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.
A 10% productivity gain is a lot for any company, regardless if they are operating a fleet of 50 or 50 000 vehicles.
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.
Our goal is to make planning optimization easy. But planning optimization can be extremely complex. So it took us quite some time to make it easy. Timefold Solver is already a lot easier than OptaPlanner to use. And the Timefold REST APIs are even easier: if you have a vehicle routing or shift scheduling case on your hands, just send in your data and get the solution.
But not all cases are easy...
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?
Not OP, but in the past we experimented with OptaPlanner for manufacturing scheduling. I think the closest available example was project job scheduling, but we struggled to make it work with finer granularity time placement, it seemed to uselessy move jobs back and forth (IIRC because finishing one stage earlier didn't necessarily improve entire job time, but you didn't want it as constraint because sometimes it is useful to have gaps).
The Project Job Scheduling example was terrible. We deleted it, and replaced it with the much cleaner Food Packaging quickstart.
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.
We support Simulated Annealing too, as well as Tabu Search and many others. By default Timefold Solver uses Late Acceptance, which behaves like Simulated Annealing but isn't parameter tuning fickle like SA.
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).
Hey Geoffrey, I missed that you were behind this! You did help me choose Drools Planner with simulated annealing in 2012. Congrats on coming a long way since then!
Yes!
These days, to handle cases with more work than resources to do it, medium constraints are used a lot too (so hard/medium/soft constaints), to penalize the amount of unassigned work. Those are harder than soft constraints, but softer than hard constraints.
- Basic: an open source Food Packaging quickstart on solver.timefold.ai
- Advanced: a Job Scheduling REST API on app.timefold.ai