Mathematical programming and the optimization of computer simulations
Mathematical programming techniques can be combined with response surface experimental design methods to optimize simulated systems. A computer simulation model has controllable input variables xi, i=1,…, n and yields responses ηj, j=1,…, m. A simulation trial at a particular set of values xik, i=1,...
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| creator | Biles, William E. Swain, James J. |
| description | Mathematical programming techniques can be combined with response surface experimental design methods to optimize simulated systems. A computer simulation model has controllable input variables xi, i=1,…, n and yields responses ηj, j=1,…, m. A simulation trial at a particular set of values xik, i=1,…n produces an estimate yik for the system response ηj. This paper describes several formulations of the so-called “simulation/optimization” problem, including constrained optimization and multiple-objective optimization. It also describes several procedures for obtaining a solution to this problem, including a direct search technique, a first-order response surface method, and a second-order response surface approach. Each of these techniques combines simulation, response surface methodology, and mathematical programming. |
| doi_str_mv | 10.1007/BFb0120864 |
| format | Book Chapter |
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S.</contributor><contributor>Avriel, M.</contributor><creatorcontrib>Biles, William E.</creatorcontrib><creatorcontrib>Swain, James J.</creatorcontrib><title>Mathematical programming and the optimization of computer simulations</title><title>Engineering Optimization</title><description>Mathematical programming techniques can be combined with response surface experimental design methods to optimize simulated systems. A computer simulation model has controllable input variables xi, i=1,…, n and yields responses ηj, j=1,…, m. A simulation trial at a particular set of values xik, i=1,…n produces an estimate yik for the system response ηj. This paper describes several formulations of the so-called “simulation/optimization” problem, including constrained optimization and multiple-objective optimization. It also describes several procedures for obtaining a solution to this problem, including a direct search technique, a first-order response surface method, and a second-order response surface approach. Each of these techniques combines simulation, response surface methodology, and mathematical programming.</description><subject>Mathematical Programming</subject><subject>Optimization</subject><subject>Response Surface Methodology</subject><subject>Search Methods</subject><subject>Simulation</subject><issn>0303-3929</issn><issn>2364-8201</issn><isbn>9783642007996</isbn><isbn>3642007996</isbn><isbn>3642008003</isbn><isbn>9783642008009</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2009</creationdate><recordtype>book_chapter</recordtype><sourceid/><recordid>eNpFkDtPw0AQhI-XRAhu-AVX0hj23nclRAkgBdFAbd3LwWD7LJ_T8OsxBIlpVjPfarUahK4I3BAAdXu_cUAoaMmP0AWTnAJoAHaMFnR2paZATlBhlD4wZYw8RQtgwEpmqDlHRc4fMEsKAtos0PrZTu-xs1PjbYuHMe1G23VNv8O2D3hGOA1T0zVf80bqcaqxT92wn-KIc9Pt2984X6Kz2rY5Fn9zid4269fVY7l9eXha3W3LTIDw0hId6lqTqFwQJBgZpXA-CgYciDRceBulC15QGqSgkioftLIhOA7eOMaW6PpwNw_j_GMcK5fSZ64IVD_1VP_1sG_nQlNu</recordid><startdate>20090224</startdate><enddate>20090224</enddate><creator>Biles, William E.</creator><creator>Swain, James J.</creator><general>Springer Berlin Heidelberg</general><scope/></search><sort><creationdate>20090224</creationdate><title>Mathematical programming and the optimization of computer simulations</title><author>Biles, William E. ; Swain, James J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-s1014-a18dff81e7bd51d96e65bce5304016945cae6bdc522d652627cd87addb40c9b33</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2009</creationdate><topic>Mathematical Programming</topic><topic>Optimization</topic><topic>Response Surface Methodology</topic><topic>Search Methods</topic><topic>Simulation</topic><toplevel>online_resources</toplevel><creatorcontrib>Biles, William E.</creatorcontrib><creatorcontrib>Swain, James J.</creatorcontrib></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Biles, William E.</au><au>Swain, James J.</au><au>Dembo, R. S.</au><au>Avriel, M.</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Mathematical programming and the optimization of computer simulations</atitle><btitle>Engineering Optimization</btitle><seriestitle>Mathematical Programming Studies</seriestitle><date>2009-02-24</date><risdate>2009</risdate><spage>189</spage><epage>207</epage><pages>189-207</pages><issn>0303-3929</issn><eissn>2364-8201</eissn><isbn>9783642007996</isbn><isbn>3642007996</isbn><eisbn>3642008003</eisbn><eisbn>9783642008009</eisbn><abstract>Mathematical programming techniques can be combined with response surface experimental design methods to optimize simulated systems. A computer simulation model has controllable input variables xi, i=1,…, n and yields responses ηj, j=1,…, m. A simulation trial at a particular set of values xik, i=1,…n produces an estimate yik for the system response ηj. 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| fulltext | fulltext |
| identifier | ISSN: 0303-3929 |
| ispartof | Engineering Optimization, 2009, p.189-207 |
| issn | 0303-3929 2364-8201 |
| language | eng |
| recordid | cdi_springer_books_10_1007_BFb0120864 |
| source | Springer Books |
| subjects | Mathematical Programming Optimization Response Surface Methodology Search Methods Simulation |
| title | Mathematical programming and the optimization of computer simulations |
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