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Model. Comput. Simul."],"published-print":{"date-parts":[[2010,1]]},"abstract":"<jats:p>\n            Industrial Strength COMPASS (ISC) is a particular implementation of a general framework for optimizing the expected value of a performance measure of a stochastic simulation with respect to integer-ordered decision variables in a finite (but typically large) feasible region defined by linear-integer constraints. The framework consists of a global-search phase, followed by a local-search phase, and ending with a \u201cclean-up\u201d (selection of the best) phase. Each phase provides a probability 1 convergence guarantee as the simulation effort increases without bound: Convergence to a globally optimal solution in the global-search phase; convergence to a locally optimal solution in the local-search phase; and convergence to the best of a small number of good solutions in the clean-up phase. In practice, ISC stops short of such convergence by applying an improvement-based transition rule from the global phase to the local phase; a statistical test of convergence from the local phase to the clean-up phase; and a ranking-and-selection procedure to terminate the clean-up phase. Small-sample validity of the statistical test and ranking-and-selection procedure is proven for normally distributed data. ISC is compared to the commercial optimization via simulation package OptQuest on five test problems that range from 2 to 20 decision variables and on the order of 10\n            <jats:sup>4<\/jats:sup>\n            to 10\n            <jats:sup>20<\/jats:sup>\n            feasible solutions. These test cases represent response-surface models with known properties and realistic system simulation problems.\n          <\/jats:p>","DOI":"10.1145\/1667072.1667075","type":"journal-article","created":{"date-parts":[[2010,8,24]],"date-time":"2010-08-24T13:16:40Z","timestamp":1282655800000},"page":"1-29","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":125,"title":["Industrial strength COMPASS"],"prefix":"10.1145","volume":"20","author":[{"given":"Jie","family":"Xu","sequence":"first","affiliation":[{"name":"Northwestern University, Evanston, IL"}]},{"given":"Barry L.","family":"Nelson","sequence":"additional","affiliation":[{"name":"Northwestern University, Evanston, IL"}]},{"given":"JEFF L.","family":"Hong","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Water Bay, Hong Kong"}]}],"member":"320","published-online":{"date-parts":[[2010,2,8]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.45.5.748"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1287\/mnsc.41.12.1946"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/352222.352225"},{"key":"e_1_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1176249.1176252"},{"key":"e_1_2_2_5_1","volume-title":"Proceedings of the NSF Design, Service, and Manufacturing Grantees and Research Conference.","author":"Andrad\u00f3ttir S."},{"key":"e_1_2_2_6_1","unstructured":"Boesel J. 1999. Search and selection for large-scale stochastic optimization. Doctoral dissertation Department of IEMS Northwestern University Evanston IL.   Boesel J. 1999. Search and selection for large-scale stochastic optimization. Doctoral dissertation Department of IEMS Northwestern University Evanston IL."},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1080\/07408170304364"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.51.5.814.16751"},{"key":"e_1_2_2_9_1","unstructured":"Buzacott J. A. and Shantikumar. J. G. 1993. Stochastic Models of Manufacturing Systems. Prentice-Hall Englewood Cliffs NJ.  Buzacott J. A. and Shantikumar. J. G. 1993. Stochastic Models of Manufacturing Systems. Prentice-Hall Englewood Cliffs NJ."},{"key":"e_1_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008349927281"},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.49.5.732.10615"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1287\/ijoc.14.3.192.113"},{"key":"e_1_2_2_13_1","volume-title":"Proceedings of the Winter Simulation Conference. IEEE, 83--95","author":"Fu M. C."},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1137\/S1052623495290684"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01797280"},{"key":"e_1_2_2_16_1","unstructured":"Hong L. J. 2004. Discrete optimization via simulation: algorithms and error control. Doctoral dissertation Department of IEMS Northwestern University Evanston IL.   Hong L. J. 2004. Discrete optimization via simulation: algorithms and error control. Doctoral dissertation Department of IEMS Northwestern University Evanston IL."},{"key":"e_1_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.1050.0237"},{"key":"e_1_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/1276927.1276932"},{"key":"e_1_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1080\/07408170600838415"},{"key":"e_1_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TEVC.2005.846356"},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/1060576.1060579"},{"key":"e_1_2_2_22_1","unstructured":"Law A. M. and Kelton W. D. 2000. Simulation Modeling and Analysis 3rd Ed. McGraw-Hill New York.   Law A. M. and Kelton W. D. 2000. Simulation Modeling and Analysis 3rd Ed. McGraw-Hill New York."},{"key":"e_1_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2004.11.006"},{"key":"e_1_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1287\/opre.49.3.334.11210"},{"key":"e_1_2_2_25_1","unstructured":"Miller B. L. and Shaw M. J. 1995. 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