Constraint and Integer Programming. - 1 Introduction. - 2 CP(FD) Basic Concepts. - 3 Integer Linear Programming Basic Concepts. - 4 Incomplete search strategies. - 5 Conclusion. - References. - Two Generic Schemes for Efficient and Robust Cooperative Algorithms. - 1 Introduction. - 2 Operations Research Algorithms and Constraint Programming. - 3 Operations Research Algorithms and Mixed Integer Programming. - 4 Constraint Programming and Mixed Integer Programming. - 5 Operations Research Algorithms and Local Search. - 6 Mixed Integer Programming and Local Search. - 7 Constraint Programming and Local Search. - References. - Branch-and-Infer: A Framework for Combining CP and IP. - 1 Introduction. - 2 Modeling in CP and IP. - 3 An illustrating example: discrete tomography. - 4 Branch and Infer. - 5 Symbolic constraints in IP. - 6 Example: Symbolic constraints for supply chain planning. - 7 Summary. - References. - Global Constraints and FiItering Algorithms. - 1 Introduction. - 2 Global Constraints. - 3 Filtering Algorithms. - 4 Two Successful Filtering Algorithms. - 5 Global Constraints and Over-eonstrained Problems. - 6 Quality of Filtering Algorithms. - 7 Discussion. - 8 Conclusion. - References. - Exploiting relaxations in CP. - 1 Introduction and Motivation. - 2 Integer Linear Programming and Relaxations. - 3 Integrating Relaxations in CP. - 4 Relax to propagate. - 5 Relax to guide the search. - 6 A case study: global optimization constraints for a Path constraint. - References. - Hybrid Problem Solving in ECLiPSe. - 1 Introduction. - 2 Integration of Constraints and Operations Research. - 3 Language Ingredients for Hybrid Solvers. - 4 ECLiPSe as a Platform for Building Hybrid Aigorithms. - 5 Programming a Hybrid Search in ECLiPSe. - 6 Conclusion. - References. - CP Based Branch-and-Price. - 1 Introduction. - 2 Three Illustrative Examples. - 3 Implementation Issues. - 4 Future Directions for CP Based Branch-and-Price. - References. - Randomized Backtrack Search. - 1 Introduction. - 2 Randomization of Backtrack Search Methods. - 3 Formal Models of Heavy-Tailed Behavior. - 4 Heavy and Fat-Tailed Distributions. - 5 Heavy and Fat-Tailed Distributions in Backtrack Search. - 6 Restart Strategies. - 7 Portfolio Strategies. - 8 Conclusions. - References. - Local Search and Constraint Programming. - LS and CP ilLustrated on a transportation Problem. - 1 Introduction. - 2 A didactic transportation problem. - 3 A CP approach for dTP. - 4 Constructive Algorithms. - 5 LS as Post-Optimization. - 6 Metaheuristics. - 7 LS during construction. - 8 Conclusions. - References. - Open Perspectives. - 1 Motivations Challenges and Applications. - 2 Transforming Models to Aigorithms. - 3 New Techniques. - 4 New Application Areas. - References. Language: English