Machine Learning & AIMachine Learning & AI
Conference45min
BEGINNER

LLMs can't optimize schedules, but AI can!

This session explains why large language models are not ideal for complex scheduling and optimization tasks, and introduces metaheuristics as a better solution. Using live demos with Timefold, an open-source Java framework, attendees will learn to solve such problems and discover how LLMs and metaheuristics can work together effectively.

Tom Cools
Tom CoolsTimefold

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Tuesday, February 10, 16:15-17:00
Room C
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Large language models are touted as the solution for every problem under the sun. Yet, when it comes to everyday tasks like creating work schedules for 30 employees across 3 shifts or optimizing truck routes for 100 deliveries these models often derail.

We'll explore the reasons why LLMs aren't the right tool for these tasks and introduce a hidden gem when it comes to tackling these problems: metaheuristics. With live demos, you’ll see how by simply annotating your domain classes and defining your constraints, you can harness their power using Timefold, an Open Source framework built in Java.

By the end of this session, you’ll walk away with a powerful addition to your problem solver toolkit and we’ll also explore how LLMs and metaheuristics can complement each other so you can create powerful AI solutions to everyday problems.
scheduling
metaheuristics
timefold
llms
talks.speakers
Tom Cools

Tom Cools

Timefold

Belgium

Developer Relations Engineer for Timefold, Java Champion and leader of the Belgian Java User Group. Tom has a decade worth of experience delivering systems and loves to share not only knowledge but also passion for our craft. You can read more at his blog (http://www.tomcools.be) or follow him on Bluesky (@tomcools.be).

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