Constraint and Integer Programming presents some of the basic ideas of constraint programming and mathematical programming, explores approaches to integration, brings us up to date on heuristic methods, and attempts to discern future directions in this fast-moving field.

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List of Figures. List of Tables. Preface. Contributing Authors. Foreword; J. Hooker. 1: Constraint and Integer Programming; M. Milano, M. Trick. 1. Introduction. 2. CP(FD) Basic Concepts. 3. Integer Linear Programming Basic Concepts. 4. Incomplete search strategies. 5. Conclusion. References. 2: Two Generic Schemes for Efficient and Robust Cooperative Algorithms; E. Danna, C. Le Pape. 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. 3: Branch-and-Infer; A. Bockmayr, T. Kasper. 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. 4: Global Constraints and Filtering Algorithms; J.-C. Régin. 1. Introduction. 2. Global Constraints. 3. Filtering Algorithms. 4. Two Successful Filtering Algorithms. 5. Global Constraints and Over-constrained Problems. 6. Quality of Filtering Algorithms. 7. Discussion. 8. Conclusion. References. 5:Exploiting relaxations in CP; F. Focacci, A. Lodi, M. Milano. 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. 6: Hybrid Problem Solving in ECLiPSe; F. Ajili, M.G. Wallace. 1. Introduction. 2. Integration of Constraints and Operations Research. 3. Language Ingredients for Hybrid Solvers. 4. ECLiPSe as a Platform for Building Hybrid Algorithms. 5. Programming a Hybrid Search in ECLiPSe. 6. Conclusion. References. 7: CP Based Branch-and-Price; K. Easton, G. Nemhauser, M. Trick. 1. Introduction. 2. Three Illustrative Examples. 3. Implementation Issues. 4. Future Directions for CP Based Branch-and-Price. References. 8: Randomized Backtrack Search; C.P. Gomes. 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. 9: Local Search and Constraint Programming: LS and CP illustrated on a transportation Problem; F. Focacci, F. Laburthe, A. Lodi. 1. Introduction. 2. A didactic transportation problem. 3. A CP approach