{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T05:38:25Z","timestamp":1742967505564,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031216855"},{"type":"electronic","value":"9783031216862"}],"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_23","type":"book-chapter","created":{"date-parts":[[2022,11,18]],"date-time":"2022-11-18T08:30:15Z","timestamp":1668760215000},"page":"325-339","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Novel Multi-objective Decomposition Formulation for\u00a0Per-Instance Configuration"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5622-392X","authenticated-orcid":false,"given":"Lucas Marcondes","family":"Pavelski","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2791-174X","authenticated-orcid":false,"given":"Myriam Regattieri","family":"Delgado","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4372-5162","authenticated-orcid":false,"given":"Marie-\u00c9l\u00e9onore","family":"Kessaci","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"key":"23_CR1","doi-asserted-by":"publisher","unstructured":"Baker, K.R., Trietsch, D.: Appendix A: practical processing time distributions. In: Principles of Sequencing and Scheduling, pp. 445\u2013458. Wiley (2009). https:\/\/doi.org\/10.1002\/9780470451793.app1","DOI":"10.1002\/9780470451793.app1"},{"key":"23_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1007\/978-3-319-50349-3_3","volume-title":"Learning and Intelligent Optimization","author":"A Blot","year":"2016","unstructured":"Blot, A., Hoos, H.H., Jourdan, L., Kessaci-Marmion, M.\u00c9., Trautmann, H.: MO-ParamILS: a multi-objective automatic algorithm configuration framework. In: Festa, P., Sellmann, M., Vanschoren, J. (eds.) LION 2016. LNCS, vol. 10079, pp. 32\u201347. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-50349-3_3"},{"key":"23_CR3","doi-asserted-by":"publisher","unstructured":"Dr\u00e9o, J.: Using performance fronts for parameter setting of stochastic metaheuristics. In: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference - GECCO 2009, p. 2197. ACM Press, Montreal (2009). https:\/\/doi.org\/10.1145\/1570256.1570301","DOI":"10.1145\/1570256.1570301"},{"key":"23_CR4","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1016\/j.cor.2016.12.021","volume":"81","author":"J Dubois-Lacoste","year":"2017","unstructured":"Dubois-Lacoste, J., Pagnozzi, F., St\u00fctzle, T.: An iterated greedy algorithm with optimization of partial solutions for the makespan permutation flowshop problem. Comput. Oper. Res. 81, 160\u2013166 (2017). https:\/\/doi.org\/10.1016\/j.cor.2016.12.021","journal-title":"Comput. Oper. Res."},{"issue":"1","key":"23_CR5","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1162\/EVCO_a_00163","volume":"25","author":"AS Dymond","year":"2017","unstructured":"Dymond, A.S., Kok, S., Heyns, P.S.: MOTA: a many-objective tuning algorithm specialized for tuning under multiple objective function evaluation budgets. Evol. Comput. 25(1), 113\u2013141 (2017). https:\/\/doi.org\/10.1162\/EVCO_a_00163","journal-title":"Evol. Comput."},{"key":"23_CR6","doi-asserted-by":"publisher","unstructured":"Fernandes, L.H.d.S., Lorena, A.C., Smith-Miles, K.: Towards understanding clustering problems and algorithms: an instance space analysis. Algorithms 14(3) (2021). https:\/\/doi.org\/10.3390\/a14030095","DOI":"10.3390\/a14030095"},{"issue":"1","key":"23_CR7","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1162\/evco_a_00242","volume":"27","author":"P Kerschke","year":"2019","unstructured":"Kerschke, P., Hoos, H.H., Neumann, F., Trautmann, H.: Automated algorithm selection: survey and perspectives. Evol. Comput. 27(1), 3\u201345 (2019)","journal-title":"Evol. Comput."},{"key":"23_CR8","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1016\/j.orp.2016.09.002","volume":"3","author":"M L\u00f3pez-Ib\u00e1\u00f1ez","year":"2016","unstructured":"L\u00f3pez-Ib\u00e1\u00f1ez, M., Dubois-Lacoste, J., C\u00e1ceres, L.P., St\u00fctzle, T., Birattari, M.: The irace package: iterated racing for automatic algorithm configuration. Oper. Res. Perspect. 3, 43\u201358 (2016). https:\/\/doi.org\/10.1016\/j.orp.2016.09.002","journal-title":"Oper. Res. Perspect."},{"issue":"1","key":"23_CR9","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/0305-0483(83)90088-9","volume":"11","author":"M Nawaz","year":"1983","unstructured":"Nawaz, M., Enscore, E.E., Ham, I.: A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega 11(1), 91\u201395 (1983). https:\/\/doi.org\/10.1016\/0305-0483(83)90088-9","journal-title":"Omega"},{"issue":"6","key":"23_CR10","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1016\/0305-0483(89)90059-5","volume":"17","author":"IH Osman","year":"1989","unstructured":"Osman, I.H., Potts, C.: Simulated annealing for permutation flow-shop scheduling. Omega 17(6), 551\u2013557 (1989)","journal-title":"Omega"},{"key":"23_CR11","doi-asserted-by":"publisher","unstructured":"Pavelski, L.M., Delgado, M., Kessaci, M.