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Model. Comput. Simul."],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>\n            We consider a continuous-valued simulation optimization (SO) problem, where a simulator is built to optimize an expected performance measure of a real-world system while parameters of the simulator are estimated from streaming data collected periodically from the system. At each period, a new batch of data is combined with the cumulative data and the parameters are re-estimated with higher precision. The system requires the decision variable to be selected in all periods. Therefore, it is sensible for the decision-maker to update the decision variable at each period by solving a more precise SO problem with the updated parameter estimate to reduce the performance loss with respect to the target system. We define this decision-making process as the multi-period SO problem and introduce a multi-period stochastic approximation (SA) framework that generates a sequence of solutions. Two algorithms are proposed: Re-start SA (\n            <jats:bold>ReSA<\/jats:bold>\n            ) reinitializes the stepsize sequence in each period, whereas Warm-start SA (\n            <jats:bold>WaSA<\/jats:bold>\n            ) carefully tunes the stepsizes, taking both fewer and shorter gradient-descent steps in later periods as parameter estimates become increasingly more precise. We show that under suitable strong convexity and regularity conditions,\n            <jats:bold>ReSA<\/jats:bold>\n            and\n            <jats:bold>WaSA<\/jats:bold>\n            achieve the best possible convergence rate in expected sub-optimality either when an unbiased or a simultaneous perturbation gradient estimator is employed, while\n            <jats:bold>WaSA<\/jats:bold>\n            accrues significantly lower computational cost as the number of periods increases. In addition, we present the\n            <jats:bold>regularized<\/jats:bold>\n            <jats:bold>ReSA<\/jats:bold>\n            , which obviates the need to know the strong convexity constant and achieves the same convergence rate at the expense of additional computation.\n          <\/jats:p>","DOI":"10.1145\/3617595","type":"journal-article","created":{"date-parts":[[2023,8,29]],"date-time":"2023-08-29T11:24:03Z","timestamp":1693308243000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["Stochastic Approximation for Multi-period Simulation Optimization with Streaming Input Data"],"prefix":"10.1145","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3160-2323","authenticated-orcid":false,"given":"Linyun","family":"He","sequence":"first","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5869-9561","authenticated-orcid":false,"given":"Uday V.","family":"Shanbhag","sequence":"additional","affiliation":[{"name":"The Pennsylvania State University, University Park, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5171-0614","authenticated-orcid":false,"given":"Eunhye","family":"Song","sequence":"additional","affiliation":[{"name":"Georgia Institute of Technology, Atlanta, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,4,8]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10589-020-00193-z"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1287\/opre.44.2.327"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jebo.2005.08.006"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1080\/00207720701792131"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1147\/rd.165.0504"},{"key":"e_1_3_2_7_2","first-page":"463","volume-title":"Proceedings of the Winter Simulation Conference","author":"Corlu Canan G.","year":"2013","unstructured":"Canan G. 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