{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T04:19:57Z","timestamp":1750306797814,"version":"3.41.0"},"reference-count":18,"publisher":"Association for Computing Machinery (ACM)","issue":"2","license":[{"start":{"date-parts":[[2014,2,1]],"date-time":"2014-02-01T00:00:00Z","timestamp":1391212800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Model. Comput. Simul."],"published-print":{"date-parts":[[2014,2]]},"abstract":"<jats:p>Analysts using simulation models often must assess a large number of alternatives in order to determine which are most effective. If effectiveness corresponds to the likelihood of yielding the best outcome, this becomes a multinomial selection problem. Unfortunately, existing procedures were developed primarily for evaluating small sets of alternatives, so parameters required to implement them may not be readily available or the sampling costs may be prohibitive when a large number of alternatives are present. We propose a truncated, sequential multinomial subset selection procedure that restricts the maximum subset size. Numerical comparisons show that our procedure can be much more efficient than the leading unrestricted procedure. Our procedure requires only one calculated parameter rather than four. We provide extensive tables for cases involving large numbers of alternatives.<\/jats:p>","DOI":"10.1145\/2567891","type":"journal-article","created":{"date-parts":[[2014,3,18]],"date-time":"2014-03-18T12:09:07Z","timestamp":1395144547000},"page":"1-22","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":4,"title":["A restricted multinomial hybrid selection procedure"],"prefix":"10.1145","volume":"24","author":[{"suffix":"Jr.","given":"H\u00e9lcio","family":"Vieira","sequence":"first","affiliation":[{"name":"Technological Institute of Aeronautics, SP, Brazil"}]},{"given":"Susan M.","family":"Sanchez","sequence":"additional","affiliation":[{"name":"Naval Postgraduate School, CA, USA"}]},{"given":"Paul J.","family":"Sanchez","sequence":"additional","affiliation":[{"name":"Naval Postgraduate School, CA, USA"}]},{"given":"Karl Heinz","family":"Kienitz","sequence":"additional","affiliation":[{"name":"Technological Institute of Aeronautics, SP, Brazil"}]},{"given":"Mischel Carmen Neyra","family":"Belderrain","sequence":"additional","affiliation":[{"name":"Technological Institute of Aeronautics, SP, Brazil"}]}],"member":"320","published-online":{"date-parts":[[2014,2]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1080\/01966324.1991.10737314"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177706362"},{"key":"e_1_2_1_3_1","doi-asserted-by":"crossref","unstructured":"R. E. Bechhofer and D. Goldsman. 1985. Truncation of the Bechhofer-Kiefer-Sobel sequential procedure for selecting the multinomial event which has the largest probability. Communications in Statistics\u2014Simulation and Computation B14 283--315.  R. E. Bechhofer and D. Goldsman. 1985. Truncation of the Bechhofer-Kiefer-Sobel sequential procedure for selecting the multinomial event which has the largest probability. Communications in Statistics\u2014Simulation and Computation B14 283--315.","DOI":"10.1080\/03610918508812441"},{"key":"e_1_2_1_4_1","doi-asserted-by":"crossref","unstructured":"R. E. Bechhofer and D. Goldsman. 1986. Truncation of the Bechhofer-Kiefer-Sobel sequential procedure for selecting the multinomial event which has the largest probability (ii): Extended tables and an improved procedure. Communications in Statistics\u2014Simulation and Computation 15 3 829--851.  R. E. Bechhofer and D. Goldsman. 1986. Truncation of the Bechhofer-Kiefer-Sobel sequential procedure for selecting the multinomial event which has the largest probability (ii): Extended tables and an improved procedure. Communications in Statistics\u2014Simulation and Computation 15 3 829--851.","DOI":"10.1080\/03610918608812545"},{"key":"e_1_2_1_5_1","unstructured":"R. E. Bechhofer J. Kiefer and M. Sobel. 1968. Sequential Identification and Ranking Procedures (with Special Reference to Koopman-Darmois Populations). University of Chicago Press Chicago.  R. E. Bechhofer J. Kiefer and M. Sobel. 1968. Sequential Identification and Ranking Procedures (with Special Reference to Koopman-Darmois Populations). University of Chicago Press Chicago."},{"key":"e_1_2_1_6_1","unstructured":"R. E. Bechhofer T. J. Santner and D. Goldsman. 1995. Design and Analysis of Experiments for Statistical Selection Screening and Multiple Comparisons. Wiley Series in Probability and Statistics. Wiley New York.  R. E. Bechhofer T. J. Santner and D. 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Sankhy\u00e3 B29, 1--17.","journal-title":"Sankhy\u00e3"},{"key":"e_1_2_1_11_1","unstructured":"S. S. Gupta and S. Panchapakesan. 1979. Multiple Decision Procedures\u2014Theory and Methodology of Selecting and Ranking Populations. John Wiley & Sons New York.  S. S. Gupta and S. Panchapakesan. 1979. Multiple Decision Procedures\u2014Theory and Methodology of Selecting and Ranking Populations. John Wiley & Sons New York."},{"key":"e_1_2_1_12_1","volume-title":"Multiple Comparisons: Theory and Methods","author":"Hsu J. C.","year":"1996","unstructured":"J. C. Hsu . 1996 . Multiple Comparisons: Theory and Methods . Chapman & Hall , London . J. C. Hsu. 1996. Multiple Comparisons: Theory and Methods. Chapman & Hall, London."},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177706363"},{"key":"e_1_2_1_14_1","first-page":"501","article-title":"Selecting the best system. In Handbooks in Operations Research and Management Science: Simulation, S. G. Henderson and B. L. 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