Hybrid Optimization

Hybrid Optimization focuses on the application of artificial intelligence and operations research techniques to constraint programming for solving combinatorial optimization problems. This book covers the most relevant topics investigated in the last ten years by leading experts in the field, and speculates about future directions for research.

This book includes contributions by experts from different but related areas of research including constraint programming, decision theory, operations research, SAT, artificial intelligence, as well as others. These diverse perspectives are actively combined and contrasted in order to evaluate their relative advantages.

This volume presents techniques for hybrid modeling, integrated solving strategies including global constraints, decomposition techniques, use of relaxations, and search strategies including tree search local search and metaheuristics. Various applications of the techniques presented as well as supplementary computational tools are also discussed.

Includes both well established research results, and directions for future research. Provides several solution methods for common constraint programming problems. Presents both theoretical techniques and real-world applications in artificial intelligence and operations research. Includes supplementary material: sn.pub/extras

Texte du rabat
This volume focuses on the integration of artificial intelligence and constraint programming to solve problems in operations research and combinatorial optimization. This volume collects the contributions of experts from various research areas including decision theory, systems engineering, propositional satisfiability, mathematical optimization, and artificial intelligence. These invited scholars describe and demonstrate some of the most important topics and results from the last ten years of research related to hybrid optimization. Key Features: - Includes both well established research results, and directions for future research. - Provides several solution methods for common combinatorial optimization and decision problems. - Presents both theoretical techniques and real-world applications in artificial intelligence and operations research. Hybrid Optimization can serve as a valuable resource for graduate students, researchers and practitioners studying artificial intelligence or operations research who are interested in investigating or applying constraint programming techniques.

Résumé

Hybrid Optimization focuses on the application of artificial intelligence and operations research techniques to constraint programming for solving combinatorial optimization problems. This book covers the most relevant topics investigated in the last ten years by leading experts in the field, and speculates about future directions for research.

This book includes contributions by experts from different but related areas of research including constraint programming, decision theory, operations research, SAT, artificial intelligence, as well as others. These diverse perspectives are actively combined and contrasted in order to evaluate their relative advantages.

This volume presents techniques for hybrid modeling, integrated solving strategies including global constraints, decomposition techniques, use of relaxations, and search strategies including tree search local search and metaheuristics. Various applications of the techniques presented as well as supplementary computational tools are also discussed.

Contenu

• Preface. - The Ten Years of CPAIOR: A Success Story (M. Milano and P. V. Hentenryck). - Hybrid Modeling (J. N. Hooker). - Global Constraints: A Survey (J.C. Régin). - Decomposition Techniques for Hybrid MILP/CP Models Applied to Scheduling and Routing Problems (P. Castro, I. E. Grossmann, and L.M. Rousseau). - Hybrid Solving Techniques (T. Achterberg and A. Lodi). - Over-Constrained Problems (W.J. V. Hoeve). - A Survey on CP-AI-OR Hybrids for Decision Making Under Uncertainty (B. Hnich, R. Rossi, S.A. Tarim, and S. Prestwich). - Constraint Programming and Local Search Hybrids (P. Shaw). - Hybrid Metaheuristics (C. Blum, J. Puchinger, G. Raidl, and A. Roli). - Learning in Search (P. Refalo). - What is Autonomous Search? (Y. Hamadi, E. Monfroy, and F. Saubion). - Software Tools Supporting Integration (T. Yunes). - Connections and Integration with SAT Solvers: A Survey and a Case Study in Computational Biology (F. Corblin, L. Bordeaux, E. Fanchon, Y. Hamadi, and L. Trilling). - Bioinformatics: A Challenge to Constraint Programming (P. Barahona, L. Krippahl, O. Perriquet). - Sports Scheduling (M. A. Trick). - Stimuli Generation for Functional Hardware Verification with Constraint Programming (A. Adir and Y. Naveh).