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youtube.com/watch?v=dCryQg… Dear Devoxx thanks for this, I really hope people which love #benchmarking and #performance, but normal devs as well, will have something to learn and fun with it 🥰

A reddit user is asking about your experience with Timefold: reddit.com/r/optimization… Reviews welcome!

The new overview of our REST API for Field Service Routing is now online. Take a look: app.timefold.ai/models/field-s… It covers almost every constraint. From time windows, fairness and technician costs to real-time planning and many other #PlanningAI requirements. Just pick and choose

One of the most interesting feats of engineering recently was explained Saturday at #Fosdem for project Liliput for #Java OpenJDK: How to put 25 bits into 2 bits. How they are circumventing the trade-off between usability and memory usage. 🧵 bsky.app/profile/geoffr…

How I built an AI company to save my open source project On this day, 3 years ago, my world fell apart. It was a Thursday. I just finished my second meeting that morning, looked at my inbox and realized it was over. My life’s work was over. OptaPlanner, the popular open source

The quickstarts readme for our open source solver had a make-over! It's now easier to find the right example and start solving your scheduling or routing problem with #PlanningAI. Try it out: github.com/TimefoldAI/tim…

How to minimize driver costs for the Vehicle Routing Problem. In this video, I cover dealing with a mixed fleet of employees and contractors. As well as overtime costs. youtube.com/watch?v=2Foa68… #OperationsResearch #VRP #FieldService #AI #PlanningAI

LLMs can’t do everything — but AI still can! 🤖 Join Tom Cools April 23rd for a session that cuts through the GenAI hype! Learn how Timefold tackles scheduling problems LLMs can’t solve Live Stream: bit.ly/4jaIpBS

Most publications of the Vehicle Routing Problem only cover the tip of the iceberg. Benchmarks and academic papers typically only handle vehicle capacity (CVRP) and time windows (VRPTW) But production deployments also need to deal with many other requirements.

Can LLMs solve scheduling or routing problems? Can GenAI help companies save millions of dollars by optimizing Vehicle Routing, Shift Rostering or Job Scheduling to increase productivity and retention? Why not? Which type of AI can? And how can we combine it with LLMs to make

Where does your enterprise stand on the scheduling maturity ladder? Are your resources scheduled manually? With or without constraint verification? Or automatically? With or without optimization? Discover the 4 Levels of Scheduling: timefold.ai/blog/the-4-lev…

This is what happens if you use floating point numbers (int, double in #Java) for financial data. Use decimal numbers (BigDecimal in Java) instead.

📅🤝🕑 We’re ecstatic to announce the new Timefold x Bryntum partnership! 🎉 Together, we’re building a complete solution that delivers Timefold’s expertly optimized schedules in a smart, user-friendly interface. Read for more 🔗 bryntum.com/blog/timefold-… #timefold #bryntum

That moment when your backend algorithms optimize a vehicle routing dataset with thousands of visits... ... and your frontend turns it into a piece of modern art.

Vehicle Routing Problems in production: When research meets reality. Our webinar yesterday covered: - Maps integration - Time calculation - Hard constraints in reality - Multi-objective optimization - Real-time planning See the recording: youtube.com/watch?v=l12GsN…

Revisit our latest webinar: Vehicle Routing Problems in production: When research meets reality timefold.ai/events/webinar…

A great plan is worthless if you can't explain why. For the operators to trust your software, they need more than a button to "Schedule with AI" that generates the optimized schedule for their employees. They need explainability. Learn how: timefold.ai/blog/explainab…

Can the optimal solution for a Traveling Salesman Problem (TSP) have crossing paths? No, it cannot. Because of the Triangle Inequality principle. But in reality, it can. Because there's road infrastructure: highways, one-way streets, etc. In the real-world, solution optimality