{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T10:56:05Z","timestamp":1778669765030,"version":"3.51.4"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030461324","type":"print"},{"value":"9783030461331","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-46133-1_39","type":"book-chapter","created":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T22:04:13Z","timestamp":1745964253000},"page":"655-671","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Manufacturing Dispatching Using Reinforcement and Transfer Learning"],"prefix":"10.1007","author":[{"given":"Shuai","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chetan","family":"Gupta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Susumu","family":"Serita","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,4,30]]},"reference":[{"key":"39_CR1","doi-asserted-by":"crossref","unstructured":"Park, J., Nguyen, S., Zhang, M., Johnston, M.: Genetic programming for order acceptance and scheduling. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 1005\u20131012. IEEE (2013)","DOI":"10.1109\/CEC.2013.6557677"},{"key":"39_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/978-3-540-71605-1_30","volume-title":"Genetic Programming","author":"D Jakobovi\u0107","year":"2007","unstructured":"Jakobovi\u0107, D., Jelenkovi\u0107, L., Budin, L.: Genetic programming heuristics for multiple machine scheduling. In: Ebner, M., O\u2019Neill, M., Ek\u00e1rt, A., Vanneschi, L., Esparcia-Alc\u00e1zar, A.I. (eds.) EuroGP 2007. LNCS, vol. 4445, pp. 321\u2013330. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-540-71605-1_30"},{"issue":"2","key":"39_CR3","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1287\/moor.1.2.117","volume":"1","author":"MR Garey","year":"1976","unstructured":"Garey, M.R., Johnson, D.S., Sethi, R.: The complexity of flowshop and jobshop scheduling. Math. Oper. Res. 1(2), 117\u2013129 (1976)","journal-title":"Math. Oper. Res."},{"issue":"2","key":"39_CR4","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1057\/jors.2010.132","volume":"62","author":"JA Vazquez-Rodriguez","year":"2011","unstructured":"Vazquez-Rodriguez, J.A., Ochoa, G.: On the automatic discovery of variants of the neh procedure for flow shop scheduling using genetic programming. J. Oper. Res. Soc. 62(2), 381\u2013396 (2011)","journal-title":"J. Oper. Res. Soc."},{"key":"39_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"321","DOI":"10.1007\/978-3-642-44973-4_36","volume-title":"Learning and Intelligent Optimization","author":"F Mascia","year":"2013","unstructured":"Mascia, F., L\u00f3pez-Ib\u00e1\u00f1ez, M., Dubois-Lacoste, J., St\u00fctzle, T.: From grammars to parameters: automatic iterated greedy design for the permutation flow-shop problem with weighted tardiness. In: Nicosia, G., Pardalos, P. (eds.) LION 2013. LNCS, vol. 7997, pp. 321\u2013334. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-44973-4_36"},{"issue":"1","key":"39_CR6","doi-asserted-by":"publisher","first-page":"110","DOI":"10.1109\/TEVC.2015.2429314","volume":"20","author":"J Branke","year":"2016","unstructured":"Branke, J., Nguyen, S., Pickardt, C.W., Zhang, M.: Automated design of production scheduling heuristics: a review. IEEE Trans. Evol. Comput. 20(1), 110\u2013124 (2016)","journal-title":"IEEE Trans. Evol. Comput."},{"key":"39_CR7","volume-title":"An Introduction to Predictive Maintenance","author":"RK Mobley","year":"2002","unstructured":"Mobley, R.K.: An Introduction to Predictive Maintenance. Elsevier, Amsterdam (2002)"},{"key":"39_CR8","doi-asserted-by":"crossref","unstructured":"Zheng, S., Ristovski, K., Farahat, A., Gupta, C.: Long short-term memory network for remaining useful life estimation. In: 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), pp. 88\u201395. IEEE (2017)","DOI":"10.1109\/ICPHM.2017.7998311"},{"key":"39_CR9","doi-asserted-by":"crossref","unstructured":"\u00d6zcan, E., Parkes, A.J.: Policy matrix evolution for generation of heuristics. In: Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation. ACM (2011)","DOI":"10.1145\/2001576.2001846"},{"key":"39_CR10","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"276","DOI":"10.1007\/978-3-642-53856-8_35","volume-title":"Computer Aided Systems Theory - EUROCAST 2013","author":"S Vonolfen","year":"2013","unstructured":"Vonolfen, S., Beham, A., Kommenda, M., Affenzeller, M.: Structural synthesis of dispatching rules for dynamic dial-a-ride problems. In: Moreno-D\u00edaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2013. LNCS, vol. 8111, pp. 276\u2013283. