Handbook of artificial intelligence and data sciences for routing problems

Contributor(s):

Material type: Text

TextPublication details: Cham Springer 2025Description: xx, 257 pISBN:

9783031782619

Subject(s):

DDC classification:

006.31 OLI

Summary: 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. (https://link.springer.com/book/10.1007/978-3-031-78262-6)

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Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode
Book	Indian Institute of Management LRC General Stacks	IT & Decisions Sciences	006.31 OLI (Browse shelf(Opens below))	1	Available		009084

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Previous	006.31 MUE Deep learning for dummies	006.31 MUL Introduction to machine learning with Python: a guide for data scientists	006.31 NOK Data science solutions with python: fast and scalable models using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn	006.31 OLI Handbook of artificial intelligence and data sciences for routing problems	006.31 PLA Deep reinforcement learning	006.31 PRA Machine learning using python	006.31 RAH Machine learning using R	Next

Table of contents:
Front Matter
Pages i-xx
Download chapter PDF
Route Sequence Prediction Through Inverse Reinforcement Learning and Bayesian Optimization
Anselmo R. Pitombeira-Neto
Pages 1-16
A Comparative Evaluation of Monolithic and Microservice Architectures for Load Profiling Services in Smart Grids
Artur F. S. Veloso, José V. R. Júnior, Matheus M. do N. Costa, Ricardo A. L. Rabelo, Placido R. Pinheiro
Pages 17-36
Heuristics for the Problem of Consolidating Orders into Vehicle Shipments with Compatible Categories and Freight Based on the Direct Distances to the Farthest Customers
Renan Sallai Iwayama, Claudio B. Cunha
Pages 37-68
Mathematical Models and Algorithms for Large-Scale Transportation Problems
Carlos A. S. Oliveira
Pages 69-91
Optimization Methods for Multicast Routing Problems
Carlos A. S. Oliveira
Pages 93-106
An Introduction to AI and Routing Problems in Mobile Telephony
Carlos A. S. Oliveira
Pages 107-122
AI Techniques for Combinatorial Optimization
Carlos A. S. Oliveira
Pages 123-135
Telecommunication Networks and Frequency Assignment Problems
Carlos A. S. Oliveira
Pages 137-155
The Metaheuristic Strategy for AI Search and Optimization
Carlos A. S. Oliveira
Pages 157-176
GRASP for Assignment Problem in Telecommunications
Carlos A. S. Oliveira
Pages 177-201
Waste Collection: Sectoring, Routing, and Scheduling for Challenging Services
Marcos Negreiros, Nelson Maculan, Claudio B. Cunha, Francisco Henrique Viana, Flávio Luis Mello
Pages 203-257

[https://link.springer.com/book/10.1007/978-3-031-78262-6]

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.

(https://link.springer.com/book/10.1007/978-3-031-78262-6)

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