Simulation optimization of complex systems with noisy parameter spaces can become computationally expensive on a single processor system. This book discusses construction of software for solving optimization problems by distributing the work load among several processors residing on a network. Open source repositories are used for the development of this software. The Simulated Annealing algorithm is used to search the parameter space for optimization. Application of the software to stochastic and deterministic problem scenarios is closely examined. Since the convergence of the simulated annealing algorithm depends on the choice of annealing parameters, different types of simple and elaborate cooling schedules are applied to problem instances and their impact on the quality of convergence is assessed.

MSE in Industrial Engineering from the University of Alabama in Huntsville (UAHuntsville). PhD candidate at UAHuntsville with research interests in simulation optimization, experimental design and multivariate statistical analysis.