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Explore the research behind optimization intelligence, from generative models and search to routing, scheduling, and multi-agent decisions.
14 publications
LPMARL: Linear Programming-based Task Assignment for Hierarchical Multi-agent Reinforcement Learning
RRNCO: Towards Real-World Routing with Neural Combinatorial Optimization
Neural Embedded Mixed-Integer Optimization for Location-Routing Problems
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Neural Genetic Search in Discrete Spaces
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Generative Flows on Synthetic Pathway for Drug Design
Genetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems
RouteFinder: Towards Foundation Models for Vehicle Routing Problems
HiMAP: Learning Heuristics-Informed Policies for Large-Scale Multi-Agent Pathfinding
Equity-Transformer: Solving NP-hard Min-max Routing Problems as Sequential Generation with Equity Context
A Neural Separation Algorithm for the Rounded Capacity Inequalities
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Publication records are drawn from Omelet’s research archive. Follow each paper for its full bibliography and publication details.