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.

Autorentext

Dr. Carlos Oliveira is a researcher and consultant in the area of combinatorial optimization and data science. He holds a PhD in Operations Research from University of Florida. Dr. Oliveira has more than 15 years of experience in academia as well as in companies such as Bloomberg, Amazon, and AT&T, where he developed optimization and scientific applications. He is the author of 4 books in combinatorial optimization and financial programming. Miltiades P. Pardalos holds a BS in Industrial Engineering and is a Phd candidate at Texas A&M. His research is in the area of optimization and applications. He has previous experience working for Amazon.com.