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-53856-8_35"},{"key":"39_CR11","doi-asserted-by":"crossref","unstructured":"Frankola, T., Golub, M., Jakobovic, D.: Evolutionary algorithms for the resource constrained scheduling problem. In: 30th International Conference on Information Technology Interfaces, ITI 2008, pp. 715\u2013722. IEEE (2008)","DOI":"10.1109\/ITI.2008.4588499"},{"key":"39_CR12","doi-asserted-by":"crossref","unstructured":"Mao, H., Alizadeh, M., Menache, I., Kandula, S.: Resource management with deep reinforcement learning. In: Proceedings of the 15th ACM Workshop on Hot Topics in Networks, pp. 50\u201356. ACM (2016)","DOI":"10.1145\/3005745.3005750"},{"key":"39_CR13","doi-asserted-by":"crossref","unstructured":"Chen, X., Hao, X., Lin, H.W., Murata, T.: Rule driven multi objective dynamic scheduling by data envelopment analysis and reinforcement learning. In: 2010 IEEE International Conference on Automation and Logistics (ICAL), pp. 396\u2013401. IEEE (2010)","DOI":"10.1109\/ICAL.2010.5585316"},{"key":"39_CR14","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/978-3-662-44845-8_26","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"S Zheng","year":"2014","unstructured":"Zheng, S., Ding, C.: Kernel alignment inspired linear discriminant analysis. In: Calders, T., Esposito, F., H\u00fcllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8726, pp. 401\u2013416. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-44845-8_26"},{"key":"39_CR15","doi-asserted-by":"crossref","unstructured":"Zheng, S., Nie, F., Ding, C., Huang, H.: A harmonic mean linear discriminant analysis for robust image classification. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 402\u2013409. IEEE (2016)","DOI":"10.1109\/ICTAI.2016.0068"},{"key":"39_CR16","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2018.2861858","author":"S Zheng","year":"2018","unstructured":"Zheng, S., Ding, C., Nie, F., Huang, H.: Harmonic mean linear discriminant analysis. IEEE Trans. Knowl. Data Eng. (2018). https:\/\/doi.org\/10.1109\/TKDE.2018.2861858","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"39_CR17","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.neucom.2019.02.001","volume":"338","author":"S Zheng","year":"2019","unstructured":"Zheng, S., Ding, C.: Sparse classification using group matching pursuit. Neurocomputing 338, 83\u201391 (2019). https:\/\/doi.org\/10.1016\/j.neucom.2019.02.001","journal-title":"Neurocomputing"},{"key":"39_CR18","unstructured":"Zheng, S., Ding, C.: Minimal support vector machine. arXiv preprint arXiv:1804.02370 (2018)"},{"key":"39_CR19","unstructured":"Zheng, S., Ding, C., Nie, F.: Regularized singular value decomposition and application to recommender system. arXiv preprint arXiv:1804.05090 (2018)"},{"key":"39_CR20","unstructured":"Wang, C., Mahadevan, S.: Manifold alignment without correspondence. In: IJCAI, vol. 2, p. 3 (2009)"},{"issue":"2","key":"39_CR21","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1007\/s10845-008-0073-9","volume":"19","author":"GR Weckman","year":"2008","unstructured":"Weckman, G.R., Ganduri, C.V., Koonce, D.A.: A neural network job-shop scheduler. J. Intell. Manuf. 19(2), 191\u2013201 (2008)","journal-title":"J. Intell. Manuf."},{"key":"39_CR22","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-642-25566-3_20","volume-title":"Learning and Intelligent Optimization","author":"H Ingimundardottir","year":"2011","unstructured":"Ingimundardottir, H., Runarsson, T.P.: Supervised learning linear priority dispatch rules for job-shop scheduling. In: Coello, C.A.C. (ed.) LION 2011. LNCS, vol. 6683, pp. 263\u2013277. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-25566-3_20"},{"issue":"6","key":"39_CR23","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1007\/s10951-005-4781-0","volume":"8","author":"X Li","year":"2005","unstructured":"Li, X., Olafsson, S.: Discovering dispatching rules using data mining. J. Sched. 8(6), 515\u2013527 (2005). https:\/\/doi.org\/10.1007\/s10951-005-4781-0","journal-title":"J. Sched."},{"issue":"13","key":"39_CR24","doi-asserted-by":"publisher","first-page":"3669","DOI":"10.1080\/00207540701846236","volume":"47","author":"YR Shiue","year":"2009","unstructured":"Shiue, Y.R.: Data-mining-based dynamic dispatching rule selection mechanism for shop floor control systems using a support vector machine approach. Int. J. Prod. Res. 47(13), 3669\u20133690 (2009)","journal-title":"Int. J. Prod. Res."