13 citations · 24 across the 11 of their papers we have counts for
11 papers
LaT: LLM-as-Trainer for Multi-Task Vehicle Routing Solvers
Yang Wang, Ya-Hui Jia, Wei-Neng Chen +3
Multi-task neural solvers aim to handle multiple Vehicle Routing Problem (VRP) variants within a unified model, avoiding separate training for each constraint combination. However,…
Learning Scenario Reduction for Two-Stage Robust Optimization with Discrete Uncertainty
Tianjue Lin, Jianan Zhou, Jieyi Bi +4
Two-Stage Robust Optimization (2RO) with discrete uncertainty is challenging, often rendering exact solutions prohibitive. Scenario reduction alleviates this issue by selecting a s…
RESCHED: Rethinking Flexible Job Shop Scheduling from a Transformer-based Architecture with Simplified States
Xiangjie Xiao, Cong Zhang, Wen Song +1
Neural approaches to the Flexible Job Shop Scheduling Problem (FJSP), particularly those based on deep reinforcement learning (DRL), have gained growing attention in recent years.…
Towards Efficient Constraint Handling in Neural Solvers for Routing Problems
Jieyi Bi, Zhiguang Cao, Jianan Zhou +5
Neural solvers have achieved impressive progress in addressing simple routing problems, particularly excelling in computational efficiency. However, their advantages under complex…
A General Neural Backbone for Mixed-Integer Linear Optimization via Dual Attention
Peixin Huang, Yaoxin Wu, Yining Ma +3
Mixed-integer linear programming (MILP) is a foundational framework for combinatorial optimization across science and engineering, but remains hard to solve at scale due to NP-hard…
Learning to Handle Complex Constraints for Vehicle Routing Problems
Jieyi Bi, Yining Ma, Jianan Zhou +4
Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feas…
Collaboration! Towards Robust Neural Methods for Routing Problems
Jianan Zhou, Yaoxin Wu, Zhiguang Cao +3
Despite enjoying desirable efficiency and reduced reliance on domain expertise, existing neural methods for vehicle routing problems (VRPs) suffer from severe robustness issues --…
MVMoE: Multi-Task Vehicle Routing Solver with Mixture-of-Experts
Jianan Zhou, Zhiguang Cao, Yaoxin Wu +4
Learning to solve vehicle routing problems (VRPs) has garnered much attention. However, most neural solvers are only structured and trained independently on a specific problem, mak…
Cross-Problem Learning for Solving Vehicle Routing Problems
Zhuoyi Lin, Yaoxin Wu, Bangjian Zhou +4
Existing neural heuristics often train a deep architecture from scratch for each specific vehicle routing problem (VRP), ignoring the transferable knowledge across different VRP va…
Learning Topological Representations with Bidirectional Graph Attention Network for Solving Job Shop Scheduling Problem
Cong Zhang, Zhiguang Cao, Yaoxin Wu +2
Existing learning-based methods for solving job shop scheduling problems (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningf…
Automated Reinforcement Learning: An Overview
Reza Refaei Afshar, Joaquin Vanschoren, Uzay Kaymak +4
Reinforcement Learning and, recently, Deep Reinforcement Learning are popular methods for solving sequential decision-making problems modeled as Markov Decision Processes. RL model…