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A COMPARISON OF SIMULATION OPTIMIZATION TECHNIQUES IN SOLVING SINGLE-OBJECTIVE, CONSTRAINED, DISCRETE VARIABLE PROBLEMS
Krumpe, Norman Joseph

2005, Master of Computer Science, Miami University, Computer Science and Systems Analysis.
Many analytic techniques are inadequate for finding optimal solutions to complex problems. Simulation is often used to model many such problems, but it is not always possible, or reasonable, to simulate every possible combination of control variables. Therefore, many simulation optimization techniques have been developed that attempt to iteratively search for a global optimum without testing every control parameter combination. The purpose of this study is to compare three such techniques: Simulated annealing, Hooke-Jeeves pattern search, and an algorithm that combines the essential features of simulated annealing and pattern search. These algorithms will be compared by applying them to several variants of three types of problems: two discrete-event simulations and one deterministic function. The results do not point to any one of these three algorithms outperforming the others. Rather results suggest that the performance of any of these algorithms depends on the many implementation decisions that must be made.
Donald Byrkett (Advisor)
51 p.

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Krumpe, N. (2005). A COMPARISON OF SIMULATION OPTIMIZATION TECHNIQUES IN SOLVING SINGLE-OBJECTIVE, CONSTRAINED, DISCRETE VARIABLE PROBLEMS. (Electronic Thesis or Dissertation). Retrieved from https://etd.ohiolink.edu/

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Krumpe, Norman. "A COMPARISON OF SIMULATION OPTIMIZATION TECHNIQUES IN SOLVING SINGLE-OBJECTIVE, CONSTRAINED, DISCRETE VARIABLE PROBLEMS." Electronic Thesis or Dissertation. Miami University, 2005. OhioLINK Electronic Theses and Dissertations Center. 11 Dec 2017.

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Krumpe, Norman "A COMPARISON OF SIMULATION OPTIMIZATION TECHNIQUES IN SOLVING SINGLE-OBJECTIVE, CONSTRAINED, DISCRETE VARIABLE PROBLEMS." Electronic Thesis or Dissertation. Miami University, 2005. https://etd.ohiolink.edu/

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