Reinforcement Learning Approach to Stochastic Vehicle Routing Problem With Correlated Demands

Reinforcement Learning Approach to Stochastic Vehicle Routing Problem With Correlated Demands

We present a novel end-to-end framework for solving the Vehicle Routing Problem with stochastic demands (VRPSD) using Reinforcement Learning (RL). Our formulation incorporates the correlation between stochastic demands through other observable stochastic variables, thereby offering an experimental d...

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Bibliographic Details
Main Authors: Zangir Iklassov, Ikboljon Sobirov, Ruben Solozabal, Martin Takac
Format: Article
Language:English
Published: IEEE 2023-01-01
Series:IEEE Access
Subjects:
Reinforcement learning
stopchastic optimization
vehicle routing problem
Online Access:https://ieeexplore.ieee.org/document/10223206/

Internet

https://ieeexplore.ieee.org/document/10223206/

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