An overview and experimental study of learning-based optimization algorithms for the vehicle routing problem
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
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 …
Neural combinatorial optimization with heavy decoder: Toward large scale generalization
Neural combinatorial optimization (NCO) is a promising learning-based approach for solving
challenging combinatorial optimization problems without specialized algorithm design by …
challenging combinatorial optimization problems without specialized algorithm design by …
Flexible job-shop scheduling via graph neural network and deep reinforcement learning
Recently, deep reinforcement learning (DRL) has been applied to learn priority dispatching
rules (PDRs) for solving complex scheduling problems. However, the existing works face …
rules (PDRs) for solving complex scheduling problems. However, the existing works face …
Neurolkh: Combining deep learning model with lin-kernighan-helsgaun heuristic for solving the traveling salesman problem
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
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 …
Reinforcement learning for solving the vehicle routing problem
We present an end-to-end framework for solving the Vehicle Routing Problem (VRP) using
reinforcement learning. In this approach, we train a single policy model that finds near …
reinforcement learning. In this approach, we train a single policy model that finds near …
Deep reinforcement learning for transportation network combinatorial optimization: A survey
Q Wang, C Tang - Knowledge-Based Systems, 2021 - Elsevier
Traveling salesman and vehicle routing problems with their variants, as classic
combinatorial optimization problems, have attracted considerable attention for decades of …
combinatorial optimization problems, have attracted considerable attention for decades of …
Multi-decoder attention model with embedding glimpse for solving vehicle routing problems
We present a novel deep reinforcement learning method to learn construction heuristics for
vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM) …
vehicle routing problems. In specific, we propose a Multi-Decoder Attention Model (MDAM) …
Deep policy dynamic programming for vehicle routing problems
Routing problems are a class of combinatorial problems with many practical applications.
Recently, end-to-end deep learning methods have been proposed to learn approximate …
Recently, end-to-end deep learning methods have been proposed to learn approximate …