Publications
Talk · International Conference on Electronics, Computer and Computation · June 1, 2023
Deep Reinforcement Learning for Stochastic Dynamic Vehicle Routing Problem
Taukekhan Mustakhov, Yernar Akhmetbek, Aigerim Bogyrbayeva
Abstract
In this study, we propose solving the Stochastic Dynamic Vehicle Routing Problem with Deep Reinforcement Learning. We propose a fully attention-based model consisting of a dynamic encoder and decoder to learn the routing policy of a single vehicle, where the customer orders appear over time. To train the model, we utilize a reinforcement learning setting, in which a reward signal guides an agent to select nodes to be visited. The computational analyses demonstrate the suggested strategy's superiority over the chosen benchmark algorithms.
Vehicle Routing Optimization MethodsReinforcement Learning in RoboticsUrban and Freight Transport Logisticsdeep reinforcement learningattention-based modelsnode selection
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