mining algorithm in job shop scheduling problem. Computer Integrated Manufacturing Systems 1\u201329."},{"issue":"03","key":"885_CR4","doi-asserted-by":"publisher","first-page":"421","DOI":"10.3785\/j.issn.1008-973X.2015.03.005","volume":"49","author":"C Wang","year":"2015","unstructured":"Wang C, Li C, Feng Y, Rong G (2015) Dispatching rule extraction method for job shop scheduling problem. J Zhejiang Univ Eng Sci 49(03):421\u2013429. https:\/\/doi.org\/10.3785\/j.issn.1008-973X.2015.03.005","journal-title":"J Zhejiang Univ Eng Sci"},{"issue":"3","key":"885_CR5","doi-asserted-by":"publisher","first-page":"568","DOI":"10.1016\/j.ejor.2009.09.019","volume":"203","author":"CU Fuendeling","year":"2010","unstructured":"Fuendeling CU, Trautmann N (2010) A priority-rule method for project scheduling with work-content constraints. Eur J Oper Res 203(3):568\u2013574. https:\/\/doi.org\/10.1016\/j.ejor.2009.09.019","journal-title":"Eur J Oper Res"},{"issue":"03","key":"885_CR6","first-page":"648","volume":"33","author":"H Fan","year":"2016","unstructured":"Fan H, Xiong H, Jang G, Li G (2016) Survey of dispatching rules for dynamic Job-Shop scheduling problem. Appl Res Comput 33(03):648\u2013653","journal-title":"Appl Res Comput"},{"key":"885_CR7","doi-asserted-by":"publisher","first-page":"555","DOI":"10.1016\/j.eswa.2018.06.053","volume":"113","author":"M Durasevic","year":"2018","unstructured":"Durasevic M, Jakobovic D (2018) A survey of dispatching rules for the dynamic unrelated machines environment. Expert Syst Appl 113:555\u2013569. https:\/\/doi.org\/10.1016\/j.eswa.2018.06.053","journal-title":"Expert Syst Appl"},{"issue":"02","key":"885_CR8","first-page":"116","volume":"20","author":"Q Zheng","year":"2015","unstructured":"Zheng Q, Xi L (2015) Heuristics for aircraft moving assembly line scheduling problem. Ind Eng Manage 20(02):116\u2013121","journal-title":"Ind Eng Manage"},{"issue":"2","key":"885_CR9","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1016\/j.cie.2009.03.008","volume":"58","author":"W Mouelhi-Chibani","year":"2010","unstructured":"Mouelhi-Chibani W, Pierreval H (2010) Training a neural network to select dispatching rules in real time. Comput Ind Eng 58(2):249\u2013256. https:\/\/doi.org\/10.1016\/j.cie.2009.03.008","journal-title":"Comput Ind Eng"},{"issue":"7782","key":"885_CR10","doi-asserted-by":"publisher","first-page":"350","DOI":"10.1038\/s41586-019-1724-z","volume":"575","author":"O Vinyals","year":"2019","unstructured":"Vinyals O, Babuschkin I, Czarnecki WM, Mathieu M, Dudzik A, Chung J et al (2019) Grandmaster level in StarCraft II using multi-agent reinforcement learning. Nature 575(7782):350","journal-title":"Nature"},{"key":"885_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2021.107526","author":"Q Wang","year":"2021","unstructured":"Wang Q, Tang C (2021) Deep reinforcement learning for transportation network combinatorial optimization: a survey. Knowl Bas Syst. https:\/\/doi.org\/10.1016\/j.knosys.2021.107526","journal-title":"Knowl Bas Syst"},{"issue":"5","key":"885_CR12","doi-asserted-by":"publisher","first-page":"3576","DOI":"10.1109\/jiot.2020.3025015","volume":"8","author":"W Guo","year":"2021","unstructured":"Guo W, Tian W, Ye Y, Xu L, Wu K (2021) Cloud resource scheduling with deep reinforcement learning and imitation learning. IEEE Internet Things J 8(5):3576\u20133586. https:\/\/doi.org\/10.1109\/jiot.2020.3025015","journal-title":"IEEE Internet Things J"},{"issue":"2","key":"885_CR13","doi-asserted-by":"publisher","first-page":"410","DOI":"10.2507\/ijsimm20-2-co10","volume":"20","author":"Y Zhao","year":"2021","unstructured":"Zhao Y, Zhang H (2021) Application of machine learning and rule scheduling in a job-shop produciton control system. Int J Simul Model 20(2):410\u2013421. https:\/\/doi.org\/10.2507\/ijsimm20-2-co10","journal-title":"Int J Simul Model"},{"issue":"11","key":"885_CR14","first-page":"2609","volume":"36","author":"L Wang","year":"2021","unstructured":"Wang L, Pan Z (2021) Scheduling optimization for flow-shop based on deep reinforcement learning and iterative greedy method. Control Decis 36(11):2609\u20132617","journal-title":"Control Decis"},{"issue":"01","key":"885_CR15","first-page":"192","volume":"27","author":"P Xiao","year":"2021","unstructured":"Xiao P, Zhang C, Meng L, Hong H, Dai W (2021) Non-permutation flow shop scheduling problem based on deep reinforcement learning. Comput Integr Manuf Syst 27(01):192\u2013205","journal-title":"Comput Integr Manuf Syst"},{"key":"885_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106208","author":"S Luo","year":"2020","unstructured":"Luo S (2020) Dynamic scheduling for flexible job shop with new job insertions by deep reinforcement learning. Appl Soft Comput. https:\/\/doi.org\/10.1016\/j.asoc.2020.106208","journal-title":"Appl Soft Comput"},{"key":"885_CR17","volume-title":"Reinforcement learning: An introduction","author":"RS Sutton","year":"2018","unstructured":"Sutton RS, Barto AG (2018) Reinforcement learning: An introduction. MIT press, Cambridge"},{"key":"885_CR18","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, et al. (2013) Playing atari with deep reinforcement learning. arXiv:13125602."