This book aims at illustrating strategies to account§for uncertainty in complex systems described by computer simulations. When§optimizing the performances of these systems, accounting or neglecting§uncertainty may lead to completely different results; therefore, uncertainty§management is a major issues in simulation-optimization. Because of its wide§field of applications, simulation-optimization issues have been addressed by§different communities with different methods, and from slightly different§perspectives. Alternative approaches have been developed, also depending on the§application context, without any well-established method clearly outperforming§the others.§§This editorial project brings together as chapter§contributors researchers from different (though interrelated) areas; namely,§statistical methods, experimental design, stochastic programming, global§optimization, metamodeling, and design and analysis of computer simulation§experiments. Editors goal is to take advantage of such a multidisciplinary§environment, to offer to the readers a much deeper understanding of the§commonalities and differences of the various approaches to simulation-based§optimization, especially in uncertain environments. Editors aim to offer a§bibliographic reference on the topic, enabling interested readers to learn§about the state-of-the-art in this research area, also accounting for potential§real-world applications to improve also the state-of-the-practice. Besides§researchers and scientists of the field, the primary audience for the proposed§book includes PhD students, academic teachers, as well as practitioners and§professionals. Each of these categories of potential readers present adequate§channels for marketing actions, e.g. scientific, academic or professional§societies, internet-based communities,§and authors or buyers of related publications.
