Handbook of Artificial Intelligence and Data Sciences for Routing Problems
Series: Springer Optimization and Its Applications; 219;
- Publisher's listprice EUR 213.99
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90 774 Ft (86 451 Ft + 5% VAT)
The price is estimated because at the time of ordering we do not know what conversion rates will apply to HUF / product currency when the book arrives. In case HUF is weaker, the price increases slightly, in case HUF is stronger, the price goes lower slightly.
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90 774 Ft
Availability
Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
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Delivery time is estimated on our previous experiences. We give estimations only, because we order from outside Hungary, and the delivery time mainly depends on how quickly the publisher supplies the book. Faster or slower deliveries both happen, but we do our best to supply as quickly as possible.
Product details:
- Publisher Springer
- Date of Publication 14 March 2025
- Number of Volumes 1 pieces, Book
- ISBN 9783031782619
- Binding Hardback
- No. of pages257 pages
- Size 235x155 mm
- Language English
- Illustrations 32 Illustrations, black & white; 29 Illustrations, color 695
Categories
Short 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.
MoreLong 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.
More
Table of 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.
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