Cantitate/Preț
Produs
Total produse
Vezi coșul
Integration of Constraint Programming, Artificial Intelligence, and Operations Research de Pierre Schaus

Integration of Constraint Programming, Artificial Intelligence, and Operations Research: Lecture Notes in Computer Science, cartea 13292

Editat de Pierre Schaus
en Limba Engleză Paperback – 11 iun 2022
This book constitutes the proceedings of the 19th International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2022, which was held in Los Angeles, CA, USA, in June 2022.The 28 regular papers presented were carefully reviewed and selected from a total of 60 submissions. The conference program included a Master Class on the topic "Bridging the Gap between Machine Learning and Optimization”.
Citește tot Restrânge

Din seria Lecture Notes in Computer Science

Preț: 49766 lei

Preț vechi: 62207 lei
-20%

Puncte Express: 746

Carte disponibilă

Livrare economică 20 iunie-04 iulie
Livrare express 06-12 iunie pentru 4263 lei

Livrare prin curier în România Termenul estimat este afișat lângă disponibilitate.
Transport gratuit pentru acest produs Plată online sau ramburs, în funcție de opțiunile comenzii.
Retur gratuit în 14 zile Comandă securizată și suport în română.
 Adaugă în coș

Wish list
Gift list
Am citit!
Adaugă în listă

Specificații

ISBN-13: 9783031080104
ISBN-10: 3031080106
Pagini: 464
Ilustrații: XIX, 442 p. 101 illus., 64 illus. in color.
Dimensiuni: 155 x 235 x 25 mm
Greutate: 0.7 kg
Ediția:1st edition 2022
Editura: Springer
Colecția Lecture Notes in Computer Science
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

A Two-Phase Hybrid Approach for the Hybrid Flexible Flowshop with Transportation Times.- A SAT Encoding to compute Aperiodic Tiling Rhythmic Canons.- Transferring Information across Restarts in MIP.- Towards Copeland Optimization in Combinatorial Problems.- Coupling Different Integer Encodings for SAT.- Model-Based Algorithm Configuration with Adaptive Capping and Prior Distributions.- Shattering Inequalities for Learning Optimal Decision Trees.- Learning Pseudo-Backdoors for Mixed Integer Programs.- Leveraging Integer Linear Programming to Learn Optimal Fair Rule Lists.- Solving the Job Shop Scheduling Problem extended with AGVs – Classical and Quantum Approaches.- Stochastic Decision Diagrams.- Improving the robustness of EPS to solve the TSP.- Efficient operations between MDDs and constraints.- Deep Policy Dynamic Programming for Vehicle Routing Problems.- Learning a Propagation Complete Formula.- A FastMap-Based Algorithm for Block Modeling.- Packing by Scheduling: Using ConstraintProgramming to Solve a Complex 2D Cutting Stock Problem.- Dealing with the product constraint.- Multiple-choice knapsack constraint in graphical models.- A Learning Large Neighborhood Search for the Staff Rerostering Problem.- Practically Uniform Solution Sampling in Constraint Programming.- Training Thinner and Deeper Neural Networks: Jumpstart Regularization.- Hybrid Offline/Online Optimization for Energy Management via Reinforcement Learning.- Enumerated Types and Type Extensions for MiniZinc.- A parallel algorithm for generalized arc-consistent filtering for the Alldifferent constraint.- Analyzing the Reachability Problem in Choice Networks.- Model-based Approaches to Multi-Attribute Diverse Matching.