Our version of a Solver as a Service deals with cases of up to 390'000 shifts in a single dataset for shift scheduling and 30'000 visits for vehicle routing problems.
Some our customers want to go even higher, and we're working on that.
There is an audience for such platforms - Timefold Platform optimizes 1,000,000 visits and 2,000,000 shifts per week - but only if it's more than just orchestration.
If it handles explainabily, what-if scenarios and insights to fulfill business needs.
And that's where supporting many solvers becomes the blocker.
A lowest common denominator design.
Those solvers are a black box. They don't expose what they're running, why they made certain decisions or how they can scale to large datasets or complex business requirements.
We've picked our poison: one solver, which we've built in the open, in the last 20 years, versatile enough to handle any scheduling problem. That delivers.
Sounds similar to Timefold Platform: app.timefold.ai
That's our Solver as a Service for scheduling problems (vehicle routing problem, shift scheduling, job scheduling, etc). It runs scheduling problems implemented with our open source solver: solver.timefold.ai
But this post is such a service for formula problems instead (think master capacity planning, portfolio optimization, etc), due to the choice of MILP solvers underneath. Similar to NextMv, Neos, etc.
Nonsense. Timefold Solver and other open source vehicle routing problem solvers deliver better results in less time, with affordable hardware, especially for real-world complexity.