Search Results for author:
Found 3 papers, 0 papers with code
Deep Reinforcement Learning for Multi-Truck Vehicle Routing Problems with Multi-Leg Demand Routes
With the goal of making deep RL a viable strategy for real-world industrial-scale supply chain logistics, we develop new extensions to existing encoder-decoder attention models which allow them to handle multiple trucks and multi-leg routing requirements.
Reinforcement Learning for Multi-Truck Vehicle Routing Problems
With the aim of making reinforcement learning a viable technique for supply chain optimization, we develop new extensions to encoder-decoder models for vehicle routing that allow for complex supply chains using classical computing today and quantum computing in the future.
Quantum Neural Networks for a Supply Chain Logistics Application
We use reinforcement learning with neural networks with embedded quantum circuits.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
