Scholarly Commons

An electronic repository for the intellectual products of the Miami University community

A Probabilistic Solution Generator of Good Enough Designs for Simulation

DSpace/Manakin Repository

Show simple item record Ozden, Mufit en_US Ho, Yu-Chi en_US 2008-07-22T19:31:18Z en_US 2013-07-10T15:06:41Z 2008-07-22T19:31:18Z en_US 2013-07-10T15:06:41Z 1999-12-01 en_US 2007-11-26 en_US
dc.identifier.uri en_US
dc.description.abstract We build a probabilistic solution generator using the learning automata theory, which can generate a small set of "good enough" designs with a predetermined high probability. The main goal of our work is to reduce a large design population to a much smaller subset of good designs that can be analyzed thoroughly in a subsequent simulation study to identify the best design among them. In the process of building the solution generator, a rough-cut design evaluation method with a high noise error is employed in order to screen designs very rapidly _ may it be an approximate method, a heuristic approach, or short simulation runs. The solution generator has been applied successfully to several serious test problems with noisy objectives. en_US
dc.subject simulation en_US
dc.subject stochastic optimization en_US
dc.subject ordinal optimization en_US
dc.subject learning automata theory en_US
dc.title A Probabilistic Solution Generator of Good Enough Designs for Simulation en_US
dc.type Text en_US
dc.type.genre Article en_US

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search SC

Advanced Search


My Account