eprintid: 13582 rev_number: 10 eprint_status: archive userid: 353 dir: disk0/00/01/35/82 datestamp: 2016-08-04 11:33:12 lastmod: 2021-08-27 17:27:33 status_changed: 2016-08-04 11:33:12 type: article metadata_visibility: show item_issues_count: 1 creators_name: Futschik, A. creators_name: Pflug, G. creators_id: 1361 creators_orcid: 0000-0001-8215-3550 title: Optimal allocation of simulation experiments in discrete stochastic optimization and approximative algorithms ispublished: pub keywords: Discrete stochastic optimization; Simulation; Sampling strategy; Large deviations 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. Two goals are considered: the minimization of the costs caused by using a statistical estimate of the true argmin, and the minimization of the expected size of the confidence sets. We show that an asymptotically optimal sampling strategy in the case of normal errors can be obtained by solving a convex optimization problem. To reduce the computational effort we propose a regularization that leads to a simple one-step allocation rule. date: 1997-09-01 date_type: published publisher: Elsevier id_number: 10.1016/S0377-2217(96)00396-7 creators_browse_id: 229 full_text_status: none publication: European Journal of Operational Research volume: 101 number: 2 pagerange: 245-260 refereed: TRUE issn: 0377-2217 coversheets_dirty: FALSE fp7_project: no fp7_type: info:eu-repo/semantics/article citation: Futschik, A. & Pflug, G. ORCID: https://orcid.org/0000-0001-8215-3550 (1997). Optimal allocation of simulation experiments in discrete stochastic optimization and approximative algorithms. European Journal of Operational Research 101 (2) 245-260. 10.1016/S0377-2217(96)00396-7 .