I live about 2km away from “De Schorre” in Boom (Belgium), or as most people probably know it: the location where the Tomorrowland festival is hosted. It is genuinely one of the most beautiful festivals I’ve ever been to, and I remember thinking: “Wow, pulling all of this off must require a lot of planning”.
If there is something I love doing, it’s looking at these kinds of “close to home” (literally in this case!) planning problems and trying to figure out how I would model them. From the stage-builders, to the stewards and of course the artists and stages, everything needs a plan, a schedule to make it all work together like a symphony.
Modeling time! I don’t know a lot about organizing a festival or music concerts, so I limited my scope to just assigning artists to stages. These are a few of the constraints I came up with.
I thought this was pretty good already. I could build the model and ship it! That is, until I talked to a friend who knows a couple things about planning festivals and he made it very clear I was missing a few essential elements.
All of these make a lot of sense and I completely missed them! This is something I see a lot at Timefold. Whenever I speak to human planners, especially when it’s not a use case we’ve explored with our pre-built models, I often come back with some new or unexpected constraints.
This brings up an important point: You can’t build a good model without input and validation by the real heroes, the human planning experts. Essentially, modeling is embedding the expertise and know-how of human experts and making it scale beyond the planners own capacity.
When we create a new model, does that mean we can just talk to a human planner, “capture” their knowledge, and then produce a fully working planning system which can live without human interaction?
No matter how long you spend creating the ultimate model, even after talking to thousands of human experts, there will always be times when you hear the expression, “This plan doesn’t feel right.”
Quite often this means we are missing some hidden intangible constraint, perhaps something the human planner themselves can’t really put to words because it comes from a place of intuition and experience. This is a common yet tricky situation: either we figure out what’s missing, or the planner will get stuck with this feeling… and feelings are a powerful thing.
In the case of planning events (or festivals), I have heard the expression, “This doesn’t vibe well”. I have not yet figured out how to create the constraint to optimize for “vibes”.
However, while I believe feelings are always valid, they aren’t always rational. In planning, a schedule that feels better might actually be much worse if you look at it through the emotionless lens of the mathematical scoring function. Giving that young DJ a chance on a bigger stage, or putting 2 high intensity acts back-to-back might feel better but it could negatively affect the raw “score” of your schedule.
The real best solution, the golden path, is probably somewhere in between mathematically perfect and emotionally excellent. So even after using computers to optimize a plan, human planners improve the schedule further by embedding some empathy, emotion, and nuance into the schedule.
People who have watched the news lately know where this is going. 2 days before opening the main stage at Tomorrowland, a real piece of art people work years to produce, burned down in a couple of hours. A real drama and any carefully crafted symphonic plan… well… not usable anymore.
Eventually, this happens to every plan, just to varying degrees. Whenever a plan meets reality, it’s almost never a 100% fit… and if it is, it doesn’t stay like that for long. Employees get sick. Delivery vehicles break down. Systems crash. Budgets get slashed.
The real challenge is not to plan, but to adapt when life throws a wrench into your schedule.
The Tomorrowland team quickly came up with 2 alternative solutions, but 1 of them sparked my interest the most. They had a stage at the camping ground and they could label that one “the main stage” and carry on. The problem is, the camping ground is pretty far away from the actual festival ground. The festival would essentially be split: main grounds and camping.
So what would have happened if I’d run it through my optimization algorithm? It would have probably moved some of the main stage artists to the festival ground due to the “artist minimal audience space” constraint. Without anything to avoid making too many changes (like an anti-disruption constraint) that would have been the mathematically more optimal solution.
Instead what they wanted to do was much more empathic: since the people on the camping ground would only have 1 stage, they would get all the main stage artists, aka most big names. From a planning perspective, it’s also easier as the schedule remains essentially the same. It just feels like a much better solution.
In the end, they didn’t even have to go for this solution as, due to acts that can only be described as “heroic”, they managed to clean up and build a new main stage with only a couple of hours delay.
Planning isn’t just about satisfying constraints or optimizing for the perfect score. It’s about translating human values, intuition, and adaptability into something machines can assist with but will probably never fully replace.
It’s clear that behind every great plan is a human making judgment calls that no algorithm can foresee. At its best, planning technology doesn’t replace the human expert, it amplifies them. It allows them to see and act on solutions they might not have seen on their own and gives them the freedom to shape those possibilities with empathy and insight.
So yes, let’s model, optimize, and automate where we can, but let’s never forget: planning is, and always will be, profoundly human.
Fuse Bryntum’s sleek Scheduler with Timefold’s optimization engine. This hands-on guide walks you through the wiring, so your schedules are user-friendly and smart.
Optimization algorithms can spit out mathematically brilliant schedules, but if planners can’t see the rationale, those “perfect” plans end up in the trash. In this blog post, Tom Cools explains how explainability turns planning optimization into a trusted ally instead of a black box.
