{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T21:47:45Z","timestamp":1775166465339,"version":"3.50.1"},"reference-count":41,"publisher":"Springer Science and Business Media LLC","issue":"30","license":[{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T00:00:00Z","timestamp":1691366400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62133011"],"award-info":[{"award-number":["62133011"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62273260"],"award-info":[{"award-number":["62273260"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61873191"],"award-info":[{"award-number":["61873191"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61973237"],"award-info":[{"award-number":["61973237"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,10]]},"DOI":"10.1007\/s00521-023-08877-3","type":"journal-article","created":{"date-parts":[[2023,8,7]],"date-time":"2023-08-07T15:01:47Z","timestamp":1691420507000},"page":"22281-22296","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Double deep Q-network-based self-adaptive scheduling approach for smart shop floor"],"prefix":"10.1007","volume":"35","author":[{"given":"Yumin","family":"Ma","sequence":"first","affiliation":[]},{"given":"Jingwen","family":"Cai","sequence":"additional","affiliation":[]},{"given":"Shengyi","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8934-2127","authenticated-orcid":false,"given":"Juan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Jianmin","family":"Xing","sequence":"additional","affiliation":[]},{"given":"Fei","family":"Qiao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,7]]},"reference":[{"key":"8877_CR1","doi-asserted-by":"publisher","first-page":"3751","DOI":"10.1007\/s00170-019-03754-7","volume":"103","author":"YJ Qu","year":"2019","unstructured":"Qu YJ, Ming XG, Liu ZW, Zhang XY, Hou ZT (2019) Smart manufacturing systems: state of the art and future trends. Int J Adv Manuf Technol 103:3751\u20133768. https:\/\/doi.org\/10.1007\/s00170-019-03754-7","journal-title":"Int J Adv Manuf Technol"},{"key":"8877_CR2","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.jmsy.2018.01.006","volume":"48","author":"F Tao","year":"2018","unstructured":"Tao F, Qing QL, Liu A, Kusiak A (2018) Data-driven smart manufacturing. J Manuf Syst 48:157\u2013169. https:\/\/doi.org\/10.1016\/j.jmsy.2018.01.006","journal-title":"J Manuf Syst"},{"issue":"4","key":"8877_CR3","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1002\/int.21868","volume":"32","author":"YF Zhang","year":"2017","unstructured":"Zhang YF, Wang J, Liu SC, Qian C (2017) Game theory based real-time shop floor scheduling strategy and method for cloud manufacturing. Int J Intell Syst 32(4):437\u2013463. https:\/\/doi.org\/10.1002\/int.21868","journal-title":"Int J Intell Syst"},{"key":"8877_CR4","doi-asserted-by":"publisher","first-page":"117460","DOI":"10.1016\/j.eswa.2022.117460","volume":"203","author":"GH Zhang","year":"2022","unstructured":"Zhang GH, Lu XX, Liu X, Zhang LT, Wei SW, Wang WQ (2022) An effective two-stage algorithm based on convolutional neural network for the bi-objective flexible job shop scheduling problem with machine breakdown. Expert Syst Appl 203:117460. https:\/\/doi.org\/10.1016\/j.eswa.2022.117460","journal-title":"Expert Syst Appl"},{"key":"8877_CR5","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1016\/j.jmsy.2021.08.008","volume":"61","author":"JL Wang","year":"2021","unstructured":"Wang JL, Gao PJ, Zhang P, Zhang J, Ip WH (2021) A fuzzy hierarchical reinforcement learning based scheduling method for semiconductor wafer manufacturing systems. J Manuf Syst 61:239\u2013248. https:\/\/doi.org\/10.1016\/j.jmsy.2021.08.008","journal-title":"J Manuf Syst"},{"key":"8877_CR6","doi-asserted-by":"publisher","first-page":"107402","DOI":"10.1016\/j.asoc.2021.107402","volume":"107","author":"F