{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,24]],"date-time":"2026-01-24T19:32:09Z","timestamp":1769283129615,"version":"3.49.0"},"reference-count":40,"publisher":"Institute for Operations Research and the Management Sciences (INFORMS)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Operations Research"],"published-print":{"date-parts":[[2024,3]]},"abstract":"<jats:p> In many applications, input data are collected frequently to update the simulation model of the system, whereas simulation is run to compare different designs\/strategies to identify the best one with a high confidence. In \u201cData-Driven Ranking and Selection Under Input Uncertainty,\u201d Wu, Wang, and Zhou consider such a simulation-based ranking and selection (R&amp;S) problem, in which the input distribution is estimated and updated with input data arriving in batches over time. Unlike most existing works of R&amp;S that conduct simulation under a fixed distribution, in this data-driven setting, simulation outputs are generated under different input distributions over time. A moving average estimator is introduced to aggregate simulation outputs generated under heterogenous distributions. Then, two sequential elimination procedures are devised by establishing exact and asymptotic confidence bands for the estimator. The efficiency of the procedures can be further boosted by incorporating the \u201cindifference zone\u201d idea and optimizing the \u201cdrop rate\u201d parameter of the moving average estimator. <\/jats:p>","DOI":"10.1287\/opre.2022.2375","type":"journal-article","created":{"date-parts":[[2022,10,18]],"date-time":"2022-10-18T12:32:44Z","timestamp":1666096364000},"page":"781-795","source":"Crossref","is-referenced-by-count":15,"title":["Data-Driven Ranking and Selection Under Input Uncertainty"],"prefix":"10.1287","volume":"72","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3911-0955","authenticated-orcid":false,"given":"Di","family":"Wu","sequence":"first","affiliation":[{"name":"Amazon Web Services, Seattle, Washington 98109;"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9863-273X","authenticated-orcid":false,"given":"Yuhao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5399-6508","authenticated-orcid":false,"given":"Enlu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332"}]}],"member":"109","reference":[{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.2013.0548"},{"key":"B3","doi-asserted-by":"publisher","DOI":"10.1080\/07408170500539105"},{"key":"B4","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177728845"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.1016\/0378-3758(92)90106-3"},{"key":"B6","doi-asserted-by":"publisher","DOI":"10.1080\/00949659708811809"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1287\/opre.49.5.744.10606"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.1016\/j.orp.2020.100162"},{"key":"B13","first-page":"1079","volume":"7","author":"Even-Dar E","year":"2006","journal-title":"J. Machine Learn. Res."},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2016.1530"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.2018.3213"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1145\/3129130"},{"key":"B19","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2014.1282"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4939-1384-8"},{"key":"B21","first-page":"3212","author":"Gabillon V","year":"2012","journal-title":"Adv. Neural Inform. Processing Systems"},{"key":"B22","doi-asserted-by":"publisher","DOI":"10.1016\/j.automatica.2017.03.019"},{"key":"B24","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.14.1.2.7710"},{"key":"B25","doi-asserted-by":"publisher","DOI":"10.1007\/BF01797280"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1002\/nav.20155"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.1287\/opre.1080.0531"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.1080\/07408170590948486"},{"key":"B29","doi-asserted-by":"publisher","DOI":"10.1080\/07408170600838415"},{"issue":"1","key":"B32","first-page":"1","volume":"17","author":"Kaufmann E","year":"2016","journal-title":"J. Machine Learn. Res."},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1145\/502109.502111"},{"key":"B34","doi-asserted-by":"publisher","DOI":"10.1287\/opre.1060.0281"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2021.2168"},{"key":"B36","doi-asserted-by":"publisher","DOI":"10.1016\/j.orl.2017.04.003"},{"key":"B37","doi-asserted-by":"publisher","DOI":"10.1057\/jos.2015.2"},{"key":"B42","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2015.1413"},{"key":"B43","first-page":"623","volume":"5","author":"Mannor S","year":"2004","journal-title":"J. Machine Learn. Res."},{"key":"B44","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2004.12.010"},{"key":"B46","doi-asserted-by":"publisher","DOI":"10.1080\/0740817X.2014.980869"},{"issue":"2","key":"B47","first-page":"562","volume":"67","author":"Song E","year":"2019","journal-title":"Oper. Res."},{"key":"B49","volume-title":"Asymptotic Statistics","volume":"3","author":"Van der Vaart AW","year":"2000"},{"key":"B52","doi-asserted-by":"publisher","DOI":"10.1109\/TAC.2018.2791425"},{"key":"B53","doi-asserted-by":"publisher","DOI":"10.1080\/24725854.2019.1659524"},{"key":"B54","doi-asserted-by":"publisher","DOI":"10.1287\/opre.2014.1316"},{"key":"B55","doi-asserted-by":"publisher","DOI":"10.1145\/2990190"},{"key":"B58","doi-asserted-by":"publisher","DOI":"10.1145\/3329117"},{"key":"B59","doi-asserted-by":"publisher","DOI":"10.1080\/07408170304413"},{"key":"B60","doi-asserted-by":"publisher","DOI":"10.1080\/07408170490500708"}],"container-title":["Operations Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/pubsonline.informs.org\/doi\/pdf\/10.1287\/opre.2022.2375","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,21]],"date-time":"2024-03-21T08:00:34Z","timestamp":1711008034000},"score":1,"resource":{"primary":{"URL":"https:\/\/pubsonline.informs.org\/doi\/10.1287\/opre.2022.2375"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3]]},"references-count":40,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,3]]}},"alternative-id":["10.1287\/opre.2022.2375"],"URL":"https:\/\/doi.org\/10.1287\/opre.2022.2375","relation":{},"ISSN":["0030-364X","1526-5463"],"issn-type":[{"value":"0030-364X","type":"print"},{"value":"1526-5463","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3]]}}}