12 citations · 20 across the 13 of their papers we have counts for
13 papers
Reinforcement Learning Enhanced LLM Agents for Complex Vehicle Routing Problems
Yi Chen, Zikang Yu, Jiahai Wang +3
Vehicle Routing Problems (VRPs) are fundamental combinatorial optimization problems with widespread applications in various scenarios. The advanced optimization solvers can effecti…
Edge-aware Decoding for Neural Asymmetric Routing
Li Liang, Jinbiao Chen, Zizhen Zhang
Neural asymmetric routing models increasingly encode directionality through matrix representations and asymmetry-aware attention. The final routing action, however, is not a node i…
UCPO: A Universal Constrained Combinatorial Optimization Method via Preference Optimization
Zhanhong Fang, Debing Wang, Jinbiao Chen +2
Neural solvers have demonstrated remarkable success in combinatorial optimization, often surpassing traditional heuristics in speed, solution quality, and generalization. However,…
BOPO: Neural Combinatorial Optimization via Best-anchored and Objective-guided Preference Optimization
Zijun Liao, Jinbiao Chen, Debing Wang +2
Neural Combinatorial Optimization (NCO) has emerged as a promising approach for NP-hard problems. However, prevailing RL-based methods suffer from low sample efficiency due to spar…
Efficient Meta Neural Heuristic for Multi-Objective Combinatorial Optimization
Jinbiao Chen, Jiahai Wang, Zizhen Zhang +3
Recently, neural heuristics based on deep reinforcement learning have exhibited promise in solving multi-objective combinatorial optimization problems (MOCOPs). However, they are s…
Neural Multi-Objective Combinatorial Optimization with Diversity Enhancement
Jinbiao Chen, Zizhen Zhang, Zhiguang Cao +4
Most of existing neural methods for multi-objective combinatorial optimization (MOCO) problems solely rely on decomposition, which often leads to repetitive solutions for the respe…
MODRL/D-EL: Multiobjective Deep Reinforcement Learning with Evolutionary Learning for Multiobjective Optimization
Yongxin Zhang, Jiahai Wang, Zizhen Zhang +1
Learning-based heuristics for solving combinatorial optimization problems has recently attracted much academic attention. While most of the existing works only consider the single…
Meta-Learning-Based Deep Reinforcement Learning for Multiobjective Optimization Problems
Zizhen Zhang, Zhiyuan Wu, Hang Zhang +1
Deep reinforcement learning (DRL) has recently shown its success in tackling complex combinatorial optimization problems. When these problems are extended to multiobjective ones, i…
MODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization
Hong Wu, Jiahai Wang, Zizhen Zhang
Recently, a deep reinforcement learning method is proposed to solve multiobjective optimization problem. In this method, the multiobjective optimization problem is decomposed to a…
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing Problems
Bo Peng, Jiahai Wang, Zizhen Zhang
Recent researches show that machine learning has the potential to learn better heuristics than the one designed by human for solving combinatorial optimization problems. The deep n…
Collective Mobile Sequential Recommendation: A Recommender System for Multiple Taxicabs
Tongwen Wu, Zizhen Zhang, Yanzhi Li +1
Mobile sequential recommendation was originally designed to find a promising route for a single taxicab. Directly applying it for multiple taxicabs may cause an excessive overlap o…
A Tabu Search Algorithm for the Multi-period Inspector Scheduling Problem
Hu Qin, Zizhen Zhang, Yubin Xie +1
This paper introduces a multi-period inspector scheduling problem (MPISP), which is a new variant of the multi-trip vehicle routing problem with time windows (VRPTW). In the MPISP,…
An Enhanced Branch-and-bound Algorithm for the Talent Scheduling Problem
Zizhen Zhang, Hu Qin, Xiaocong Liang +1
The talent scheduling problem is a simplified version of the real-world film shooting problem, which aims to determine a shooting sequence so as to minimize the total cost of the a…