Performance evaluation of increasingly complex human-made systems requires the use of simulation models. However, these systems are difficult to describe and capture by succinct mathematical models. Accepting the need for search-based methods as a complement to established analytical techniques, quickly narrowing the search for optimum performance (ordinal optimization) is more important than accurately estimating the values of system performance during the process of optimization (cardinal optimization).

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 will establish distinct advantages of the "softer" ordinal approach for search-based type problems, analyze its general properties, and show the many orders of magnitude improvement in computational efficiency that is possible.

Performance evaluation of increasingly complex human-made systems requires the use of simulation models. However, these systems are difficult to describe and capture by succinct mathematical models. Accepting the need for search-based methods as a complement to established analytical techniques, quickly narrowing the search for optimum performance (ordinal optimization) is more important than accurately estimating the values of system performance during the process of optimization (cardinal optimization).

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 will establish distinct advantages of the "softer" ordinal approach for search-based type problems, analyze its general properties, and show the many orders of magnitude improvement in computational efficiency that is possible.

Performance evaluation of increasingly complex human-made systems requires the use of simulation models. However, these systems are difficult to describe and capture by succinct mathematical models. Accepting the need for search-based methods as a complement to established analytical techniques, quickly narrowing the search for optimum performance (ordinal optimization) is more important than accurately estimating the values of system performance during the process of optimization (cardinal optimization).

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 will establish distinct advantages of the "softer" ordinal approach for search-based type problems, analyze its general properties, and show the many orders of magnitude improvement in computational efficiency that is possible.