},{"key":"39_CR25","unstructured":"Zhang, W., Dietterich, T.G.: A reinforcement learning approach to job-shop scheduling. In: IJCAI, vol. 95, pp. 1114\u20131120. Citeseer (1995)"},{"issue":"4","key":"39_CR26","doi-asserted-by":"publisher","first-page":"455","DOI":"10.1145\/2740070.2626334","volume":"44","author":"R Grandl","year":"2015","unstructured":"Grandl, R., Ananthanarayanan, G., Kandula, S., Rao, S., Akella, A.: Multi-resource packing for cluster schedulers. ACM SIGCOMM Comput. Commun. Rev. 44(4), 455\u2013466 (2015)","journal-title":"ACM SIGCOMM Comput. Commun. Rev."},{"key":"39_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"193","DOI":"10.1007\/978-3-642-23400-2_19","volume-title":"Euro-Par 2011 Parallel Processing","author":"S Zheng","year":"2011","unstructured":"Zheng, S., Shae, Z.-Y., Zhang, X., Jamjoom, H., Fong, L.: Analysis and modeling of social influence in high performance computing workloads. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011. LNCS, vol. 6852, pp. 193\u2013204. Springer, Heidelberg (2011). https:\/\/doi.org\/10.1007\/978-3-642-23400-2_19"},{"key":"39_CR28","volume-title":"Reinforcement Learning: An Introduction","author":"RS Sutton","year":"1998","unstructured":"Sutton, R.S., Barto, A.G., et al.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)"},{"issue":"3\u20134","key":"39_CR29","first-page":"229","volume":"8","author":"RJ Williams","year":"1992","unstructured":"Williams, R.J.: Simple statistical gradient-following algorithms for connectionist reinforcement learning. Mach. Learn. 8(3\u20134), 229\u2013256 (1992)","journal-title":"Mach. Learn."},{"key":"39_CR30","unstructured":"Sutton, R.S., McAllester, D.A., Singh, S.P., Mansour, Y.: Policy gradient methods for reinforcement learning with function approximation. In: Advances in Neural Information Processing Systems, pp. 1057\u20131063 (2000)"},{"key":"39_CR31","doi-asserted-by":"crossref","unstructured":"Zheng, S., Vishnu, A., Ding, C.: Accelerating deep learning with shrinkage and recall. In: 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), pp. 963\u2013970. IEEE (2016)","DOI":"10.1109\/ICPADS.2016.0129"},{"key":"39_CR32","unstructured":"Ammar, H.B., Eaton, E., Ruvolo, P., Taylor, M.E.: Unsupervised cross-domain transfer in policy gradient reinforcement learning via manifold alignment. In: Proceedings of AAAI (2015)"},{"key":"39_CR33","doi-asserted-by":"crossref","unstructured":"Joshi, G., Chowdhary, G.: Cross-domain transfer in reinforcement learning using target apprentice. arXiv preprint arXiv:1801.06920 (2018)","DOI":"10.1109\/ICRA.2018.8462977"},{"issue":"10","key":"39_CR34","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan, S.J., Yang, Q., et al.: A survey on transfer learning. IEEE Trans. Knowl. Data Eng. 22(10), 1345\u20131359 (2010)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"39_CR35","doi-asserted-by":"crossref","unstructured":"Zheng, S., Cai, X., Ding, C., Nie, F., Huang, H.: A closed form solution to multi-view low-rank regression. In: AAAI, pp. 1973\u20131979 (2015)","DOI":"10.1609\/aaai.v29i1.9461"},{"key":"39_CR36","unstructured":"Zheng, S.: Machine learning: several advances in linear discriminant analysis, multi-view regression and support vector machine. Ph.D. thesis, The University of Texas at Arlington (2017)"}],"container-title":["Lecture Notes in Computer Science","Machine Learning and Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-46133-1_39","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,29]],"date-time":"2025-04-29T22:04:21Z","timestamp":1745964261000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-46133-1_39"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030461324","9783030461331"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-46133-1_39","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"30 April 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"W\u00fcrzburg","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/ecmlpkdd2019.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"733","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"130","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.04","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"ECML PKDD Workshops Information: single-blind review, submissions: 200, full papers accepted: 70, short papers accepted: 46","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}