{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,24]],"date-time":"2025-09-24T10:18:42Z","timestamp":1758709122295,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031332708"},{"type":"electronic","value":"9783031332715"}],"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\/978-3-031-33271-5_8","type":"book-chapter","created":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T17:03:04Z","timestamp":1684774984000},"page":"114-123","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Online Learning for\u00a0Scheduling MIP Heuristics"],"prefix":"10.1007","author":[{"given":"Antonia","family":"Chmiela","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ambros","family":"Gleixner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pawel","family":"Lichocki","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastian","family":"Pokutta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,5,23]]},"reference":[{"issue":"1","key":"8_CR1","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.disopt.2006.10.006","volume":"4","author":"T Achterberg","year":"2007","unstructured":"Achterberg, T.: Conflict analysis in mixed integer programming. Discret. Optim. 4(1), 4\u201320 (2007)","journal-title":"Discret. Optim."},{"key":"8_CR2","unstructured":"Balcan, M.F., Dick, T., Sandholm, T., Vitercik, E.: Learning to branch. In: International Conference on Machine Learning, pp. 344\u2013353. PMLR (2018)"},{"key":"8_CR3","unstructured":"Baltean-Lugojan, R., Bonami, P., Misener, R., Tramontani, A.: Scoring positive semidefinite cutting planes for quadratic optimization via trained neural networks (2019). https:\/\/optimization-online.org\/?p=17362"},{"issue":"6","key":"8_CR4","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1016\/j.orl.2013.08.007","volume":"41","author":"T Berthold","year":"2013","unstructured":"Berthold, T.: Measuring the impact of primal heuristics. Oper. Res. Lett. 41(6), 611\u2013614 (2013)","journal-title":"Oper. Res. Lett."},{"key":"8_CR5","doi-asserted-by":"crossref","unstructured":"Berthold, T.: Primal MINLP heuristics in a nutshell. In: International Conference on Operations Research (2013)","DOI":"10.1007\/978-3-319-07001-8_4"},{"key":"8_CR6","doi-asserted-by":"publisher","first-page":"189","DOI":"10.1007\/s10898-017-0600-3","volume":"70","author":"T Berthold","year":"2018","unstructured":"Berthold, T.: A computational study of primal heuristics inside an MI(NL)P solver. J. Glob. Optim. 70, 189\u2013206 (2018)","journal-title":"J. Glob. Optim."},{"key":"8_CR7","first-page":"1","volume":"33","author":"T Berthold","year":"2017","unstructured":"Berthold, T., Hendel, G., Koch, T.: From feasibility to improvement to proof: three phases of solving mixed-integer programs. Optim. Methods Softw. 33, 1\u201319 (2017)","journal-title":"Optim. Methods Softw."},{"key":"8_CR8","unstructured":"Bestuzheva, K., et al.: The SCIP Optimization Suite 8.0. ZIB-Report 21-41, Zuse Institute Berlin (2021). https:\/\/nbn-resolving.de\/urn:nbn:de:0297-zib-85309"},{"issue":"1","key":"8_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000024","volume":"5","author":"S Bubeck","year":"2012","unstructured":"Bubeck, S., Nicol\u00f3, C.B.: Regret analysis of stochastic and nonstochastic multi-armed bandit problems. Found. Trends Mach. Learn. 5(1), 1\u2013122 (2012)","journal-title":"Found. Trends Mach. Learn."},{"key":"8_CR10","unstructured":"Chmiela, A., Khalil, E., Gleixner, A., Lodi, A., Pokutta, S.: Learning to schedule heuristics in branch and bound. In: Advances in Neural Information Processing Systems, vol. 34 (2021)"},{"key":"8_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1007\/978-3-030-64583-0_61","volume-title":"Machine Learning, Optimization, and Data Science","author":"G Iommazzo","year":"2020","unstructured":"Iommazzo, G., D\u2019Ambrosio, C., Frangioni, A., Liberti, L.: A learning-based mathematical programming formulation for the automatic configuration of optimization solvers. In: Nicosia, G., et al. (eds.) LOD 2020. LNCS, vol. 12565, pp. 700\u2013712. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-64583-0_61"},{"key":"8_CR12","doi-asserted-by":"crossref","unstructured":"Etheve, M., Al\u00e9s, Z., Bissuel, C., Juan, O., Kedad-Sidhoum, S.: Reinforcement learning for variable selection in a branch and bound algorithm. arXiv:2005.10026 (2020)","DOI":"10.1007\/978-3-030-58942-4_12"},{"issue":"3","key":"8_CR13","doi-asserted-by":"publisher","first-page":"443","DOI":"10.1007\/s12532-020-00194-3","volume":"13","author":"A Gleixner","year":"2021","unstructured":"Gleixner, A., et al.: MIPLIB 2017: data-driven compilation of the 6th mixed-integer programming library. Math. Program. Comput. 13(3), 443\u2013490 (2021)","journal-title":"Math. Program. Comput."},{"key":"8_CR14","unstructured":"He, H., Daume III, H., Eisner, J.M.: Learning to search in branch and bound algorithms. In: Advances in Neural Information Processing Systems, vol. 27, pp. 3293\u20133301 (2014)"},{"issue":"2","key":"8_CR15","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1007\/s12532-021-00209-7","volume":"14","author":"G Hendel","year":"2022","unstructured":"Hendel, G.: Adaptive large neighborhood search for mixed integer programming. Math. Program. Comput. 14(2), 185\u2013221 (2022)","journal-title":"Math. Program. Comput."