Fu","year":"2021","unstructured":"Fu F, Zhou H (2021) A combined multi-agent system for distributed multi-project scheduling problems. Appl Soft Comput 107:107402. https:\/\/doi.org\/10.1016\/j.asoc.2021.107402","journal-title":"Appl Soft Comput"},{"key":"8877_CR7","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1007\/s00170-015-7987-0","volume":"85","author":"MA Salido","year":"2016","unstructured":"Salido MA, Escamilla J, Giret A, Barber F (2016) A genetic algorithm for energy-efficiency in job-shop scheduling. Int J Adv Manuf Technol 85:1303\u20131314. https:\/\/doi.org\/10.1007\/s00170-015-7987-0","journal-title":"Int J Adv Manuf Technol"},{"key":"8877_CR8","doi-asserted-by":"publisher","unstructured":"Caricato P, Grieco A, Nucci F (2008) Simulation and mathematical programming for a multi-objective configuration problem in a hybrid flow shop. In: Winter simulation conference, pp. 1820\u20131828. https:\/\/doi.org\/10.1109\/WSC.2008.4736271","DOI":"10.1109\/WSC.2008.4736271"},{"issue":"2","key":"8877_CR9","doi-asserted-by":"publisher","first-page":"1862","DOI":"10.1109\/WSC.2002.1166480","volume":"2002","author":"AK Gupta","year":"2002","unstructured":"Gupta AK, Sivakumar AI (2002) Simulation based multi-objective schedule optimization in semiconductor manufacturing. Proc Winter Simul Confer 2002(2):1862\u20131870. https:\/\/doi.org\/10.1109\/WSC.2002.1166480","journal-title":"Proc Winter Simul Confer"},{"key":"8877_CR10","doi-asserted-by":"publisher","first-page":"988","DOI":"10.1007\/s00170-006-0674-4","volume":"34","author":"A Singh","year":"2007","unstructured":"Singh A, Mehta NK, Jain PK (2007) Multicriteria dynamic scheduling by swapping of dispatching rules. Int J Adv Manuf Technol 34:988\u20131007. https:\/\/doi.org\/10.1007\/s00170-006-0674-4","journal-title":"Int J Adv Manuf Technol"},{"issue":"03","key":"8877_CR11","doi-asserted-by":"publisher","first-page":"529","DOI":"10.13196\/j.cims.2019.03.001","volume":"25","author":"WL Wang","year":"2019","unstructured":"Wang WL, Zhang ZJ, Gao N, Zhao YW (2019) Progress of big data analytics methods based on artificial intelligence technology. Comput Integr Manuf Syst 25(03):529\u2013547. https:\/\/doi.org\/10.13196\/j.cims.2019.03.001","journal-title":"Comput Integr Manuf Syst"},{"issue":"001","key":"8877_CR12","doi-asserted-by":"publisher","first-page":"67","DOI":"10.16451\/j.cnki.issn1003-6059.201901009","volume":"32","author":"LP Wan","year":"2019","unstructured":"Wan LP, Lan XG, Zhang HB, Zheng NN (2019) A review of deep reinforcement learning theory and application. Pattern Recogn Artif Intell 32(001):67\u201381. https:\/\/doi.org\/10.16451\/j.cnki.issn1003-6059.201901009","journal-title":"Pattern Recogn Artif Intell"},{"key":"8877_CR13","doi-asserted-by":"publisher","first-page":"113545","DOI":"10.1016\/j.eswa.2020.113545","volume":"158","author":"Y Yang","year":"2020","unstructured":"Yang Y, Huang M, Wang ZY, Zhu QB (2020) Robust scheduling based on extreme learning machine for bi-objective flexible job-shop problems with machine breakdowns. Expert Syst Appl 158:113545. https:\/\/doi.org\/10.1016\/j.eswa.2020.113545","journal-title":"Expert Syst Appl"},{"key":"8877_CR14","doi-asserted-by":"publisher","first-page":"1439","DOI":"10.1016\/j.promfg.2020.10.200","volume":"51","author":"G Koulinas","year":"2020","unstructured":"Koulinas G, Paraschos P, Koulouriotis D (2020) A decision trees-based knowledge mining approach for controlling a complex production system. Procedia Manuf 51:1439\u20131445. https:\/\/doi.org\/10.1016\/j.promfg.2020.10.200","journal-title":"Procedia Manuf"},{"key":"8877_CR15","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.jmsy.2021.08.002","volume":"61","author":"P Zheng","year":"2021","unstructured":"Zheng