Springer Cham, 2025. — 257 p. — (Springer Optimization and Its Applications, volume 219). — eBook ISBN 978-3-031-78262-6.
Multidisciplinary topic has the benefit of bringing together different perspectives to facilitate new developments.
Covers basic techniques that are of interest to researchers who need familiarity with routing problems.
Covers advanced methods that are of interest to researchers who want to explore the frontiers of AI.
This handbook delves into the rapidly evolving field of artificial intelligence and optimization, focusing on the intersection of machine learning, combinatorial optimization, and real-world applications in transportation and network design.
Covering an array of topics from classical optimization problems such as the Traveling Salesman Problem and the Knapsack Problem, to modern techniques including advanced heuristic methods, Generative Adversarial Networks, and Variational Autoencoders, this book provides a roadmap for solving complex problems. The included case studies showcase practical implementations of algorithms in predicting route sequences, traffic management, and eco-friendly transportation.
This comprehensive guide is essential for researchers, practitioners, and students interested in AI and optimization. Whether you are a researcher seeking standard approaches or a professional looking for practical solutions to industry challenges, this book offers valuable insights into modern AI algorithms.
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