},{"key":"8_CR16","doi-asserted-by":"crossref","unstructured":"Hendel, G., Miltenberger, M., Witzig, J.: Adaptive algorithmic behavior for solving mixed integer programs using bandit algorithms. In: International Conference on Operations Research (2018)","DOI":"10.1007\/978-3-030-18500-8_64"},{"key":"8_CR17","doi-asserted-by":"crossref","unstructured":"Huang, L., et al.: Improving primal heuristics for mixed integer programming problems based on problem reduction: a learning-based approach. arXiv:2209.13217 (2022)","DOI":"10.1109\/ICARCV57592.2022.10004252"},{"key":"8_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2021.108353","volume":"123","author":"Z Huang","year":"2022","unstructured":"Huang, Z., et al.: Learning to select cuts for efficient mixed-integer programming. Pattern Recognit. 123, 108353 (2022)","journal-title":"Pattern Recognit."},{"key":"8_CR19","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1613\/jair.2861","volume":"36","author":"F Hutter","year":"2009","unstructured":"Hutter, F., Hoos, H., Leyton-Brown, K., St\u00fctzle, T.: Paramils: an automatic algorithm configuration framework. J. Artif. Intell. Res. (JAIR) 36, 267\u2013306 (2009)","journal-title":"J. Artif. Intell. Res. (JAIR)"},{"key":"8_CR20","doi-asserted-by":"crossref","unstructured":"Hutter, F., Hoos, H.H., Leyton-Brown, K.: Sequential model-based optimization for general algorithm configuration. In: Learning and Intelligent Optimization, pp. 507\u2013523 (2011)","DOI":"10.1007\/978-3-642-25566-3_40"},{"key":"8_CR21","doi-asserted-by":"crossref","unstructured":"Khalil, E.B., Bodic, P.L., Song, L., Nemhauser, G., Dilkina, B.: Learning to branch in mixed integer programming. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (2016)","DOI":"10.1609\/aaai.v30i1.10080"},{"key":"8_CR22","doi-asserted-by":"crossref","unstructured":"Kruber, M., L\u00fcbbecke, M., Parmentier, A.: Learning when to use a decomposition. In: International Conference on AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, pp. 202\u2013210 (2017)","DOI":"10.1007\/978-3-319-59776-8_16"},{"issue":"3","key":"8_CR23","doi-asserted-by":"publisher","first-page":"497","DOI":"10.2307\/1910129","volume":"28","author":"AH Land","year":"1960","unstructured":"Land, A.H., Doig, A.G.: An automatic method of solving discrete programming problems. Econometrica 28(3), 497\u2013520 (1960)","journal-title":"Econometrica"},{"key":"8_CR24","doi-asserted-by":"publisher","DOI":"10.1017\/9781108571401","volume-title":"Bandit Algorithms","author":"T Lattimore","year":"2020","unstructured":"Lattimore, T., Szepesv\u00e1ri, C.: Bandit Algorithms. Cambridge University Press, Cambridge (2020)"},{"key":"8_CR25","first-page":"1","volume":"10","author":"A Lodi","year":"2013","unstructured":"Lodi, A., Tramontani, A.: Performance variability in mixed-integer programming. Tutor. Oper. Res. 10, 1\u201312 (2013)","journal-title":"Tutor. Oper. Res."},{"issue":"2","key":"8_CR26","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1007\/s11750-017-0451-6","volume":"25","author":"A Lodi","year":"2017","unstructured":"Lodi, A., Zarpellon, G.: On learning and branching: a survey. TOP 25(2), 207\u2013236 (2017). https:\/\/doi.org\/10.1007\/s11750-017-0451-6","journal-title":"TOP"},{"key":"8_CR27","unstructured":"Nair, V., et al.: Solving mixed integer programs using neural networks. arXiv preprint: arXiv:2012.13349 (2020)"},{"key":"8_CR28","unstructured":"Paulus, M.B., Zarpellon, G., Krause, A., Charlin, L., Maddison, C.: Learning to cut by looking ahead: cutting plane selection via imitation learning. In: Proceedings of the 39th International Conference on Machine Learning, vol. 162, pp. 17584\u201317600 (2022)"},{"key":"8_CR29","unstructured":"Scavuzzo, L., et al.: Learning to branch with tree MDPs. arXiv:2205.11107 (2022)"},{"key":"8_CR30","unstructured":"Tang, Y., Agrawal, S., Faenza, Y.: Reinforcement learning for integer programming: learning to cut. In: Proceedings of the 37th International Conference on Machine Learning, vol. 119, pp. 9367\u20139376 (2020)"},{"key":"8_CR31","doi-asserted-by":"crossref","unstructured":"Turner, M., Koch, T., Serrano, F., Winkler, M.: Adaptive cut selection in mixed-integer linear programming. arXiv:2202.10962 (2022)","DOI":"10.5802\/ojmo.25"},{"key":"8_CR32","doi-asserted-by":"crossref","unstructured":"Yilmaz, K., Yorke-Smith, N.: A study of learning search approximation in mixed integer branch and bound: node selection in SCIP. AI 2, 150\u2013178 (2021)","DOI":"10.3390\/ai2020010"}],"container-title":["Lecture Notes in Computer Science","Integration of Constraint Programming, Artificial Intelligence, and Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-33271-5_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T21:39:05Z","timestamp":1729460345000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-33271-5_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031332708","9783031332715"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-33271-5_8","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":"23 May 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CPAIOR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Integration of Constraint Programming, Artificial Intelligence, and Operations Research","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Nice","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"France","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":"29 May 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 June 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cpaior2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/sites.google.com\/view\/cpaior2023","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"71","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":"26","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":"6","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":"37% - 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)"}}]}}