Integration of Constraint Programming, Artificial Intelligence, and Operations Research

23rd International Conference, CPAIOR 2026, Rabat, Morocco, May 26–29, 2026, Proceedings

AvTias Guns

Häftad, Engelska, 2026

Del i serien Lecture Notes in Computer Science

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Beskrivning

This book constitutes the refereed proceedings of the 23rd International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, CPAIOR 2026, held in Rabat, Morocco, during May 26–29, 2026.The 37 full papers included in this volume were carefully reviewed and selected from 92 submissions. They focus on new ideas, techniques and applications in Constraint Programming, Artificial Intelligence, and Operations Research.

Produktinformation

Utgivningsdatum:2026-07-05
Mått:155 x 235 x undefined mm
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:650
Förlag:Springer Nature Switzerland AG
ISBN:9783032272416

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT
Programmeringsböcker inom Data och IT

Innehållsförteckning

.- Backbone-based Predict and Search for Pseudo-Boolean Optimization.- CORL: A Reinforcement Learning Framework for Combinatorial Optimization.- Enhancing Scalability in Distributed Flexible Flowshop Scheduling: A Hybrid RL-CP approach.- Augmenting the Filtering of the Multidimensional Knapsack Constraint using Lagrange Multipliers Alterations.- Learning to Choose Branching Rules for Nonconvex MINLPs.- Imitation-Guided World Models for Multi-Agent Train Rescheduling.- Clustering for Relaxed and Restricted Decision Diagram Bounds: When It Works and Why.- A Scalable Learning Approach for Efficient Computation of Independent Set and Cover Variants.- Computing Minimax Regret by Bounding the Weight Space From Within and Without.- Resolution Meets Cutting Planes: Introducing Hypercube Linear Resolution.- Transit Network Design with Two-Level Demand Uncertainties: A Machine Learning and Contextual Stochastic Optimization Framework.- Improving the planning of stochastic tasks with availability windows using prediction.- Resource-Constrained Project Scheduling Problem with Transfer Times using Secondary Resources with Instant Self-transfers.- No-Opponent-Cycle Propagators for Solving Parity Games.- Contextual Preference Distribution Learning.- Multi-objective Maximum Satisfiability by Single-objective Implicit Hitting Set Optimization.- Cost-Minimal Parameter Correction Subsets for Unsatisfiable Constraint Problems.- Towards Solving Polynomial-Objective Integer Programming with Hypergraph Neural Networks.- Integrating Bayesian Optimization and Combinatorial Optimization for LEO Satellite Constellation Design.- Explainability Results for the Rotating Workforce Scheduling Problem.- Generalised Arc Consistency via the Synchronised Product of Finite Automata with respect to a Constraint.- Open-Source Implementation of Slack Induction by String Removals for Routing and Orienteering Problems.- Heuristic Multiobjective Discrete Optimization using Restricted Decision Diagrams.- Exact Synthetic Populations for Scalable Societal and Market Modeling.- Optimization over Trained (and Sparse) Neural Networks: A Surrogate within a Surrogate.- Introducing automatically derived subproblem relaxations in a logic-based Benders decomposition solver.- A hybrid learning-based matheuristic to solve the vehicle routing problem with stochastic demands.- Scheduling Data Transfers with Priorities for Space Missions.- BaB-PoNN: A Bit-Exact Branch-and-Bound Framework for Verified Robustness of Posit Neural Networks.- Probing Features for Automatic Algorithm Selection for Pseudo- Boolean Optimization.- Singleton Node Consistency for Quadratic Assignment Problems in Cost Function Networks.- Exact Certification of Data-Poisoning Attacks Using Mixed-Integer Programming.- From Historical Templates to Hints: Selecting Effective Initializations for the Rack-Loading Problem.- GPU-Acceleration for DIDP: Design and Application to TSPTW.- Optimization over Trained Neural Networks: Going Large with Gradient-Based Algorithms.- Graph Isomorphism: Mixed-Integer Convex Optimization from First-Order Methods.- Large Neighborhood Search meets Iterative Neural Constraint Heuristics.