Integration of AI and OR Techniques in Constraint Programming

This book constitutes the proceedings of the 12th International Conference on the Integration of Artificial Intelligence (AI) and Operations Research (OR) Techniques in Constraint Programming, CPAIOR 2015, held in Barcelona, Spain, in May 2015. The 29 papers presented together with 8 short papers in this volume were carefully reviewed and selected from 90 submissions. The purpose of the conference series is to bring together researchers in the fields of Constraint Programming, Artificial Intelligence and Operations Research to explore ways of solving hard and large scale combinatorial optimization problems that emerge in various industrial domains. Pooling the skills and strengths of this diverse group of researchers has proved extremely effective and valuable during the past decade leading to improvements and cross-fertilization between the three fields as well as breakthrough for actual applications.

Table of Contents

Chapter 1. A Time-Dependent No-Overlap Constraint: Application to Urban Delivery Problems
Chapter 2. Rectangle Placement for VLSI Testing
Chapter 3. A Constraint-Based Local Search for Edge Disjoint Rooted Distance-Constrained Minimum Spanning Tree Problem
Chapter 4. A Benders Approach to the Minimum Chordal Completion Problem
Chapter 5. MaxSAT-Based Scheduling of B2B Meetings
Chapter 6. Embedding Decision Trees and Random Forests in Constraint Programming
Chapter 7. Scheduling with Fixed Maintenance, Shared Resources and Nonlinear Feedrate Constraints: A Mine Planning Case Study
Chapter 8. Learning Value Heuristics for Constraint Programming
Chapter 9. Derivative-Free Optimization: Lifting Single-Objective to Multi-Objective Algorithm
Chapter 10. Branching on Multi-aggregated Variables
Chapter 11. Time-Table Disjunctive Reasoning for the Cumulative Constraint
Chapter 12. Uncertain Data Dependency Constraints in Matrix Models
Chapter 13. An Efficient Local Search for Partial Latin Square Extension Problem
Chapter 14. Enhancing MIP Branching Decisions by Using the Sample Variance of Pseudo Costs
Chapter 15. BDD-Guided Clause Generation
Chapter 16. Combining Constraint Propagation and Discrete Ellipsoid-Based Search to Solve the Exact Quadratic Knapsack Problem
Chapter 17. Large Neighborhood Search for Energy Aware Meeting Scheduling in Smart Buildings
Chapter 18. ILP and CP Formulations for the Lazy Bureaucrat Problem
Chapter 19. The Smart Table Constraint
Chapter 20. Constraint-Based Sequence Mining Using Constraint Programming
Chapter 21. A Comparative Study of MIP and CP Formulations for the B2B Scheduling Optimization Problem
Chapter 22. Constraint-Based Local Search for Golomb Rulers
Chapter 23. Packing While Traveling: Mixed Integer Programming for a Class of Nonlinear Knapsack Problems
Chapter 24. MaxSAT-Based Cutting Planes for Learning Graphical Models
Chapter 25. A Multistage Stochastic Programming Approach to the Dynamic and Stochastic VRPTW
Chapter 26. Constraint Solving on Bounded String Variables
Chapter 27. Freight Train Threading with Different Algorithms
Chapter 28. Learning General Constraints in CSP
Chapter 29. Understanding the Potential of Propagators
Chapter 30. Failure-Directed Search for Constraint-Based Scheduling