Handbook of artificial intelligence and data sciences for routing problems 

 - Cham  Springer  2025 
 - xx, 257 p. 
<br/><br/> Table of contents:<br/>Front Matter<br/>Pages i-xx<br/>Download chapter PDF <br/>Route Sequence Prediction Through Inverse Reinforcement Learning and Bayesian Optimization<br/>Anselmo R. Pitombeira-Neto<br/>Pages 1-16<br/>A Comparative Evaluation of Monolithic and Microservice Architectures for Load Profiling Services in Smart Grids<br/>Artur F. S. Veloso, José V. R. Júnior, Matheus M. do N. Costa, Ricardo A. L. Rabelo, Placido R. Pinheiro<br/>Pages 17-36<br/>Heuristics for the Problem of Consolidating Orders into Vehicle Shipments with Compatible Categories and Freight Based on the Direct Distances to the Farthest Customers<br/>Renan Sallai Iwayama, Claudio B. Cunha<br/>Pages 37-68<br/>Mathematical Models and Algorithms for Large-Scale Transportation Problems<br/>Carlos A. S. Oliveira<br/>Pages 69-91<br/>Optimization Methods for Multicast Routing Problems<br/>Carlos A. S. Oliveira<br/>Pages 93-106<br/>An Introduction to AI and Routing Problems in Mobile Telephony<br/>Carlos A. S. Oliveira<br/>Pages 107-122<br/>AI Techniques for Combinatorial Optimization<br/>Carlos A. S. Oliveira<br/>Pages 123-135<br/>Telecommunication Networks and Frequency Assignment Problems<br/>Carlos A. S. Oliveira<br/>Pages 137-155<br/>The Metaheuristic Strategy for AI Search and Optimization<br/>Carlos A. S. Oliveira<br/>Pages 157-176<br/>GRASP for Assignment Problem in Telecommunications<br/>Carlos A. S. Oliveira<br/>Pages 177-201<br/>Waste Collection: Sectoring, Routing, and Scheduling for Challenging Services<br/>Marcos Negreiros, Nelson Maculan, Claudio B. Cunha, Francisco Henrique Viana, Flávio Luis Mello<br/>Pages 203-257<br/><br/>[https://link.springer.com/book/10.1007/978-3-031-78262-6] 
<br/><br/> 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.<br/><br/>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.<br/><br/>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.<br/><br/>(https://link.springer.com/book/10.1007/978-3-031-78262-6) 
<br/><br/><label>ISBN: </label>9783031782619 
<br/><br/><label>Subjects--Topical Terms: </label><br/>AI--Algorithms<br/>Datascience<br/>Artificial intelligence
<br/><br/><label>Dewey Class. No.: </label>006.31  / OLI