},{"issue":"7540","key":"885_CR19","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG et al (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u2013533. https:\/\/doi.org\/10.1038\/nature14236","journal-title":"Nature"},{"key":"885_CR20","unstructured":"Kingma Diederik P, Adam JB (2014) A method for stochastic optimization. arXiv:14126980."},{"key":"885_CR21","unstructured":"Christiano PF, Leike J, Brown T, Martic M, Legg S, Amodei D (2017) Deep reinforcement learning from human preferences. Advances in neural information processing systems 30."},{"issue":"1","key":"885_CR22","doi-asserted-by":"publisher","first-page":"14","DOI":"10.1016\/j.elerap.2011.07.009","volume":"11","author":"T Miller","year":"2012","unstructured":"Miller T, Niu J (2012) An assessment of strategies for choosing between competitive marketplaces. Electron Commer Res Appl 11(1):14\u201323. https:\/\/doi.org\/10.1016\/j.elerap.2011.07.009","journal-title":"Electron Commer Res Appl"},{"key":"885_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.comnet.2021.107969","author":"LB Wang","year":"2021","unstructured":"Wang LB, Hu X, Wang Y, Xu SJ, Ma SJ, Yang KX et al (2021) Dynamic job-shop scheduling in smart manufacturing using deep reinforcement learning. Comput Netw. https:\/\/doi.org\/10.1016\/j.comnet.2021.107969","journal-title":"Comput Netw"},{"key":"885_CR24","unstructured":"OR-Library[EB\/OL]. http:\/\/people.brunel.ac.uk\/~mastjjb\/jeb\/info.html."},{"issue":"10","key":"885_CR25","doi-asserted-by":"publisher","first-page":"2291","DOI":"10.1016\/j.cor.2011.12.005","volume":"39","author":"Ren Q-d-e-j","year":"2012","unstructured":"Q-d-e-j Ren, Wang Y (2012) A new hybrid genetic algorithm for job shop scheduling problem. Comput Oper Res 39(10):2291\u20132299. https:\/\/doi.org\/10.1016\/j.cor.2011.12.005","journal-title":"Comput Oper Res"},{"issue":"2","key":"885_CR26","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1109\/tsmca.2007.914753","volume":"38","author":"H-W Ge","year":"2008","unstructured":"Ge H-W, Sun L, Liang Y-C, Qian F (2008) An effective PSO and AIS-based hybrid intelligent algorithm for job-shop scheduling. IEEE Trans Syst Man Cybern Part Syst Humans 38(2):358\u201368. https:\/\/doi.org\/10.1109\/tsmca.2007.914753","journal-title":"IEEE Trans Syst Man Cybern Part Syst Humans"},{"key":"885_CR27","doi-asserted-by":"publisher","first-page":"186474","DOI":"10.1109\/access.2020.3029868","volume":"8","author":"B-A Han","year":"2020","unstructured":"Han B-A, Yang J-J (2020) Research on adaptive job shop scheduling problems based on dueling double DQN. IEEE Access 8:186474\u2013186495. https:\/\/doi.org\/10.1109\/access.2020.3029868","journal-title":"IEEE Access"}],"container-title":["Evolutionary Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-023-00885-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12065-023-00885-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12065-023-00885-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,17]],"date-time":"2024-02-17T12:34:33Z","timestamp":1708173273000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12065-023-00885-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,9]]},"references-count":27,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2024,2]]}},"alternative-id":["885"],"URL":"https:\/\/doi.org\/10.1007\/s12065-023-00885-5","relation":{"has-preprint":[{"id-type":"doi","id":"10.21203\/rs.3.rs-2148871\/v1","asserted-by":"object"}]},"ISSN":["1864-5909","1864-5917"],"issn-type":[{"value":"1864-5909","type":"print"},{"value":"1864-5917","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,9]]},"assertion":[{"value":"10 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 March 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 September 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 October 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declared that they had no conflicts of interest to this work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}