## Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems: 5th International Conference, CPAIOR 2008 Paris, France, May 20-23, 2008 ProceedingsThe 5th International Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR 2008) was held in Paris, France May 20–23, 2008. The purpose of this conference series is to bring together researchers in the ?elds of constraint programming, arti?cial intelligence, and operations research to explore ways of solving large-scale, practical optimization problems through integration and hybridization of the ?elds’ di?erent techniques. Through the years, this research community is discovering that the ?elds have much in c- mon, and there has been tremendous richness in the resulting cross-fertilization of ?elds. This year, we allowed submissions of both long (15 page) and short (5 page) papers, with short papers either being original work, a reduced version of a long paper, or an extended abstract of work published elsewhere. We were not s- prised by the 69 submissions in the long paper category: this is an active ?eld with many researchers. We were surprised by the 61 short paper submissions. This was far more than predicted. With 130 high-quality submissions, compe- tion for acceptance in this year’s program was particularly ?erce. In the end, we accepted 18 long papers and 22 short papers for presentation and publication in this volume. |

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### Contents

Accomplishments Opportunities and Challenges | 1 |

Selected Challenges from Distribution and Commerce in the Airline and Travel Industry | 2 |

30 Years of Constraint Programming | 5 |

A New Approach to Integrate CP and MIP | 6 |

New Filtering for the cumulative Constraint in the Context of NonOverlapping Rectangles | 21 |

Multistage Benders Decomposition for Optimizing Multicore Architectures | 36 |

Fast and Scalable Domino Portrait Generation | 51 |

Gap Reduction Techniques for Online Stochastic Project Scheduling | 66 |

Rapidly Solving an Online Sequence of Maximum Flow Problems with Extensions to Computing Robust Minimum Cuts | 283 |

A Hybrid Approach for Solving ShiftSelection and TaskSequencing Problems | 288 |

Solving a LogTruck Scheduling Problem with Constraint Programming | 293 |

Using Local Search to Speed Up Filtering Algorithms for Some NPHard Constraints | 298 |

A Hybrid Approach | 303 |

Efficient Haplotype Inference with Combined CP and OR Techniques | 308 |

Integration of CP and Compilation Techniques for Instruction Sequence Test Generation | 313 |

Propagating Separable Equalities in an MDD Store | 318 |

Integrating Symmetry Dominance and BoundandBound in a Multiple Knapsack Solver | 82 |

Cost Propagation Numerical Propagation for Optimization Problems | 97 |

FitnessDistance Correlation and SolutionGuided Multipoint Constructive Search for CSPs | 112 |

Leveraging Belief Propagation Backtrack Search and Statistics for Model Counting | 127 |

An Empirical Study on Knapsack Problems | 142 |

A Novel Approach For Detecting Symmetries in CSP Models | 158 |

A Multistep Anticipatory Algorithm for Online Stochastic Combinatorial Optimization | 173 |

Optimal Deployment of EventuallySerializable Data Services | 188 |

Counting Solutions of Knapsack Constraints | 203 |

From HighLevel Model to BranchandPrice Solution in G12 | 218 |

Simpler and Incremental Consistency Checking and Arc Consistency Filtering Algorithms for the Weighted Spanning Tree Constraint | 233 |

Stochastic Satisfiability Modulo Theories for Nonlinear Arithmetic | 248 |

A Hybrid Constraint Programming Local Search Approach to the JobShop Scheduling Problem | 263 |

Counting Solutions of Integer Programs Using Unrestricted Subtree Detection | 278 |

The Weighted CFG Constraint | 323 |

CP with ACO | 328 |

A Combinatorial Auction Framework for Solving Decentralized Scheduling Problems Extended Abstract | 333 |

Constraint Optimization and Abstraction for Embedded Intelligent Systems | 338 |

A Parallel Macro Partitioning Framework for Solving Mixed Integer Programs | 343 |

Guiding Stochastic Search by Dynamic Learning of the Problem Topography | 349 |

Hybrid Variants for Iterative Flattening Search | 355 |

Global Propagation of Practicability Constraints | 361 |

The Polytope of TreeStructured Binary Constraint Satisfaction Problems | 367 |

The Steel Mill Slab Design Problem Revisited | 377 |

Filtering Atmost on Pairs of Set Variables | 382 |

MIP Formulation and Strengthening with Logic Constraints | 387 |

392 | |

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Amsaa anticipatory algorithm approach Artiﬁcial Intelligence assignment backtracking benchmark Berlin Heidelberg 2008 branch-and-bound branching column component compute conﬁgurations conﬂict consistency constraint programming constraint satisfaction problems cost CP model CPAIOR CPLEX decision deﬁned Deﬁnition denote diﬀerent distribution domain edges eﬃcient elite solution feasible solution ﬁltering ﬁnd ﬁnding ﬁnite ﬁrst ﬁxed ﬂow formula framework given global graph haplotypes Heidelberg heuristic evaluation hybrid implementation infeasible integer programming interval iterations knapsack constraints knapsack problem linear LNCS local search lower bound LP relaxation method minimum spanning tree mixed integer programming node Operations Research optimal solution parameterised performance pheromone problem instances proﬁt propagation pruning quantiﬁed random relaxation S-RCPSP sampling satisﬁed scenarios scheduling problem SCIP search algorithms sequence SGMPCS signiﬁcant signiﬁcantly solve solver spanning tree speciﬁc Springer stochastic subproblem subtree symmetry tabu search tasks techniques Trick Eds update upper bound variables X-MDP