The optimization of simulation models by genetic algorithms:a comparative study
This dissertation is a comparative study of simulation optimization methods. We compare a new technique, genetic search, to two old techniques: the pattern search and the response surface methodology search. The pattern search uses the Hooke and Jeeves algorithm and the response surface method se...
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| Format: | Others |
| Language: | en |
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Virginia Tech
2014
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| Online Access: | http://hdl.handle.net/10919/38929 http://scholar.lib.vt.edu/theses/available/etd-07282008-135041/ |
| Summary: | This dissertation is a comparative study of simulation optimization methods. We
compare a new technique, genetic search, to two old techniques: the pattern search and
the response surface methodology search. The pattern search uses the Hooke and Jeeves
algorithm and the response surface method search uses the code of Dennis Smith. The
research compares these three algorithms for accuracy and stability.
In accuracy we look at how close the algorithm comes to the optimum. The optimum
having been previously determined from exhaustive testing. We evaluate stability by
using the variance of the response function as determined from 50 searches.
The test-bed consists of three simulation models. We took the three simulation models
from text books and modified them to make them optimization models if that was
required. The first model consists of a big S, little s inventory system with two decision
variables: big S and little s. The response is the monthly cost of operating the inventory
system. The second model was a university time-sharing computer system with two
decision variables: quantum, the amount of time that the computer spends on a job
before sending it back to the queue and overhead, that is the time that its takes to
execute this routing operation. The response was the cost of operating the system
determined from a cost function. The third model was a job-shop with five decision
variables: the number of machines at each of the five work stations. The response was
the cost of operating the job-shop again determined from a cost function. The decision
variables were integer for the inventory system and job-shop, and were real for the
computer system. === Ph. D. |
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