{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T23:52:59Z","timestamp":1770076379678,"version":"3.49.0"},"publisher-location":"Cham","reference-count":26,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783031216855","type":"print"},{"value":"9783031216862","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21686-2_40","type":"book-chapter","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T08:30:15Z","timestamp":1668760215000},"page":"580-595","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Resource Allocation Optimization in\u00a0Business Processes Supported by\u00a0Reinforcement Learning and\u00a0Process Mining"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4806-0830","authenticated-orcid":false,"given":"Thais Rodrigues","family":"Neubauer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0330-3931","authenticated-orcid":false,"given":"Valdinei Freire","family":"da Silva","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6261-1497","authenticated-orcid":false,"given":"Marcelo","family":"Fantinato","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3551-6480","authenticated-orcid":false,"given":"Sarajane Marques","family":"Peres","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"key":"40_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-49851-4","volume-title":"Process Mining: Data Science in Action","author":"WMP van der Aalst","year":"2016","unstructured":"van der Aalst, W.M.P.: Process Mining: Data Science in Action, 2nd edn. Springer, Heidelberg (2016). https:\/\/doi.org\/10.1007\/978-3-662-49851-4","edition":"2"},{"issue":"2","key":"40_CR2","doi-asserted-by":"publisher","first-page":"169","DOI":"10.1016\/S0921-8890(00)00087-7","volume":"33","author":"M Aydin","year":"2000","unstructured":"Aydin, M., \u00d6ztemel, E.: Dynamic job-shop scheduling using reinforcement learning agents. Robot. Auton. Syst. 33(2), 169\u2013178 (2000)","journal-title":"Robot. Auton. Syst."},{"key":"40_CR3","unstructured":"Baker, K.R.: Introduction to Sequencing and Scheduling, 1st edn. Wiley, Hoboken (1974)"},{"key":"40_CR4","doi-asserted-by":"publisher","unstructured":"van Dongen, B.: BPI challenge 2012. 4TU.ResearchData.Dataset (2012). https:\/\/doi.org\/10.4121\/uuid:3926db30-f712-4394-aebc-75976070e91f","DOI":"10.4121\/uuid:3926db30-f712-4394-aebc-75976070e91f"},{"key":"40_CR5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-56509-4","volume-title":"Fundamentals of Business Process Management","author":"M Dumas","year":"2018","unstructured":"Dumas, M., de La Rosa, M., Mendling, J., Reijers, H.A.: Fundamentals of Business Process Management, 2nd edn. Springer, Heidelberg (2018). https:\/\/doi.org\/10.1007\/978-3-662-56509-4","edition":"2"},{"issue":"18","key":"40_CR6","first-page":"503","volume":"6","author":"D Ernst","year":"2005","unstructured":"Ernst, D., Geurts, P., Wehenkel, L.: Tree-based batch mode reinforcement learning. J. Mach. Learn. Res. 6(18), 503\u2013556 (2005)","journal-title":"J. Mach. Learn. Res."},{"issue":"8","key":"40_CR7","first-page":"1090","volume":"32","author":"I Firouzian","year":"2019","unstructured":"Firouzian, I., Zahedi, M., Hassanpour, H.: Cycle time optimization of processes using an entropy-based learning for task allocation. Int. J. Eng. 32(8), 1090\u20131100 (2019)","journal-title":"Int. J. Eng."},{"key":"40_CR8","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1007\/s13740-021-00121-2","volume":"10","author":"F Folino","year":"2021","unstructured":"Folino, F., Pontieri, L.: Ai-empowered process mining for complex application scenarios: survey and discussion. J. Data Semant. 10, 77\u2013106 (2021)","journal-title":"J. Data Semant."},{"key":"40_CR9","doi-asserted-by":"crossref","unstructured":"Garcia, C.d.S., et al.: Process mining techniques and applications - a systematic mapping study. Expert Syst. Appl. 133, 260\u2013295 (2019)","DOI":"10.1016\/j.eswa.2019.05.003"},{"issue":"1","key":"40_CR10","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.datak.2010.09.002","volume":"70","author":"Z Huang","year":"2011","unstructured":"Huang, Z., van der Aalst, W., Lu, X., Duan, H.: Reinforcement learning based resource allocation in business process management. Data Knowl. Eng. 70(1), 127\u2013145 (2011)","journal-title":"Data Knowl. Eng."},{"key":"40_CR11","doi-asserted-by":"crossref","unstructured":"Jaramillo, J., Arias, J.: Automatic classification of event logs sequences for failure detection in WfM\/BPM systems. In: Proceedings of the IEEE Colombian Conference on Applications of Computational Intelligence, pp. 1\u20136. IEEE (2019)","DOI":"10.1109\/ColCACI.2019.8781973"},{"key":"40_CR12","unstructured":"Justin, G.H., Wickens, C.D.: Engineering Psychology and Human Performance. Pearson, Tokyo (1999)"},{"key":"40_CR13","series-title":"Lecture Notes in Business Information Processing","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1007\/978-3-642-28108-2_4","volume-title":"Business Process Management Workshops","author":"A Koschmider","year":"2012","unstructured":"Koschmider, A., Yingbo, L., Schuster, T.: Role assignment in business process models. In: Daniel, F., Barkaoui, K., Dustdar, S. (eds.) BPM 2011. LNBIP, vol. 99, pp. 37\u201349. