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 …
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 …
Learning to search feasible and infeasible regions of routing problems with flexible neural k-opt
In this paper, we present Neural k-Opt (NeuOpt), a novel learning-to-search (L2S) solver for
routing problems. It learns to perform flexible k-opt exchanges based on a tailored action …
routing problems. It learns to perform flexible k-opt exchanges based on a tailored action …
Energy-optimal routing for electric vehicles using deep reinforcement learning with transformer
This paper presents an end-to-end deep reinforcement learning (DRL) approach aimed at
efficiently determining energy-optimal routes for a group of electric logistic vehicles, with the …
efficiently determining energy-optimal routes for a group of electric logistic vehicles, with the …
Meta-learning-based deep reinforcement learning for multiobjective optimization problems
Z Zhang, Z Wu, H Zhang, J Wang - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Deep reinforcement learning (DRL) has recently shown its success in tackling complex
combinatorial optimization problems. When these problems are extended to multiobjective …
combinatorial optimization problems. When these problems are extended to multiobjective …
A reinforcement learning-variable neighborhood search method for the capacitated vehicle routing problem
Finding the best sequence of local search operators that yields the optimal performance of
Variable Neighborhood Search (VNS) is an important open research question in the field of …
Variable Neighborhood Search (VNS) is an important open research question in the field of …