Glop: Learning global partition and local construction for solving large-scale routing problems in real-time
The recent end-to-end neural solvers have shown promise for small-scale routing problems
but suffered from limited real-time scaling-up performance. This paper proposes GLOP …
but suffered from limited real-time scaling-up performance. This paper proposes GLOP …
Neural Combinatorial Optimization Algorithms for Solving Vehicle Routing Problems: A Comprehensive Survey with Perspectives
Although several surveys on Neural Combinatorial Optimization (NCO) solvers specifically
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
designed to solve Vehicle Routing Problems (VRPs) have been conducted. These existing …
A novel fractional-order memristive Hopfield neural network for traveling salesman problem and its FPGA implementation
X Li, X Yang, X Ju - Neural Networks, 2024 - Elsevier
This paper proposes a novel fractional-order memristive Hopfield neural network (HNN) to
address traveling salesman problem (TSP). Fractional-order memristive HNN can efficiently …
address traveling salesman problem (TSP). Fractional-order memristive HNN can efficiently …
Balanced influence maximization in social networks based on deep reinforcement learning
Balanced influence maximization aims to balance the influence maximization of multiple
different entities in social networks and avoid the emergence of filter bubbles and echo …
different entities in social networks and avoid the emergence of filter bubbles and echo …
Reinforcement learning-based nonautoregressive solver for traveling salesman problems
The traveling salesman problem (TSP) is a well-known combinatorial optimization problem
(COP) with broad real-world applications. Recently, neural networks (NNs) have gained …
(COP) with broad real-world applications. Recently, neural networks (NNs) have gained …
A deep reinforcement learning algorithm framework for solving multi-objective traveling salesman problem based on feature transformation
S Zhao, S Gu - Neural Networks, 2024 - Elsevier
As a special type of multi-objective combinatorial optimization problems (MOCOPs), the
multi-objective traveling salesman problem (MOTSP) plays an important role in practical …
multi-objective traveling salesman problem (MOTSP) plays an important role in practical …
A deep reinforcement learning with dynamic spatio-temporal graph model for solving urban logistics delivery planning problems
Y Li, Q Guan, J Gu, X Jiang - International Journal of Digital Earth, 2024 - Taylor & Francis
The urban logistics delivery planning problems are a crucial component of urban spatial
decision analysis. Most studies typically focus on traditional urban logistics delivery planning …
decision analysis. Most studies typically focus on traditional urban logistics delivery planning …
A lightweight CNN-transformer model for learning traveling salesman problems
M Jung, J Lee, J Kim - Applied Intelligence, 2024 - Springer
Several studies have attempted to solve traveling salesman problems (TSPs) using various
deep learning techniques. Among them, Transformer-based models show state-of-the-art …
deep learning techniques. Among them, Transformer-based models show state-of-the-art …
Deep sensitivity analysis for objective-oriented combinatorial optimization
Pathogen control is a critical aspect of modern poultry farming, providing important benefits
for both public health and productivity. Effective poultry management measures to reduce …
for both public health and productivity. Effective poultry management measures to reduce …
A hierarchical deep reinforcement learning method for solving urban route planning problems under large-scale customers and real-time traffic conditions
As urbanization and economic growth advance, large-scale customers and real-time traffic
conditions have become crucial factors in urban route planning. Deep reinforcement …
conditions have become crucial factors in urban route planning. Deep reinforcement …