P, Xia LQ, Li CX, Li XY, Liu BF (2021) Towards self-X cognitive manufacturing network: an industrial knowledge graph-based multi-agent reinforcement learning approach. J Manuf Syst 61:16\u201326. https:\/\/doi.org\/10.1016\/j.jmsy.2021.08.002","journal-title":"J Manuf Syst"},{"issue":"2","key":"8877_CR16","doi-asserted-by":"publisher","first-page":"100107","DOI":"10.1016\/j.jjimei.2022.100107","volume":"2","author":"A Jamwal","year":"2022","unstructured":"Jamwal A, Agrawal R, Sharma M (2022) Deep learning for manufacturing sustainability: models, applications in Industry 4.0 and implications. Int J Inf Manag Data Insights 2(2):100107. https:\/\/doi.org\/10.1016\/j.jjimei.2022.100107","journal-title":"Int J Inf Manag Data Insights"},{"key":"8877_CR17","doi-asserted-by":"publisher","unstructured":"Zhang, J., Gao, L., Qin, W., Lyu, Y. L., and Li, X. Y. (2016). Big-data-driven operational analysis and decision-making methodology in intelligent workshop. Comput Integr Manuf Syst 22(05), 1220\u20131228. https:\/\/doi.org\/10.13196\/j.cims.2016.05.007.","DOI":"10.13196\/j.cims.2016.05.007"},{"issue":"3","key":"8877_CR18","doi-asserted-by":"publisher","first-page":"1303","DOI":"10.1007\/s10845-017-1325-3","volume":"30","author":"C Wang","year":"2019","unstructured":"Wang C, Jiang PY (2019) Deep neural networks based order completion time prediction by using real-time job shop RFID data. J Intell Manuf 30(3):1303\u20131318. https:\/\/doi.org\/10.1007\/s10845-017-1325-3","journal-title":"J Intell Manuf"},{"issue":"10","key":"8877_CR19","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.ifacol.2022.09.369","volume":"55","author":"LM Steinbacher","year":"2022","unstructured":"Steinbacher LM, Ait-Alla A, Rippel D, D\u00fce T, Freitag M (2022) Modelling framework for reinforcement learning based scheduling applications. IFAC-PapersOnLine 55(10):67\u201372. https:\/\/doi.org\/10.1016\/j.ifacol.2022.09.369","journal-title":"IFAC-PapersOnLine"},{"issue":"4","key":"8877_CR20","doi-asserted-by":"publisher","first-page":"257","DOI":"10.23919\/CSMS.2021.0027","volume":"1","author":"L Wang","year":"2021","unstructured":"Wang L, Pan ZX, Wang JJ (2021) A review of reinforcement learning based intelligent optimization for manufacturing scheduling. Complex Syst Model Simul 1(4):257\u2013270. https:\/\/doi.org\/10.23919\/CSMS.2021.0027","journal-title":"Complex Syst Model Simul"},{"key":"8877_CR21","doi-asserted-by":"publisher","first-page":"604","DOI":"10.1016\/j.cie.2018.03.039","volume":"125","author":"YR Shiue","year":"2018","unstructured":"Shiue YR, Lee KC, Su CT (2018) Real-time scheduling for a smart factory using a reinforcement learning approach. Comput Ind Eng 125:604\u2013614. https:\/\/doi.org\/10.1016\/j.cie.2018.03.039","journal-title":"Comput Ind Eng"},{"key":"8877_CR22","doi-asserted-by":"publisher","unstructured":"Chen XL, Hao XC, Lin HW, Murata T (2010) Rule driven multi objective dynamic scheduling by data envelopment analysis and reinforcement learning. In: ICAL 2010: IEEE international conference on automation and logistics, pp 396\u2013401. https:\/\/doi.org\/10.1109\/ICAL.2010.5585316","DOI":"10.1109\/ICAL.2010.5585316"},{"issue":"2","key":"8877_CR23","doi-asserted-by":"publisher","first-page":"107969","DOI":"10.1016\/j.comnet.2021.107969","volume":"190","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 190(2):107969. https:\/\/doi.org\/10.1016\/j.comnet.2021.107969","journal-title":"Comput Netw"},{"issue":"7540","key":"8877_CR24","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"},{"issue":"7","key":"8877_CR25","doi-asserted-by":"publisher","first-page":"4276","DOI":"10.1109\/TII.2019.2908210","volume":"15","author":"CC Lin","year":"2019","unstructured":"Lin CC, Deng DJ, Chih YL, Chiu HT (2019) Smart