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Simul."],"published-print":{"date-parts":[[2013,7]]},"abstract":"<jats:p>We consider simulation-optimization (SO) models where the decision variables are integer ordered and the objective function is defined implicitly via a simulation oracle, which for any feasible solution can be called to compute a point estimate of the objective-function value. We develop R-SPLINE---a Retrospective-search algorithm that alternates between a continuous Search using Piecewise-Linear Interpolation and a discrete Neighborhood Enumeration, to asymptotically identify a local minimum. R-SPLINE appears to be among the first few gradient-based search algorithms tailored for solving integer-ordered local SO problems. In addition to proving the almost-sure convergence of R-SPLINE\u2019s iterates to the set of local minima, we demonstrate that the probability of R-SPLINE returning a solution outside the set of true local minima decays exponentially in a certain precise sense. R-SPLINE, with no parameter tuning, compares favorably with popular existing algorithms.<\/jats:p>","DOI":"10.1145\/2499913.2499916","type":"journal-article","created":{"date-parts":[[2013,8,1]],"date-time":"2013-08-01T15:14:00Z","timestamp":1375370040000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":47,"title":["Integer-Ordered Simulation Optimization using R-SPLINE"],"prefix":"10.1145","volume":"23","author":[{"given":"Honggang","family":"Wang","sequence":"first","affiliation":[{"name":"Rutgers University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raghu","family":"Pasupathy","sequence":"additional","affiliation":[{"name":"Virginia Tech and IBM T. J. Watson Research"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bruce W.","family":"Schmeiser","sequence":"additional","affiliation":[{"name":"Purdue University"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2013,7]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0377-2217(00)00190-9"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.41.12.1946"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1137\/0806027"},{"key":"e_1_2_1_4_1","volume-title":"Handbooks in Operations Research and Management Science","author":"Andrad\u00f3ttir S.","unstructured":"Andrad\u00f3ttir , S. 2006. An overview of simulation optimization via random search . In Simulation, S. G. Henderson and B. L. Nelson Eds., Handbooks in Operations Research and Management Science , Elsevier , 617--631. Andrad\u00f3ttir, S. 2006. 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