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Examines the difficulties of simulation-based optimization problems
Identifies the distinct advantages of the ordinal approach for search-based type problems
The tools described in this book can be used separately or in conjunction with other methodological tools of optimization
Performance evaluation of increasingly complex human-made systems requires the use of simulation models. However, these systems are difficult to describe and capture by succint mathematical models. The purpose of this book is to address the difficulties of the optimization of complex systems via simulation models or other computation-intensive models involving possible stochastic effects and discrete choices. This book establishes distinct advantages of the "softer" ordinal approach for search-based type problems, analyzes its general properties, and shows the many orders of magnitude improvement in computational efficiency that is possible.