manufacturing scheduling with edge computing using multi-class deep Q network. IEEE Trans Ind Informat 15(7):4276\u20134284. https:\/\/doi.org\/10.1109\/TII.2019.2908210","journal-title":"IEEE Trans Ind Informat"},{"key":"8877_CR26","doi-asserted-by":"publisher","unstructured":"Waschneck B, Reichstaller A, Belzner L, Altenmuller T, Bauernhansl T, Knapp A, et al. (2018) Deep reinforcement learning for semiconductor production scheduling. In: 29th annual SEMI advanced semiconductor manufacturing conference, pp 301\u2013306. https:\/\/doi.org\/10.1109\/ASMC.2018.8373191","DOI":"10.1109\/ASMC.2018.8373191"},{"issue":"21","key":"8877_CR27","doi-asserted-by":"publisher","first-page":"106208","DOI":"10.1016\/j.asoc.2020.106208","volume":"91","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 91(21):106208. https:\/\/doi.org\/10.1016\/j.asoc.2020.106208","journal-title":"Appl Soft Comput"},{"issue":"3","key":"8877_CR28","doi-asserted-by":"publisher","first-page":"7","DOI":"10.13196\/j.cims.2015.03.018","volume":"21","author":"YM Ma","year":"2015","unstructured":"Ma YM, Qiao F, Chen X, Tian K, Wu XH (2015) Dynamic scheduling approach based on SVM for semiconductor production line. Comput Integr Manuf Syst 21(3):7. https:\/\/doi.org\/10.13196\/j.cims.2015.03.018","journal-title":"Comput Integr Manuf Syst"},{"issue":"10","key":"8877_CR29","doi-asserted-by":"publisher","first-page":"20","DOI":"10.13603\/j.cnki.51-1621\/z.2019.10.004","volume":"34","author":"M Hong","year":"2019","unstructured":"Hong M, Wang L, Wu LB (2019) Re-understanding of distance from the high point of view: connotation, type and representation. J Neijiang Norm Univer 34(10):20\u201324. https:\/\/doi.org\/10.13603\/j.cnki.51-1621\/z.2019.10.004","journal-title":"J Neijiang Norm Univer"},{"key":"8877_CR30","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1007\/978-3-030-19642-4_24","volume":"976","author":"DN Coelho","year":"2019","unstructured":"Coelho DN, Barreto GA (2019) Approximate linear dependence as a design method for kernel prototype-based classifiers. Adv Intell Syst Comput 976:241\u2013250. https:\/\/doi.org\/10.1007\/978-3-030-19642-4_24","journal-title":"Adv Intell Syst Comput"},{"issue":"3\u20134","key":"8877_CR31","doi-asserted-by":"publisher","first-page":"279","DOI":"10.1007\/BF00992698","volume":"8","author":"C Watkins","year":"1992","unstructured":"Watkins C, J., Dayan, and Peter. (1992) Q-learning. Mach Learn 8(3\u20134):279\u2013292. https:\/\/doi.org\/10.1007\/BF00992698","journal-title":"Mach Learn"},{"key":"8877_CR32","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1007\/978-3-319-46687-3_2","volume":"9947","author":"JW Zhai","year":"2016","unstructured":"Zhai JW, Liu Q, Zhang ZZ, Zhong S, Zhu HJ, Zhang P et al (2016) Deep Q-learning with prioritized sampling. Neural Inf Process 9947:13\u201322. https:\/\/doi.org\/10.1007\/978-3-319-46687-3_2","journal-title":"Neural Inf Process"},{"issue":"1","key":"8877_CR33","doi-asserted-by":"publisher","first-page":"2094","DOI":"10.1609\/aaai.v30i1.10295","volume":"30","author":"HV Hasselt","year":"2016","unstructured":"Hasselt HV, Guez A, Silver D (2016) Deep reinforcement learning with double Q-learning. Thirtieth AAAI Confer Artif Intell 30(1):2094\u20132100. https:\/\/doi.org\/10.1609\/aaai.v30i1.10295","journal-title":"Thirtieth AAAI Confer Artif Intell"},{"key":"8877_CR34","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1016\/j.jmsy.2020.06.012","volume":"58","author":"KS Xia","year":"2020","unstructured":"Xia KS, Sacco C, Kirkpatrick M, Saidy C, Nguyen L, Kircaliali A et al (2020) A digital twin to train deep reinforcement learning agent for smart manufacturing plants: environment, interfaces and intelligence. J Manuf Syst 58:210\u2013230. https:\/\/doi.org\/10.1016\/j.jmsy.2020.06.012","journal-title":"J