\u00c9., Freitas, A.A.: Stochastic local search and parameters recommendation: a case study on flowshop problems. Int. Trans. Oper. Res. itor.12922 (2020). https:\/\/doi.org\/10.1111\/itor.12922","DOI":"10.1111\/itor.12922"},{"key":"23_CR12","series-title":"Studies in Computational Intelligence","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/978-3-642-23229-9_8","volume-title":"Recent Advances in Intelligent Engineering Systems","author":"E Pitzer","year":"2012","unstructured":"Pitzer, E., Affenzeller, M.: A Comprehensive Survey on Fitness Landscape Analysis. In: Fodor, J., Klempous, R., Su\u00e1rez Araujo, C.P. (eds.) Recent Advances in Intelligent Engineering Systems. Studies in Computational Intelligence, vol. 378, pp. 161\u2013186. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-23229-9_8"},{"key":"23_CR13","doi-asserted-by":"publisher","unstructured":"Prager, R.P., Trautmann, H., Wang, H., B\u00e4ck, T.H.W., Kerschke, P.: Per-instance configuration of the modularized CMA-ES by means of classifier chains and exploratory landscape analysis. In: 2020 IEEE Symposium Series on Computational Intelligence (SSCI), pp. 996\u20131003 (2020). https:\/\/doi.org\/10.1109\/SSCI47803.2020.9308510","DOI":"10.1109\/SSCI47803.2020.9308510"},{"key":"23_CR14","doi-asserted-by":"publisher","first-page":"473","DOI":"10.1023\/A:1018983524911","volume":"86","author":"C Reeves","year":"1999","unstructured":"Reeves, C.: Landscapes, operators and heuristic search. Ann. Oper. Res. 86, 473\u2013490 (1999). https:\/\/doi.org\/10.1023\/A:1018983524911","journal-title":"Ann. Oper. Res."},{"key":"23_CR15","doi-asserted-by":"publisher","unstructured":"Rice, J.R.: The Algorithm Selection Problem. In: Rubinoff, M., Yovits, M.C. (eds.) Advances in Computers. Advances in Computers, vol. 15, pp. 65\u2013118. Elsevier, Washington (1976). https:\/\/doi.org\/10.1016\/S0065-2458(08)60520-3","DOI":"10.1016\/S0065-2458(08)60520-3"},{"issue":"3","key":"23_CR16","doi-asserted-by":"publisher","first-page":"2033","DOI":"10.1016\/j.ejor.2005.12.009","volume":"177","author":"R Ruiz","year":"2007","unstructured":"Ruiz, R., St\u00fctzle, T.: A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. Eur. J. Oper. Res. 177(3), 2033\u20132049 (2007). https:\/\/doi.org\/10.1016\/j.ejor.2005.12.009","journal-title":"Eur. J. Oper. Res."},{"key":"23_CR17","doi-asserted-by":"publisher","unstructured":"Schede, E., et al.: A survey of methods for automated algorithm configuration (2022). https:\/\/doi.org\/10.48550\/ARXIV.2202.01651","DOI":"10.48550\/ARXIV.2202.01651"},{"key":"23_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"542","DOI":"10.1007\/978-3-642-12239-2_56","volume-title":"Applications of Evolutionary Computation","author":"SK Smit","year":"2010","unstructured":"Smit, S.K., Eiben, A.E.: Parameter tuning of evolutionary algorithms: generalist vs. specialist. In: Di Chio, C., et al. (eds.) EvoApplications 2010. LNCS, vol. 6024, pp. 542\u2013551. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-642-12239-2_56"},{"key":"23_CR19","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.cor.2013.11.015","volume":"45","author":"K Smith-Miles","year":"2014","unstructured":"Smith-Miles, K., Baatar, D., Wreford, B., Lewis, R.: Towards objective measures of algorithm performance across instance space. Comput. Oper. Res. 45, 12\u201324 (2014). https:\/\/doi.org\/10.1016\/j.cor.2013.11.015","journal-title":"Comput. Oper. Res."},{"key":"23_CR20","unstructured":"St\u00fctzle, T.: Applying iterated local search to the permutation flow shop problem. Technical report, FG Intellektik, TU Darmstadt, Darmstadt, Germany (1998)"},{"key":"23_CR21","doi-asserted-by":"crossref","unstructured":"Xu, L., Hoos, H., Leyton-Brown, K.: Hydra: automatically configuring algorithms for portfolio-based selection. In: Twenty-Fourth AAAI Conference on Artificial Intelligence (2010)","DOI":"10.1609\/aaai.v24i1.7565"},{"issue":"6","key":"23_CR22","doi-asserted-by":"publisher","first-page":"712","DOI":"10.1109\/TEVC.2007.892759","volume":"11","author":"Q Zhang","year":"2007","unstructured":"Zhang, Q., Li, H.: MOEA\/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712\u2013731 (2007). https:\/\/doi.org\/10.1109\/TEVC.2007.892759","journal-title":"IEEE Trans. Evol. Comput."}],"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_23","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T17:11:29Z","timestamp":1709831489000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21686-2_23"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031216855","9783031216862"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21686-2_23","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"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)"}}]}}