{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T16:19:58Z","timestamp":1747153198952,"version":"3.40.5"},"publisher-location":"Cham","reference-count":14,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031366246"},{"type":"electronic","value":"9783031366253"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-36625-3_27","type":"book-chapter","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T12:02:36Z","timestamp":1688731356000},"page":"335-349","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Monte Carlo Tree Search with\u00a0Adaptive Estimation for\u00a0DAG Scheduling"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3324-9091","authenticated-orcid":false,"given":"Alexander","family":"Allahverdyan","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6850-2424","authenticated-orcid":false,"given":"Anastasiia","family":"Zhadan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6343-7168","authenticated-orcid":false,"given":"Ivan","family":"Kondratov","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8210-6084","authenticated-orcid":false,"given":"Vikenty","family":"Mikheev","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7908-2261","authenticated-orcid":false,"given":"Ovanes","family":"Petrosian","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9103-4203","authenticated-orcid":false,"given":"Aleksei","family":"Romanovskii","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4294-4555","authenticated-orcid":false,"given":"Vitaliy","family":"Kharin","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,8]]},"reference":[{"issue":"10","key":"27_CR1","doi-asserted-by":"publisher","first-page":"3080","DOI":"10.1080\/00207543.2018.1535205","volume":"57","author":"S Chen","year":"2019","unstructured":"Chen, S., Fang, S., Tang, R.: A reinforcement learning based approach for multi-projects scheduling in cloud manufacturing. Int. J. Prod. Res. 57(10), 3080\u20133098 (2019)","journal-title":"Int. J. Prod. Res."},{"key":"27_CR2","first-page":"1","volume-title":"Branch and Bound Algorithms-principles And Examples","author":"J Clausen","year":"1999","unstructured":"Clausen, J.: Branch and Bound Algorithms-principles And Examples, pp. 1\u201330. Department of Computer Science, University of Copenhagen pp (1999)"},{"key":"27_CR3","unstructured":"Flint, C., Bramas, B.: Finding new heuristics for automated task prioritizing in heterogeneous computing. HAL-Inria preprint, pp. 1\u201343 (Nov 2020). https:\/\/hal.inria.fr\/hal-02993015, working paper or preprint"},{"key":"27_CR4","unstructured":"Gurobi Optimization, LLC: Gurobi Optimizer Reference Manual (2023). https:\/\/www.gurobi.com"},{"key":"27_CR5","doi-asserted-by":"crossref","unstructured":"Hwang, J.J., Chow, Y.C., Anger, F.D., Lee, C.Y.: Scheduling precedence graphs in systems with interprocessor communication times. Siam J. Comput. 18(2), 244\u2013257 (1989)","DOI":"10.1137\/0218016"},{"key":"27_CR6","doi-asserted-by":"publisher","unstructured":"Li, J., et al.: Path: Performance-aware task scheduling for energy-harvesting nonvolatile processors. IEEE Trans. Very Large Scale Integr. (VLSI) Syst.26(9), 1671\u20131684 (2018). https:\/\/doi.org\/10.1109\/TVLSI.2018.2825605","DOI":"10.1109\/TVLSI.2018.2825605"},{"key":"27_CR7","doi-asserted-by":"publisher","first-page":"1096","DOI":"10.1007\/s11390-019-1962-4","volume":"34","author":"H Lin","year":"2019","unstructured":"Lin, H., Li, M.F., Jia, C.F., Liu, J.N., An, H.: Degree-of-node task scheduling of fine-grained parallel programs on heterogeneous systems. J. Comput. Sci. Technol. 34, 1096\u20131108 (2019)","journal-title":"J. Comput. Sci. Technol."},{"key":"27_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1007\/978-3-642-44973-4_35","volume-title":"Learning and Intelligent Optimization","author":"M Loth","year":"2013","unstructured":"Loth, M., Sebag, M., Hamadi, Y., Schoenauer, M., Schulte, C.: Hybridizing Constraint Programming and Monte-Carlo Tree Search: Application to the Job Shop Problem. In: Nicosia, G., Pardalos, P. (eds.) LION 2013. LNCS, vol. 7997, pp. 315\u2013320. Springer, Heidelberg (2013). https:\/\/doi.org\/10.1007\/978-3-642-44973-4_35"},{"key":"27_CR9","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.jpdc.2017.05.001","volume":"117","author":"AI Orhean","year":"2018","unstructured":"Orhean, A.I., Pop, F., Raicu, I.: New scheduling approach using reinforcement learning for heterogeneous distributed systems. J. Parallel Distrib. Comput. 117, 292\u2013302 (2018)","journal-title":"J. Parallel Distrib. Comput."},{"key":"27_CR10","doi-asserted-by":"publisher","unstructured":"Singh, J., Mangipudi, B., Betha, S., Auluck, N.: Restricted duplication based milp formulation for scheduling task graphs on unrelated parallel machines. In: 2012 Fifth International Symposium on Parallel Architectures, Algorithms and Programming, pp. 202\u2013209 (2012). https:\/\/doi.org\/10.1109\/PAAP.2012.37","DOI":"10.1109\/PAAP.2012.37"},{"key":"27_CR11","doi-asserted-by":"crossref","unstructured":"Suman, C., Kumar, G.: Analysis of process scheduling algorithm for multiprocessor system. In: 2018 7th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), pp. 564\u2013569. IEEE (2018)","DOI":"10.1109\/ICRITO.2018.8748657"},{"key":"27_CR12","unstructured":"Suter, F., Hunold, S.: Daggen: A synthetic task graph generator (2013). https:\/\/github.com\/frs69wq\/daggen"},{"key":"27_CR13","doi-asserted-by":"crossref","unstructured":"\u015awiechowski, M., Godlewski, K., Sawicki, B., Ma\u0144dziuk, J.: Monte carlo tree search: A review of recent modifications and applications. Artificial Intelligence Review, pp. 1\u201366 (2022)","DOI":"10.1007\/s10462-022-10228-y"},{"issue":"3","key":"27_CR14","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1016\/S0022-0000(75)80008-0","volume":"10","author":"JD Ullman","year":"1975","unstructured":"Ullman, J.D.: Np-complete scheduling problems. J. Comput. Syst. Sci. 10(3), 384\u2013393 (1975)","journal-title":"J. Comput. Syst. Sci."}],"container-title":["Lecture Notes in Computer Science","Advances in Swarm Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-36625-3_27","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T15:31:37Z","timestamp":1710343897000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-36625-3_27"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031366246","9783031366253"],"references-count":14,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-36625-3_27","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"8 July 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICSI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Swarm Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 July 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 July 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"swarm2023","order":10,"name":"conference_id","label":"Conference ID","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":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"170","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":"81","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":"48% - 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":"2.6","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":"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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}