Distributionally Robust Optimization With Applications To Support Vector Machines

Distributionally Robust Optimization with Applications to Support Vector Machines

Proceedings of the 11th International Conference on Production Research – Americas

This book presents the proceedings of the conference and provides valuable insights into the issues facing Small and Medium Enterprises (SMEs), particularly in the areas of sustainable operations and digitalization. It comprises a series of papers presented at the conference, covering topics such as: challenges faced by SMEs in a post-pandemic era; digitalization and its impact on SMEs; sustainable operations in SMEs; international market performance improvement in SMEs; SMEs infrastructure and integration with research, development, and innovation institutions; and SMEs participation in business networks. The papers offer a unique perspective on the challenges and opportunities facing SMEs and provides practical solutions for those looking to help their organizations thrive in a rapidly changing business environment.

Nature-inspired Methods for Stochastic, Robust and Dynamic Optimization

Nature-inspired algorithms have a great popularity in the current scientific community, being the focused scope of many research contributions in the literature year by year. The rationale behind the acquired momentum by this broad family of methods lies on their outstanding performance evinced in hundreds of research fields and problem instances. This book gravitates on the development of nature-inspired methods and their application to stochastic, dynamic and robust optimization. Topics covered by this book include the design and development of evolutionary algorithms, bio-inspired metaheuristics, or memetic methods, with empirical, innovative findings when used in different subfields of mathematical optimization, such as stochastic, dynamic, multimodal and robust optimization, as well as noisy optimization and dynamic and constraint satisfaction problems.