Integration of Constraint Programming, Artificial Intelligence, and Operations Research

Core Boosting in SAT-Based Multi-Objective Optimization.- Fair Minimum Representation Clustering.- Proof Logging for the Circuit Constraint.- Probabilistic Lookahead Strong Branching via a Stochastic Abstract Branching Model.- Lookahead, Merge and Reduce for Compiling Relaxed Decision Diagrams for Optimization.- LEO: Learning Efficient Orderings for Multiobjective BDDs.- Minimizing the Cost of Leveraging Influencers in Social Networks: IP and CP Approaches.- Learning Deterministic Surrogates for Robust Convex QCQP.- Strategies for Compressing the Pareto Frontier: Application to Strategic Planning of Hydropower in the Amazon Basin.- Improving Metaheuristic Effciency for Stochastic Optimization Problems by Sequential Predictive Sampling.- SMT-based Repair of Disjunctive Temporal Networks with Uncertainty: Strong and Weak Controllability.- CaVE: A Cone-aligned Approach for Fast Predict-then-optimize with Binary Linear Programs.- A Constraint Programming Approach for Aircraft Disassembly Scheduling.- Optimization Over Trained Neural Networks: Taking a Relaxing Walk.- Learning From Scenarios for Repairable Stochastic Scheduling.- Explainable Algorithm Selection for the Capacitated Lot Sizing Problem.- Efficient Structured Perceptron for NP-hard Combinatorial Optimization Problems.- Robustness Verification in Neural Networks.- An Improved Neuro-Symbolic Architecture to Fine-Tune Generative AI Systems.- Bound Tightening using Rolling-Horizon Decomposition for Neural Network Verification.- Learning Heuristics for Combinatorial Optimization Problems on K-Partite Hypergraphs.