Comments URL: https://news.ycombinator.com/item?id=47204663

Points: 5

# Comments: 0

- Basic: an open source Food Packaging quickstart on solver.timefold.ai

- Advanced: a Job Scheduling REST API on app.timefold.ai

- Timefold Solver (not MIP, focussed on vehicle routing, job scheduling, shift rostering, etc): http://solver.timefold.ai

- Several solvers at COIN-OR: https://www.coin-or.org/

- Choco

Think Java/C++/Python vs Assembly.

Ironically, talking about problem reduction... most math equation based solver can't scale quadratic equations, so users "relax" the business constraints (to the point that projects fail in production).

Without involving the human planners, the project won't succeed. We're empowering them, with PlanningAI assistance, so they can focus on what they planning must do, for who, instead of how it gets there. So when the plan goes off the rails - 5 people call in sick - they can get it back on the rails in seconds with Real-Time Planning.

The engineering work is only half the work, or less. Fitting the technology into the human processes is another big chunk. Half of my videos on youtube deal with such cases: Continuous Planning, Real-Time Planning, Non-distruptive Replanning, Pinning, ... Not code, not technology, but design patterns.

And even then, this is far from 100% of the solution. Technology and education is still not enough.

That human planner with 30 years of business knowledge in his/her head is still a critical: he/she will always need to tweak, oversee and sometimes overrule the planning solution in production.

However, the cost and risk to achieve that productivity gain is typically huge. Many Operations Research projects fail. And when they do, they are very expensive failures. "Managers getting fired" expensive.

With our technology, we're making OR projects easy and quick to put into production.

Out of all our quickstarts, Project Job Scheduling is - by far - the one I am least proud of. It was overfitted for a particular problem and - in hindsight - it didn't align well to real-world variants of Job Shop Scheduling that many people face. But before Food Packaging, it was all we had.

Sorry for the pain. Thanks for sharing these insights.

What kind of use case where you trying to solve with OptaPlanner? In the past, I've seen a strong correlation between the ease of solving the problem and the availability of of a quickstart example.

For example, our tech is even used for court scheduling (in different countries), but every single one of those cases was difficult. Other cases are far more simple.

It all depends on the planning problem.

Do you remember any particular pain in your OptaPlanner experience that we can improve going forward? Around which year was this?

We also have an overview of our open source solver here: https://timefold.ai/open-source-solver

Do note that the base algorithm (such as SA, TS or LA) is less than 5% of the work to get great results. We do a lot of additional things on top of that (incremental score calculation, smart neighborhood selection, multi threaded solving and many more).

Our maps service does do a bunch of additional optimizations (caching, incremental requests, ...) to assist any solver model we run that request maps information.

Yes, having in-house expertise to integration optimization into their existing tools is hard. Especially if they use low level solver APIs (especially if it's math equations).

We're working making that easier with high-level REST APIs (Timefold Field Service Routing, etc). And with education (Timefold Academy) by creating videos and articles on how to integrate real-time planning, continuous planning, labor law constraints, fairness, cost reduction, etc

See https://www.youtube.com/@timefold/videos

Until the startup has investors that agree to pay the founders a wage (to reduce personal financial stress so they can concentrate on the company) - or the startup is profitable - it's vital that your savings don't run out...

Comments URL: https://news.ycombinator.com/item?id=42999454

Points: 451

# Comments: 106

We ended up removing it from the default quickstarts [1], but maybe we should reinstate it in timefold-sandbox?

[1] https://github.com/TimefoldAI/timefold-quickstarts/commit/d9...

Comments URL: https://news.ycombinator.com/item?id=42492906

Points: 1

# Comments: 0