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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Veröffentlicht in:	IEEE access 2023-01, Vol.11, p.1-1
Hauptverfasser:	Iklassov, Zangir, Sobirov, Ikboljon, Solozabal, Ruben, Takac, Martin
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Correlation Costs Environmental impact Heuristic methods Metaheuristics Noise levels Reinforcement learning Routing Stochastic processes Stopchastic Optimization Vehicle routing Vehicle Routing Problem
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