I got paid minimum wage to solve an impossible problem.
An experiment involving sweeping floors, simulated annealing, and why algorithms that optimize for the wrong thing ruin your life.
Sweeping the entire Albert Heijn floor. Sounds simple. And should’ve been simple.
But I’m a Computer Science student, with a problem: I can’t stop trying to optimize things that (probably) don’t need optimizing.
So instead of just doing my job and, well… sweeping… I did what any “reasonable” person would do: I turned the supermarket floor plan into a grid graph, built a visual editor and wrote a C++ path optimizer using simulated annealing.
But before we dive into how this went spectacularly wrong, and how this made me realize how this makes everyone miserable, I need you to answer a quick question:
If you were to take over my job for one day (I wouldn’t recommend it but hypothetically speaking) and needed to sweep the entire Albert Heijn floor, would you take path A or B?
Seriously. Look at them. Which one seems more efficient for sweeping a supermarket floor?
If you picked path A: congratulations, you think like an algorithm and are most likely a robot. (Good luck with CAPTCHA questions.)
But you are technically right. Path A is shorter by distance. It is absolutely useless however.
Look at those turns. Actually imagine for a second that you would walk around taking those turns. You’d look insane, like some Roomba having a seizure.
Path A is what happens when you optimize for the wrong thing.
Which, spoiler alert, is the entire point of this story. But we’ll get there, let me first explain how we got here:
Step 1: Turn reality into a (overly simple) model.
First, I took the Albert Heijn floor plan and converted it into a grid. Each tile is either empty (should be swept) or an obstacle (wall, checkout counter, yoghurt package somebody threw on the ground).
I built a visual editor in Processing (a Java tool for people who like making things look cool), so I could easily map out the store and export the resulting graph.
Converting the floorplan into the grid structure was therefore quite easy.
The tiling of the actual floor helped to subdivide the area into bite-sized chunks.
This could then easily be converted into a network structure (also called a graph), by interpreting each tile as a node and then connecting them to neighboring tiles.
As you can see, I allowed for horizontal and vertical movement, as well as diagonal movement (as long as you don’t fly through walls).
The only thing to do next was to find a cycle through this network while making sure to visit all nodes (tiles). This would then be the solution to my sweeping problem.
(This problem is also called the Traveling Salesman Problem, see article for more details and why it is so hard to “solve”.)
Step 2: Write the optimizer.
Since it is computationally impossible to find the best path in a graph of this size, we have to resort to heuristics. Heuristics basically try to find a very good solution in a short time, instead of trying to find the perfect solution (which is more or less impossible).
So I implemented the path optimizer in C++.
The underlying heuristic algorithm: simulated annealing.
If you’re not familiar, simulated annealing is essentially trying a bunch of little changes (also called local moves).
At first, you just accept every little change (even if it makes the path worse), but throughout the algorithm you slowly get more picky and at the end only allow changes that strictly improve the path.
This is inspired by how metals cool down. By starting with a high temperature (just trying different moves) to explore a lot, and then gradually cooling down to settle into a low energy state (close to optimal).
Watch this gif. See how it start chaotic and gradually settles into something stable? That’s simulated annealing doing its thing.
For the local move, I used the 2-opt move. You take two edges in your path, remove them, and reconnect them in a different way. If this tiny change makes the path better, keep it. If not, either keep it (if the temperature is still high) or discard it.
Then just do that 1 billion times. Or well… let your computer do that 1 billion times.
Step 3: F*ck up.
After letting it run for a while, I got my first “optimized” path. Here’s what it came up with:
Look at it. That path has more sharp turns than a Christopher Nolan movie. There is no way anybody is crazy enough to actually sweep like this. You would probably throw up afterwards.
Technically, it covers the entire floor. Technically, it’s (almost) the shortest sweeping path. Technically, it’s perfect.
There are some good parts to it, but practically, it’s absolutely useless.
The algorithm did exactly what I asked for. (Thankfully, imagine if it would just do something else entirely, that would be scary.)
I just asked it the wrong question.
Step 4: Optimize for reality.
I quickly realized I was optimizing for the wrong thing. Distance isn’t everything.
Turns matter. Momentum matters. Not looking like a malfunctioning robot matters.
So I added a “turn penalty” to the cost function and asked it to also minimize that. Basically telling the algorithm: “Turning 90 degrees costs you extra points. Turning 180 degrees? You are out of your mind.”
This resulted in smoother routes, even if it makes the distance slightly longer.
Look at that. It’s actually… walkable. You could give this path to an actual human being and they wouldn’t quit on the spot.
We are not optimizing for distance anymore. We are optimizing for reality.
Step 5: Break it.
Here’s where it gets fun.
You can adjust the penalty for sharp turns. This acts as a slider between “pure efficiency” and “actually useful”.
You can literally see the trade-off. As you increase the penalty, the path gets smoother but a little longer. As you decrease it, you get efficiency but chaos.
Which path you choose is entirely up to you. It depends on things like, how easy it is for you to turn, if total distance is a priority or not, and how much dizziness you can tolerate.
Step 6: Realize life is not so good after all.
This isn’t just about sweeping floors however.
This is about everything.
Social media algorithms optimize for engagement. They’re really good at it. The problem?
Engagement ≠ happiness. Engagement ≠ truth. Engagement = clicks, screen time, rage and reaction.
Consequences? Outrage, misinformation, doomscrolling, anxiety.
The algorithms are working perfectly. It’s doing exactly what it was designed to do. The cost function is just wrong. (Instagram would probably think otherwise.)
Recommendation algorithms optimize for watch time and click-through rates. Your grandma is binge-watching conspiracy theories on YouTube for 6 hours.
The algorithm crushed it. She feels like shit.
No surprises there.
Even LLMs (Large Language Models) like ChatGPT optimize for the wrong thing. They optimize for sounding confident. For sounding like they know the answer.
Not for being right. Not for being honest.
They’re trained to complete patterns, not to say “I don’t know”. So they just guess. Without any shame and with perfect grammar.
This even applies to things outside of tech.
Think of businesses. Most of them optimize for profit. Earth, the environment, morals or ethics? They are not integrated into the cost function, so won’t be optimized.
Did I use this optimized path at my actual job?
No. Obviously not. I just swept the floor like a normal person.
But building this taught me something that I think about constantly: technical correctness is worthless if you’re solving the wrong problem.
You can write perfect code. You can build flawless systems. You can optimize the sh*t out of your cost function. And you can still end up with something that sucks.
The important part isn’t the optimization algorithm. The important part is figuring out what you should be optimizing for in the first place.
Most of the time, we don’t even ask that question. We just optimize for whatever’s easy to measure and hope it works out.
Spoiler: it probably doesn’t.
If you learned something from this, great. If you just enjoyed watching me over-complicate a sweeping job, also great.
Either way, thanks for reading about my attempt to optimize a task that absolutely did not need optimizing.
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GitHub repository (code): here
What path does it come up with if you connect time with movement? For example: moving straight is 1 second, but turning and moving straight takes 2 seconds?
What an incredibly useful exercise! You learned more in that optimization effort than 90% of the engineers I've ever worked with, not to mention the general public.
And the real-world applications are endless--not just in technical fields. The great flaw in so much political thinking is the idea that "we can fix this problem" without considering if it's really the right problem to fix, balanced against all the competing problems. Even the USSR optimized their systems for the outcome they got.