强化学习在车辆路径问题中的研究综述.
牛鹏飞， 王晓峰， 芦磊， 张九龙 - Journal of Computer …, 2022 - search.ebscohost.com
车辆路径问题是物流运输优化中的核心问题, 目的是在满足顾客需求下得到一条最低成本的车辆
路径规划. 但随着物流运输规模的不断增大, 车辆路径问题求解难度增加, 并且对实时性要求也 …
路径规划. 但随着物流运输规模的不断增大, 车辆路径问题求解难度增加, 并且对实时性要求也 …
Dual-decoder attention model in hierarchical reinforcement framework for dynamic crowd logistics problem with batch-matching
Dynamic routing problem with crowd-sourced driver can be challenging as the dynamically
arrived crowd drivers are utilized to meet the dynamically placed customer demand. To …
arrived crowd drivers are utilized to meet the dynamically placed customer demand. To …
Collaborative Delivery Optimization With Multiple Drones via Constrained Hybrid Pointer Network
Drone participation in truck delivery is a potential booster for the last-mile logistics system,
which has been an emerging hot research field. Among that, how to arrange a fleet of …
which has been an emerging hot research field. Among that, how to arrange a fleet of …
Dray-Q: Demand-dependent trailer repositioning using deep reinforcement learning
H Aghazadeh, Y Wang, S Sun, X Wang - Transportation Research Part C …, 2024 - Elsevier
Trailer repositioning in drayage operations is a crucial element in the efficient transportation
of goods between global commerce and local communities. Prior research has made two …
of goods between global commerce and local communities. Prior research has made two …
Pareto Improver: Learning Improvement Heuristics for Multi-Objective Route Planning
As a research hotspot across logistics, operations research, and artificial intelligence, route
planning has become a key technology for intelligent transportation systems. Recently, data …
planning has become a key technology for intelligent transportation systems. Recently, data …
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 …
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 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 …
Multi-Task Learning for Routing Problem with Cross-Problem Zero-Shot Generalization
Vehicle routing problems (VRPs), which can be found in numerous real-world applications,
have been an important research topic for several decades. Recently, the neural …
have been an important research topic for several decades. Recently, the neural …