Integration of Constraint Programming, Artificial Intelligence, and Operations Research 21st International Conference, CPAIOR 2024, Uppsala, Sweden, May 28–31, 2024, Proceedings, Part II

This book constitutes the proceedings of the 21st International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2024, held in Uppsala, Sweden, during May 28–31, 2024.The 33 full papers and the 9 short papers presented in the proceedings were carefully reviewed and selected from a total of 104 submissions.

The content of the papers focus on new techniques or applications in the area and foster the integration of techniques from different fields dealing with large and complex problems. 

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.