23 citations · 32 across the 9 of their papers we have counts for
9 papers
USPR: Learning a Unified Solver for Profiled Routing
Chuanbo Hua, Federico Berto, Zhikai Zhao +3
The Profiled Vehicle Routing Problem (PVRP) extends the classical VRP by incorporating vehicle-client-specific preferences and constraints, reflecting real-world requirements such…
RRNCO: Towards Real-World Routing with Neural Combinatorial Optimization
Jiwoo Son, Zhikai Zhao, Federico Berto +4
The practical deployment of Neural Combinatorial Optimization (NCO) for Vehicle Routing Problems (VRPs) is hindered by a critical sim-to-real gap. This gap stems not only from trai…
CAMP: Collaborative Attention Model with Profiles for Vehicle Routing Problems
Chuanbo Hua, Federico Berto, Jiwoo Son +3
The profiled vehicle routing problem (PVRP) is a generalization of the heterogeneous capacitated vehicle routing problem (HCVRP) in which the objective is to optimize the routes of…
Neural Genetic Search in Discrete Spaces
Hyeonah Kim, Sanghyeok Choi, Jiwoo Son +2
Effective search methods are crucial for improving the performance of deep generative models at test time. In this paper, we introduce a novel test-time search method, Neural Genet…
PARCO: Parallel AutoRegressive Models for Multi-Agent Combinatorial Optimization
Federico Berto, Chuanbo Hua, Laurin Luttmann +6
Combinatorial optimization problems involving multiple agents are notoriously challenging due to their NP-hard nature and the necessity for effective agent coordination. Despite ad…
Ant Colony Sampling with GFlowNets for Combinatorial Optimization
Minsu Kim, Sanghyeok Choi, Hyeonah Kim +3
We present the Generative Flow Ant Colony Sampler (GFACS), a novel meta-heuristic method that hierarchically combines amortized inference and parallel stochastic search. Our method…
Meta-SAGE: Scale Meta-Learning Scheduled Adaptation with Guided Exploration for Mitigating Scale Shift on Combinatorial Optimization
Jiwoo Son, Minsu Kim, Hyeonah Kim +1
This paper proposes Meta-SAGE, a novel approach for improving the scalability of deep reinforcement learning models for combinatorial optimization (CO) tasks. Our method adapts pre…
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
Federico Berto, Chuanbo Hua, Junyoung Park +30
Combinatorial optimization (CO) is fundamental to several real-world applications, from logistics and scheduling to hardware design and resource allocation. Deep reinforcement lear…
Equity-Transformer: Solving NP-hard Min-Max Routing Problems as Sequential Generation with Equity Context
Jiwoo Son, Minsu Kim, Sanghyeok Choi +2
Min-max routing problems aim to minimize the maximum tour length among multiple agents by having agents conduct tasks in a cooperative manner. These problems include impactful real…