Manuf Syst"},{"key":"8877_CR35","doi-asserted-by":"publisher","first-page":"106886","DOI":"10.1016\/j.compchemeng.2020.106886","volume":"139","author":"R Nian","year":"2020","unstructured":"Nian R, Liu JF, Huang B (2020) A review on reinforcement learning: introduction and applications in industrial process control. Comput Chem Eng 139:106886. https:\/\/doi.org\/10.1016\/j.compchemeng.2020.106886","journal-title":"Comput Chem Eng"},{"key":"8877_CR36","unstructured":"Kempf K (1994) Intel five-machine six step mini-fab description. Intel\/ASUReport. http:\/\/www.eas.asu.edu\/aar\/research\/in-tel\/papers\/fabspec.Html"},{"issue":"10","key":"8877_CR37","doi-asserted-by":"publisher","first-page":"1052","DOI":"10.3390\/app7101052","volume":"7","author":"YM Ma","year":"2017","unstructured":"Ma YM, Qiao F, Zhao F, Sutherland JW (2017) Dynamic scheduling of a semiconductor production line based on a composite rule set. Appl Sci 7(10):1052. https:\/\/doi.org\/10.3390\/app7101052","journal-title":"Appl Sci"},{"issue":"7","key":"8877_CR38","doi-asserted-by":"publisher","first-page":"780","DOI":"10.1080\/0951192X.2022.2025622","volume":"35","author":"YM Ma","year":"2022","unstructured":"Ma YM, Li SY, Qiao F, Lu XY, Liu J (2022) A data-driven scheduling knowledge management method for smart shop floor. Int J Comput Integr Manuf 35(7):780\u2013793. https:\/\/doi.org\/10.1080\/0951192X.2022.2025622","journal-title":"Int J Comput Integr Manuf"},{"key":"8877_CR39","doi-asserted-by":"publisher","first-page":"4289","DOI":"10.1007\/s00170-022-09632-z","volume":"121","author":"X Fang","year":"2022","unstructured":"Fang X, Wang HH, Liu GJ, Tian XJ, Ding GF, Zhang HZ (2022) Industry application of digital twin: from concept to implementation. Int J Adv Manuf Technol 121:4289\u20134312. https:\/\/doi.org\/10.1007\/s00170-022-09632-z","journal-title":"Int J Adv Manuf Technol"},{"issue":"1","key":"8877_CR40","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1007\/s10845-013-0763-9","volume":"26","author":"BH Zhou","year":"2015","unstructured":"Zhou BH, Li X, Fung RYK (2015) Dynamic scheduling of photolithography process based on Kohonen neural network. J Intell Manuf 26(1):73\u201385. https:\/\/doi.org\/10.1007\/s10845-013-0763-9","journal-title":"J Intell Manuf"},{"key":"8877_CR41","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.jmsy.2021.09.011","volume":"61","author":"JC Serrano-Ruiz","year":"2021","unstructured":"Serrano-Ruiz JC, Mula J, Poler R (2021) Smart manufacturing scheduling: a literature review. J Manuf Syst 61:265\u2013287. https:\/\/doi.org\/10.1016\/j.jmsy.2021.09.011","journal-title":"J Manuf Syst"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08877-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08877-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08877-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,16]],"date-time":"2023-09-16T15:03:48Z","timestamp":1694876628000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08877-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,7]]},"references-count":41,"journal-issue":{"issue":"30","published-print":{"date-parts":[[2023,10]]}},"alternative-id":["8877"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08877-3","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,7]]},"assertion":[{"value":"16 December 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This work does not contain any ethical issues or personal information.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"No human or animal was involved in this work; thus, no consent was required.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"All authors have given their permission for publishing this work.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publication"}}]}}