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 …
基于深度强化学习的组合优化研究进展
李凯文， 张涛， 王锐， 覃伟健， 贺惠晖， 黄鸿 - 自动化学报, 2021 - aas.net.cn
组合优化问题广泛存在于国防, 交通, 工业, 生活等各个领域, 几十年来, 传统运筹优化方法是解决
组合优化问题的主要手段, 但随着实际应用中问题规模的不断扩大, 求解实时性的要求越来越高 …
组合优化问题的主要手段, 但随着实际应用中问题规模的不断扩大, 求解实时性的要求越来越高 …
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 …
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 …
Analytics and machine learning in vehicle routing research
The Vehicle Routing Problem (VRP) is one of the most intensively studied combinatorial
optimisation problems for which numerous models and algorithms have been proposed. To …
optimisation problems for which numerous models and algorithms have been proposed. To …
How good is neural combinatorial optimization? A systematic evaluation on the traveling salesman problem
S Liu, Y Zhang, K Tang, X Yao - IEEE Computational …, 2023 - ieeexplore.ieee.org
Traditional solvers for tackling combinatorial optimization (CO) problems are usually
designed by human experts. Recently, there has been a surge of interest in utilizing deep …
designed by human experts. Recently, there has been a surge of interest in utilizing deep …
Erdos goes neural: an unsupervised learning framework for combinatorial optimization on graphs
N Karalias, A Loukas - Advances in Neural Information …, 2020 - proceedings.neurips.cc
Combinatorial optimization (CO) problems are notoriously challenging for neural networks,
especially in the absence of labeled instances. This work proposes an unsupervised …
especially in the absence of labeled instances. This work proposes an unsupervised …
Learning large neighborhood search policy for integer programming
We propose a deep reinforcement learning (RL) method to learn large neighborhood search
(LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to …
(LNS) policy for integer programming (IP). The RL policy is trained as the destroy operator to …
Learning to solve combinatorial optimization problems on real-world graphs in linear time
I Drori, A Kharkar, WR Sickinger, B Kates… - 2020 19th IEEE …, 2020 - ieeexplore.ieee.org
Combinatorial optimization algorithms for graph problems are usually designed afresh for
each new problem with careful attention by an expert to the problem structure. In this work …
each new problem with careful attention by an expert to the problem structure. In this work …
Reinforcement learning based truck-and-drone coordinated delivery
Coronavirus disease 2019 has brought a great challenge to the supply of daily necessities
and medical items for home-quarantined people. Considering the unmanned operation …
and medical items for home-quarantined people. Considering the unmanned operation …