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Machine Learning Warehouse Picking Optimisation

To optimise picking batches in a warehouse with up to 8,000 SKUs, we need to split picking orders into small segements of orders which would each fit into a plastic tote. The segments of orders can be grouped together to be picked in a single zone of a warehouse, with each order split across multiple warehouse zones.

Given a map of a warehouse, SKU data, location information and order data, split a batch of orders into the most optimal configuration, based on factors including tote utilisation and route optimisation.

The expected outcome is a Sagemaker (or equivalent) model, and an API gateway endpoint which will reply to REST queries to create picking batches.

Compétences : Machine Learning (ML), AWS SageMaker, Algorithme

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Concernant l'employeur :
( 0 commentaires ) Adelaide, Australia

Nº du projet : #31922685

16 freelances font une offre moyenne de 4484 $ pour ce travail

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