Reinforcement learning for logistics and supply chain management: Methodologies, state of the art, and future opportunities
With advances in technologies, data science techniques, and computing equipment, there
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
has been rapidly increasing interest in the applications of reinforcement learning (RL) to …
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 …
UAV-enabled secure communications by multi-agent deep reinforcement learning
Unmanned aerial vehicles (UAVs) can be employed as aerial base stations to support
communication for the ground users (GUs). However, the aerial-to-ground (A2G) channel …
communication for the ground users (GUs). However, the aerial-to-ground (A2G) channel …
Data-driven trajectory quality improvement for promoting intelligent vessel traffic services in 6G-enabled maritime IoT systems
Future generation communication systems, such as 5G and 6G wireless systems, exploit the
combined satellite-terrestrial communication infrastructures to extend network coverage and …
combined satellite-terrestrial communication infrastructures to extend network coverage and …
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 …
Heterogeneous attentions for solving pickup and delivery problem via deep reinforcement learning
Recently, there is an emerging trend to apply deep reinforcement learning to solve the
vehicle routing problem (VRP), where a learnt policy governs the selection of next node for …
vehicle routing problem (VRP), where a learnt policy governs the selection of next node for …
Optimizing task assignment for reliable blockchain-empowered federated edge learning
A rapid-growing machine learning technique called federated edge learning has emerged to
allow a massive number of edge devices (eg smart phones) to collaboratively train globally …
allow a massive number of edge devices (eg smart phones) to collaboratively train globally …
Mean field deep reinforcement learning for fair and efficient UAV control
Unmanned aerial vehicles (UAVs) can provide flexible network coverage services. UAVs
can be applied in a large number of scenarios, such as emergency communication and …
can be applied in a large number of scenarios, such as emergency communication and …
Wifi-based indoor robot positioning using deep fuzzy forests
Addressing the positioning problem of a mobile robot remains challenging to date despite
many years of research. Indoor robot positioning strategies developed in the literature either …
many years of research. Indoor robot positioning strategies developed in the literature either …
Optimization of ride-sharing with passenger transfer via deep reinforcement learning
D Wang, Q Wang, Y Yin, TCE Cheng - Transportation Research Part E …, 2023 - Elsevier
With the emergence of the sharing economy and the rapid growth of mobile communications
technologies, many novel sharing service models have been developed stemming from ride …
technologies, many novel sharing service models have been developed stemming from ride …