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-28108-2_4"},{"issue":"3","key":"40_CR14","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1080\/07421222.2002.11045693","volume":"18","author":"A Kumar","year":"2002","unstructured":"Kumar, A., van der Aalst, W., Verbeek, H.: Dynamic work distribution in workflow management systems: how to balance quality and performance. J. Manag. Inf. Syst. 18(3), 157\u2013194 (2002)","journal-title":"J. Manag. Inf. Syst."},{"key":"40_CR15","unstructured":"Levine, S., Kumar, A., Tucker, G., Fu, J.: Offline reinforcement learning: tutorial, review, and perspectives on open problems. CoRR abs\/2005.01643 (2020)"},{"key":"40_CR16","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1007\/978-3-662-46170-9_5","volume-title":"Process-Aware Systems","author":"X Liu","year":"2015","unstructured":"Liu, X., Chen, J., Ji, Yu., Yu, Y.: Q-learning algorithm for task allocation based on social relation. In: Cao, J., Wen, L., Liu, X. (eds.) PAS 2014. CCIS, vol. 495, pp. 49\u201358. Springer, Heidelberg (2015). https:\/\/doi.org\/10.1007\/978-3-662-46170-9_5"},{"key":"40_CR17","first-page":"1","volume":"12","author":"ARC Maita","year":"2017","unstructured":"Maita, A.R.C., Martins, L.C., Paz, C.R.L., Rafferty, L., Hung, P.C.K., Peres, S.M.: A systematic mapping study of process mining. Enterp. Inf. Syst. 12, 1\u201345 (2017)","journal-title":"Enterp. Inf. Syst."},{"key":"40_CR18","doi-asserted-by":"crossref","unstructured":"Puterman, M.L.: Markov Decision Processes. Wiley, Hoboken (1994)","DOI":"10.1002\/9780470316887"},{"key":"40_CR19","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1007\/11564096_32","volume-title":"Machine Learning: ECML 2005","author":"M Riedmiller","year":"2005","unstructured":"Riedmiller, M.: Neural fitted Q iteration \u2013 first experiences with a data efficient neural reinforcement learning method. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 317\u2013328. Springer, Heidelberg (2005). https:\/\/doi.org\/10.1007\/11564096_32"},{"key":"40_CR20","unstructured":"Riedmiller, M., Braun, H.: A direct adaptive method for faster backpropagation learning: the RPROP algorithm. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 1, pp. 586\u2013591 (1993)"},{"key":"40_CR21","unstructured":"da Silva, G.A., Ferreira, D.R.: Applying hidden Markov models to process mining. In: Proceedings of the 4th Iberian Conference on Information Systems and Technologies, pp. 207\u2013210. AISTI (2009)"},{"key":"40_CR22","volume-title":"Reinforcement Learning: An Introduction","author":"RS Sutton","year":"2018","unstructured":"Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (2018)"},{"key":"40_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-28616-2","volume-title":"Business Process Management: Concepts, Languages, Architectures","author":"M Weske","year":"2007","unstructured":"Weske, M.: Business Process Management: Concepts, Languages, Architectures, 2nd edn. Springer, Heidelberg (2007). https:\/\/doi.org\/10.1007\/978-3-642-28616-2","edition":"2"},{"key":"40_CR24","doi-asserted-by":"crossref","unstructured":"Yaghoubi, M., Zahedi, M.: Resource allocation using task similarity distance in business process management systems. In: Proceedings of the 2nd International Conference of Signal Processing and Intelligent Systems, pp. 1\u20135 (2016)","DOI":"10.1109\/ICSPIS.2016.7869851"},{"key":"40_CR25","unstructured":"Zhang, W., Dietterich, T.G.: A reinforcement learning approach to job-shop scheduling. In: Proceedings of the 14th International Joint Conference on Artificial Intelligence, vol. 2, pp. 1114\u20131120. Morgan Kaufmann, Burlington (1995)"},{"issue":"9","key":"40_CR26","doi-asserted-by":"publisher","first-page":"2887","DOI":"10.1007\/s10489-020-01686-4","volume":"50","author":"W Zhao","year":"2020","unstructured":"Zhao, W., Pu, S., Jiang, D.: A human resource allocation method for business processes using team faultlines. Appl. Intell. 50(9), 2887\u20132900 (2020). https:\/\/doi.org\/10.1007\/s10489-020-01686-4","journal-title":"Appl. Intell."}],"container-title":["Lecture Notes in Computer Science","Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21686-2_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:13:46Z","timestamp":1709831626000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21686-2_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031216855","9783031216862"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21686-2_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"19 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"BRACIS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazilian Conference on Intelligent Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Campinas","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Brazil","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"bracis2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www2.sbc.org.br\/bracis2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"JEMS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"225","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":"89","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":"40% - 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","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":"4","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}