Discrete Diversity And Dispersion Maximization
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Discrete Diversity and Dispersion Maximization
This book demonstrates the metaheuristic methodologies that apply to maximum diversity problems to solve them. Maximum diversity problems arise in many practical settings from facility location to social network analysis and constitute an important class of NP-hard problems in combinatorial optimization. In fact, this volume presents a “missing link” in the combinatorial optimization-related literature. In providing the basic principles and fundamental ideas of the most successful methodologies for discrete optimization, this book allows readers to create their own applications for other discrete optimization problems. Additionally, the book is designed to be useful and accessible to researchers and practitioners in management science, industrial engineering, economics, and computer science, while also extending value to non-experts in combinatorial optimization. Owed to the tutorials presented in each chapter, this book may be used in a master course, a doctoral seminar, or as supplementary to a primary text in upper undergraduate courses. The chapters are divided into three main sections. The first section describes a metaheuristic methodology in a tutorial style, offering generic descriptions that, when applied, create an implementation of the methodology for any optimization problem. The second section presents the customization of the methodology to a given diversity problem, showing how to go from theory to application in creating a heuristic. The final part of the chapters is devoted to experimentation, describing the results obtained with the heuristic when solving the diversity problem. Experiments in the book target the so-called MDPLIB set of instances as a benchmark to evaluate the performance of the methods.
Operations Research Proceedings 2024
This book contains a selection of peer-reviewed papers presented at the International Conference on Operations Research (OR 2024), held at the Technical University of Munich, Germany, from September 3 to 6, 2024. Over 650 scientists from all over the world attended the OR 2024 in Munich. Three plenaries and nine semi-plenaries covered theoretical aspects of Operations Research, applications, and real-world practices. In addition, more than 500 presentations were held over three days in up to 23 parallel sessions. Covering a wide range of topics, the book highlights cutting-edge research and practical applications across various domains of modern operations research. It places a special emphasis on the theme "Data, Learning, and Optimization", exploring how data-driven approaches, machine learning techniques, and optimization strategies are shaping decision-making processes and operational efficiency. The contributions in this book reflect the diversity and innovation in the field, making it a valuable resource for researchers, practitioners, and academics alike.
Mathematical Analysis, Differential Equations And Applications
This comprehensive volume presents essential mathematical results devoted to topics of mathematical analysis, differential equations and their various applications. It focuses on differential operators, Wardowski maps, low-oscillation functions, Galois and Pataki connections, Hardy-type inequalities, to name just a few.Effort has been made for this unique title to have an interdisciplinary flavor and features several applications such as in tomography, elastic scattering, fluid mechanics, etc.This work could serve as a useful reference text to benefit professionals, academics and graduate students working in theoretical computer science, computer mathematics, and general applied mathematics.