5 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,…
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 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…