> Destination Dispatch [...] are in general worse
I wonder if this is an artifact of how the author used random destinations. I worked in a building that used Destination Dispatch, and the common travel pattern seemed to be:
- Everyone who is not on the ground floor generally want to go to the ground floor.
- People who are on the ground floor generally travel in large groups to the same destination.
This happens because people who worked on the same floor often leave for lunch at the same time, and return at the same time to the same floor. Destination Dispatch helps in this case because it's batching large groups of people with the same destination.
This is case where you need to make sure your test data reflects real-world usage or you end up optimizing for the wrong thing.
Ideally a new elevator installation would have a trial period where they record usage and then run it through a simulator like this to determine the optimal strategy for that particular building. And you'd want to re-run it every once in a while as the building tenants/usage changes. I wonder if this actually happens, though.
Most likely not. I think all the big players have been using more traditional ML approaches for years. Then again someone new might go there and fail miserably.
This is an industry that evolves extremely slowly. A decade back one of the big players in this space was still using a software emulation of their original hardware relay logic, because it was cheaper than retraining all the technicians...
It is just the reality that LLMs are not superior for this purpose. They are most likely very much inferior. There is no need to use language models for something that involve no language.
I feel weirded each time LLMs are suggested for anything as some sort of holy solution for every single problem. Robot surgery let LLM control it. Robots cooking LLMs. Nuclear power plants and oil refineries LLMs... Industry has find a sledge hammer and now everything is a nail...
omoikane · · focus · HN ↗
I wonder if this is an artifact of how the author used random destinations. I worked in a building that used Destination Dispatch, and the common travel pattern seemed to be:
- Everyone who is not on the ground floor generally want to go to the ground floor.
- People who are on the ground floor generally travel in large groups to the same destination.
This happens because people who worked on the same floor often leave for lunch at the same time, and return at the same time to the same floor. Destination Dispatch helps in this case because it's batching large groups of people with the same destination.
pimlottc · · focus · HN ↗
Ideally a new elevator installation would have a trial period where they record usage and then run it through a simulator like this to determine the optimal strategy for that particular building. And you'd want to re-run it every once in a while as the building tenants/usage changes. I wonder if this actually happens, though.
shepherdjerred · · focus · HN ↗
Ekaros · · focus · HN ↗
swiftcoder · · focus · HN ↗
Ekaros · · focus · HN ↗
I feel weirded each time LLMs are suggested for anything as some sort of holy solution for every single problem. Robot surgery let LLM control it. Robots cooking LLMs. Nuclear power plants and oil refineries LLMs... Industry has find a sledge hammer and now everything is a nail...