13 papers
Learning to Solve Compositional Geometry Routing Problems
Mingfeng Fan, Jianan Zhou, Jiaqi Cheng +3
We study the Compositional Geometry Routing Problem (CGRP), a unified superclass of traditional routing problems that covers point-only, line-only, area-only, and arbitrary hybrid…
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…
Enhancing Cross-Problem Vehicle Routing via Federated Learning
Xiangchi Meng, Jianan Zhou, Jie Gao +4
Vehicle routing problems (VRPs) constitute a core optimization challenge in modern logistics and supply chain management. The recent neural combinatorial optimization (NCO) has dem…
PyVRP: LLM-Driven Metacognitive Heuristic Evolution for Hybrid Genetic Search in Vehicle Routing Problems
Manuj Malik, Jianan Zhou, Shashank Reddy Chirra +1
Designing high-performing metaheuristics for NP-hard combinatorial optimization problems, such as the Vehicle Routing Problem (VRP), remains a significant challenge, often requirin…
Generalizable Heuristic Generation Through LLMs with Meta-Optimization
Yiding Shi, Jianan Zhou, Wen Song +4
Heuristic design with large language models (LLMs) has emerged as a promising approach for tackling combinatorial optimization problems (COPs). However, existing approaches often r…
Preference-Driven Multi-Objective Combinatorial Optimization with Conditional Computation
Mingfeng Fan, Jianan Zhou, Yifeng Zhang +3
Recent deep reinforcement learning methods have achieved remarkable success in solving multi-objective combinatorial optimization problems (MOCOPs) by decomposing them into multipl…