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Full Description
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.
Contents
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.
