no code implementations • 27 Feb 2024 • Cong Zhang, Zhiguang Cao, Yaoxin Wu, Wen Song, Jing Sun
Existing learning-based methods for solving job shop scheduling problem (JSSP) usually use off-the-shelf GNN models tailored to undirected graphs and neglect the rich and meaningful topological structures of disjunctive graphs (DGs).
1 code implementation • 31 May 2023 • Jianan Zhou, Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to the less reliance on hand-crafted rules.
1 code implementation • 27 Feb 2023 • Jianan Zhou, Yaoxin Wu, Zhiguang Cao, Wen Song, Jie Zhang, Zhenghua Chen
Dispatching vehicle fleets to serve flights is a key task in airport ground handling (AGH).
1 code implementation • 20 Nov 2022 • Cong Zhang, Zhiguang Cao, Wen Song, Yaoxin Wu, Jie Zhang
Recent studies in using deep reinforcement learning (DRL) to solve Job-shop scheduling problems (JSSP) focus on construction heuristics.
1 code implementation • 13 Sep 2022 • Rongkai Zhang, Cong Zhang, Zhiguang Cao, Wen Song, Puay Siew Tan, Jie Zhang, Bihan Wen, Justin Dauwels
We propose a manager-worker framework based on deep reinforcement learning to tackle a hard yet nontrivial variant of Travelling Salesman Problem (TSP), \ie~multiple-vehicle TSP with time window and rejections (mTSPTWR), where customers who cannot be served before the deadline are subject to rejections.
2 code implementations • 25 Apr 2022 • Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Hongliang Guo, YueJiao Gong, Yeow Meng Chee
We present an efficient Neural Neighborhood Search (N2S) approach for pickup and delivery problems (PDPs).
1 code implementation • NeurIPS 2021 • Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
We then design a neural network to learn policies for each variable in parallel, trained by a customized actor-critic algorithm.
1 code implementation • NeurIPS 2021 • Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
We present NeuroLKH, a novel algorithm that combines deep learning with the strong traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem.
1 code implementation • 6 Oct 2021 • Jingwen Li, Yining Ma, Ruize Gao, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang
To solve those problems, we propose a DRL method based on the attention mechanism with a vehicle selection decoder accounting for the heterogeneous fleet constraint and a node selection decoder accounting for the route construction, which learns to construct a solution by automatically selecting both a vehicle and a node for this vehicle at each step.
no code implementations • 6 Oct 2021 • Jingwen Li, Liang Xin, Zhiguang Cao, Andrew Lim, Wen Song, Jie Zhang
In particular, the heterogeneous attention mechanism specifically prescribes attentions for each role of the nodes while taking into account the precedence constraint, i. e., the pickup node must precede the pairing delivery node.
2 code implementations • NeurIPS 2021 • Yining Ma, Jingwen Li, Zhiguang Cao, Wen Song, Le Zhang, Zhenghua Chen, Jing Tang
Moreover, the positional features are embedded through a novel cyclic positional encoding (CPE) method to allow Transformer to effectively capture the circularity and symmetry of VRP solutions (i. e., cyclic sequences).
no code implementations • ICLR 2022 • Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang
Many practical combinatorial optimization problems under uncertainty can be modeled as stochastic integer programs (SIPs), which are extremely challenging to solve due to the high complexity.
no code implementations • 29 Sep 2021 • Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
Recent studies show that deep neural networks can be trained to learn good heuristics for various Combinatorial Optimization Problems (COPs).
1 code implementation • 30 Jun 2021 • Zhizheng Zhang, Wen Song, Qiqiang Li
While deep learning has achieved great success in RUL prediction, existing methods have difficulties in processing long sequences and extracting information from the sensor and time step aspects.
1 code implementation • 19 Dec 2020 • Liang Xin, Wen Song, Zhiguang Cao, Jie Zhang
We present a novel deep reinforcement learning method to learn construction heuristics for vehicle routing problems.
5 code implementations • NeurIPS 2020 • Cong Zhang, Wen Song, Zhiguang Cao, Jie Zhang, Puay Siew Tan, Chi Xu
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling problem (JSSP).
1 code implementation • 23 Dec 2019 • Wen Song, Zhiguang Cao, Jie Zhang, Andrew Lim
In this paper, we propose a deep reinforcement learning based approach to automatically discover new variable ordering heuristics that are better adapted for a given class of CSP instances.
1 code implementation • 12 Dec 2019 • Yaoxin Wu, Wen Song, Zhiguang Cao, Jie Zhang, Andrew Lim
In this paper, we propose a deep reinforcement learning framework to learn the improvement heuristics for routing problems.