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--- |
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license: mit |
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language: |
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- en |
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library_name: tensorflowtts |
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pipeline_tag: reinforcement-learning |
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--- |
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Model used for solving Capacitated Vehicle Routing Problem (CVRP). The CVRP is a variant of the vehicle routing problem (VRP) in which vehicles have a limited carrying capacity and must visit a set of customer locations to deliver or collect items. |
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Model is based on GitHub repo [HERE](https://github.com/d-eremeev/ADM-VRP), and was used for medium.com article **"Vaccine Supply Chain Optimization with AI-Powered Capacitated Vehicle Routing Problem(CVRP)"**. |
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**Dynamic Attention Model (AM-D) Approach**: |
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After vehicle returns to depot, the remaining nodes could be considered as a new (smaller) instance (graph) to be solved. |
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Idea: update embedding of the remaining nodes using encoder after agent arrives back to depot. |
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**Implementation**: |
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- Force RL agent to wait for others once it arrives to . |
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- When every agent is in depot, apply encoder with mask to the whole batch. |
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If you want to train your own model with AM-D approach: |
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1. Prepare data (depo location Lat/Long, nodes location Lat/Long and capacity of the vehicles) |
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2. Transform data with TensorFlow tranform_to_tensor [Here is Gist](https://gist.github.com/PiotrKrosniak/f488eea5b31a2d61e21554041a1ee59b) example with transforming from Pandas Data Frame |
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3. Train the model using |
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