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Using Constraint Programming to Improve Grocery Picking Efficiency at Loblaws

Offered By: Toronto Machine Learning Series (TMLS) via YouTube

Tags

Constraint Programming Courses Data Science Courses E-commerce Courses Supply Chain Management Courses Operational Efficiency Courses

Course Description

Overview

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Explore a data science solution for improving grocery picking efficiency in this 49-minute conference talk from the Toronto Machine Learning Series. Discover how Loblaw Digital tackles the challenge of reducing cost-to-serve for online grocery orders through innovative constraint programming. Learn about the strategy to maximize items picked per cart and minimize walking distance, as Mathieu Sylvestre, Data Scientist at Loblaw Companies Ltd., presents a high-level overview of their current solution. Gain insights into how constraint programming is applied to optimize the fulfillment process for billions of dollars worth of online grocery sales across hundreds of stores.

Syllabus

Using Constraint Programming to Improve Grocery Picking Efficiency at Loblaws


Taught by

Toronto Machine Learning Series (TMLS)

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