000			03337nam a22002297a 4500
005			20251012170031.0
008			251012b |||||||| |||| 00| 0 eng d
020			_a9783031782619
082			_a006.31 _bOLI
245			_aHandbook of artificial intelligence and data sciences for routing problems
260			_aCham _bSpringer _c2025
300			_axx, 257 p.
365			_aINR _b13642.82
500			_aTable 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]
520			_aThis 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)
650			_aAI--Algorithms _925559
650			_aDatascience _925560
650			_aArtificial intelligence
700			_aOliveira, Carlos A. S. [Editor] _925561
700			_aPardalos, Miltiades P. [Editor] _925562
942			_cBK _2ddc
999			_c10486 _d10486