Search Results for author:
Found 9 papers, 7 papers with code
USPR: Learning a Unified Solver for Profiled Routing
The Profiled Vehicle Routing Problem (PVRP) extends the classical VRP by incorporating vehicle-client-specific preferences and constraints, reflecting real-world requirements such as zone restrictions and service-level preferences.
Neural Combinatorial Optimization for Real-World Routing
First, we introduce a new, openly available dataset with real-world data containing a diverse dataset of locations, distances, and duration matrices from 100 cities, considering realistic settings with actual routing distances and durations obtained from Open Source Routing Machine (OSRM).
Neural Genetic Search in Discrete Spaces
Effective search methods are crucial for improving the performance of deep generative models at test time.
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems
CAMP employs a specialized attention-based encoder architecture to embed profiled client embeddings in parallel for each vehicle profile.
Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization
To address these issues, we propose Parallel AutoRegressive Combinatorial Optimization (PARCO), a reinforcement learning framework designed to construct high-quality solutions for multi-agent combinatorial tasks efficiently.
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference and parallel stochastic search.
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
To fill this gap, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 23 state-of-the-art methods and more than 20 CO problems.
Equity-Transformer: Solving NP-hard Min-Max Routing Problems as Sequential Generation with Equity Context
Notably, our method achieves significant reductions of runtime, approximately 335 times, and cost values of about 53\% compared to a competitive heuristic (LKH3) in the case of 100 vehicles with 1, 000 cities of mTSP.
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
First, SML transforms the context embedding for subsequent adaptation of SAGE based on scale information.
