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.

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

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.

Inhalt

Chapter 1. Route Sequence Prediction through Inverse Reinforcement Learning and Bayesian Optimization.- Chapter 2. A Comparative Evaluation of Monolithic and Microservices Architectures for Load Profiling Services in Smart Grids.- Chapter 3. Heuristics for the problem of consolidating orders into vehicle shipments with compatible categories and freight based on the direct distances to the farthest customers.- Chapter 4. Mathematical Models and Algorithms for Large-Scale Transportation Problems.- Chapter 5. Optimization Methods for Multicast Routing Problems.- Chapter 6. An Introduction to AI and Routing Problems in Mobile Telephony.- Chapter 7. AI Techniques for Combinatorial Optimization.- Chapter 8. Telecommunication Networks and Frequency Assignment Problems.- Chapter 9. The Metaheuristic Strategy for AI Search and Optimization.- Chapter 10. GRASP for Assignment Problem in Telecomunications.- Chapter 11. Waste Collection: Sectoring, Routing and Scheduling for Challenging Services.