An overview and experimental study of learning-based optimization algorithms for the vehicle routing problem
B Li, G Wu, Y He, M Fan… - IEEE/CAA Journal of …, 2022 - ieeexplore.ieee.org
The vehicle routing problem (VRP) is a typical discrete combinatorial optimization problem,
and many models and algorithms have been proposed to solve the VRP and its variants …
and many models and algorithms have been proposed to solve the VRP and its variants …
A review on learning to solve combinatorial optimisation problems in manufacturing
C Zhang, Y Wu, Y Ma, W Song, Z Le… - IET Collaborative …, 2023 - Wiley Online Library
An efficient manufacturing system is key to maintaining a healthy economy today. With the
rapid development of science and technology and the progress of human society, the …
rapid development of science and technology and the progress of human society, the …
Learning to dispatch for job shop scheduling via deep reinforcement learning
C Zhang, W Song, Z Cao, J Zhang… - Advances in neural …, 2020 - proceedings.neurips.cc
Priority dispatching rule (PDR) is widely used for solving real-world Job-shop scheduling
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
problem (JSSP). However, the design of effective PDRs is a tedious task, requiring a myriad …
Difusco: Graph-based diffusion solvers for combinatorial optimization
Z Sun, Y Yang - Advances in Neural Information Processing …, 2023 - proceedings.neurips.cc
Abstract Neural network-based Combinatorial Optimization (CO) methods have shown
promising results in solving various NP-complete (NPC) problems without relying on hand …
promising results in solving various NP-complete (NPC) problems without relying on hand …
Towards omni-generalizable neural methods for vehicle routing problems
J Zhou, Y Wu, W Song, Z Cao… - … Conference on Machine …, 2023 - proceedings.mlr.press
Learning heuristics for vehicle routing problems (VRPs) has gained much attention due to
the less reliance on hand-crafted rules. However, existing methods are typically trained and …
the less reliance on hand-crafted rules. However, existing methods are typically trained and …
Pomo: Policy optimization with multiple optima for reinforcement learning
YD Kwon, J Choo, B Kim, I Yoon… - Advances in Neural …, 2020 - proceedings.neurips.cc
In neural combinatorial optimization (CO), reinforcement learning (RL) can turn a deep
neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a …
neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a …
Neurolkh: Combining deep learning model with lin-kernighan-helsgaun heuristic for solving the traveling salesman problem
L Xin, W Song, Z Cao, J Zhang - Advances in Neural …, 2021 - proceedings.neurips.cc
We present NeuroLKH, a novel algorithm that combines deep learning with the strong
traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem …
traditional heuristic Lin-Kernighan-Helsgaun (LKH) for solving Traveling Salesman Problem …
Learning to iteratively solve routing problems with dual-aspect collaborative transformer
Y Ma, J Li, Z Cao, W Song, L Zhang… - Advances in Neural …, 2021 - proceedings.neurips.cc
Recently, Transformer has become a prevailing deep architecture for solving vehicle routing
problems (VRPs). However, it is less effective in learning improvement models for VRP …
problems (VRPs). However, it is less effective in learning improvement models for VRP …
A multi-action deep reinforcement learning framework for flexible Job-shop scheduling problem
K Lei, P Guo, W Zhao, Y Wang, L Qian, X Meng… - Expert Systems with …, 2022 - Elsevier
This paper presents an end-to-end deep reinforcement framework to automatically learn a
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
policy for solving a flexible Job-shop scheduling problem (FJSP) using a graph neural …
Learning combinatorial optimization on graphs: A survey with applications to networking
N Vesselinova, R Steinert, DF Perez-Ramirez… - IEEE …, 2020 - ieeexplore.ieee.org
Existing approaches to solving combinatorial optimization problems on graphs suffer from
the need to engineer each problem algorithmically, with practical problems recurring in …
the need to engineer each problem algorithmically, with practical problems recurring in …