eprintid: 5002 rev_number: 22 eprint_status: archive userid: 351 dir: disk0/00/00/50/02 datestamp: 2016-01-15 02:08:11 lastmod: 2021-08-27 17:15:51 status_changed: 2016-01-15 02:08:11 type: monograph metadata_visibility: show item_issues_count: 2 creators_name: Futschik, A. creators_name: Pflug, G.C. creators_id: 1361 creators_orcid: 0000-0001-8215-3550 title: Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization ispublished: pub internal_subjects: iis_met divisions: prog_opt abstract: Approximate solutions for discrete stochastic optimization problems are often obtained via simulation. It is reasonable to complement these solutions by confidence regions for the argmin-set. We address the question, how a certain total number of random draws should be distributed among the set of alternatives. We propose a one-step allocation rule which turns out to be asymptotically optimal in the case of normal errors for two goals: To minimize the costs caused by using only an approximate solution and to minimize the expected size of the confidence sets. date: 1996-03 date_type: published publisher: WP-96-023 iiasapubid: WP-96-023 price: 10 creators_browse_id: 229 full_text_status: public monograph_type: working_paper place_of_pub: IIASA, Laxenburg, Austria pages: 18 coversheets_dirty: FALSE fp7_type: info:eu-repo/semantics/book citation: Futschik, A. & Pflug, G.C. ORCID: https://orcid.org/0000-0001-8215-3550 (1996). Asymptotically Optimal Allocation of Simulation Experiments in Discrete Stochastic Optimization. IIASA Working Paper. IIASA, Laxenburg, Austria: WP-96-023 document_url: https://pure.iiasa.ac.at/id/eprint/5002/1/WP-96-023.pdf