Integrating Machine Learning into Vehicle Routing Problem: Methods and Applications
R Shahbazian, LDP Pugliese, F Guerriero… - IEEE …, 2024 - ieeexplore.ieee.org
The vehicle routing problem (VRP) and its variants have been intensively studied by the
operational research community. The existing surveys and the majority of the published …
operational research community. The existing surveys and the majority of the published …
DPN: Decoupling Partition and Navigation for Neural Solvers of Min-max Vehicle Routing Problems
The min-max vehicle routing problem (min-max VRP) traverses all given customers by
assigning several routes and aims to minimize the length of the longest route. Recently …
assigning several routes and aims to minimize the length of the longest route. Recently …
A Scalable and Adaptable Supervised Learning Approach for Solving the Traveling Salesman Problems
The Traveling Salesman Problem (TSP) is a well-known combinatorial optimization problem
that has attracted extensive research efforts in developing exact methods and heuristics …
that has attracted extensive research efforts in developing exact methods and heuristics …
UDC: A Unified Neural Divide-and-Conquer Framework for Large-Scale Combinatorial Optimization Problems
Single-stage neural combinatorial optimization solvers have achieved near-optimal results
on various small-scale combinatorial optimization (CO) problems without needing expert …
on various small-scale combinatorial optimization (CO) problems without needing expert …
A deep learning Attention model to solve the Vehicle Routing Problem and the Pick-up and Delivery Problem with Time Windows
B Rabecq, R Chevrier - arXiv preprint arXiv:2212.10399, 2022 - arxiv.org
SNCF, the French public train company, is experimenting to develop new types of
transportation services by tackling vehicle routing problems. While many deep learning …
transportation services by tackling vehicle routing problems. While many deep learning …
Centralized Deep Reinforcement Learning Method for Dynamic Multi-Vehicle Pickup and Delivery Problem With Crowdshippers
Crowdshipping problem can be challenging as the platform are continuously but
sporadically receiving crowdshippers and delivery tasks with heterogeneous origin and …
sporadically receiving crowdshippers and delivery tasks with heterogeneous origin and …
Where to go: Agent guidance with deep reinforcement learning in a city-scale online ride-hailing service
Online ride-hailing services have become a prevalent transportation system across the
world. In this paper, we study a challenging problem of how to direct vacant taxis around a …
world. In this paper, we study a challenging problem of how to direct vacant taxis around a …
Solving pickup and drop-off problem using hybrid pointer networks with deep reinforcement learning
In this study, we propose a general method for tackling the Pickup and Drop-off Problem
(PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our …
(PDP) using Hybrid Pointer Networks (HPNs) and Deep Reinforcement Learning (DRL). Our …
Prioritized League Reinforcement Learning for Large-Scale Heterogeneous Multiagent Systems
Q Fu, Z Pu, M Chen, T Qiu, J Yi - arXiv preprint arXiv:2403.18057, 2024 - arxiv.org
Large-scale heterogeneous multiagent systems feature various realistic factors in the real
world, such as agents with diverse abilities and overall system cost. In comparison to …
world, such as agents with diverse abilities and overall system cost. In comparison to …
Too Big, so Fail?--Enabling Neural Construction Methods to Solve Large-Scale Routing Problems
JK Falkner, L Schmidt-Thieme - arXiv preprint arXiv:2309.17089, 2023 - arxiv.org
In recent years new deep learning approaches to solve combinatorial optimization
problems, in particular NP-hard Vehicle Routing Problems (VRP), have been proposed. The …
problems, in particular NP-hard Vehicle Routing Problems (VRP), have been proposed. The …