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For the Empire State Building, visitors take up to three consecutive elevator rides to get to the observation decks: one to get up to the 80th floor, another from 80 to 86 and the main observation deck (though the hearty can take the stairs), and an optional, extra-charge trip from 86 up to the topmost observation area on 102. It's also possible that there's a little bit of complexity being glossed over here. The author was probably just looking for any sort of answer to 'What's the most famous building you've ever done any work for?', rather than 'what's the most mathematically-interesting part of your job?' I suspect that the problem here is a failure on the part of the article writer. Isn't making the elevator go faster a job for an engineer? Does one really need to be a mathematician to know that a faster elevator moves people faster? yeah, you want someone with a serious background in applied optimization, statistics, or artificial intelligence. Given the potential complexity of how many parameters and models can potentially be considered. Speed also needs to be optimized not just based upon the desire to reach the destination quickly, but also considering the rate at which the mechanisms will wear out, the energy consumption caused by more rapid movement, and to encourage people to use the stairs. This problem can be considered from several different angles within ML either as a regression problem or classification, for example. In a real-world setting you may have other phenomena that actually need to be learned, such as different groups taking lunches at set times of day, large meetings that cause several floors to congregate on one, et cetera. At the start of the day, for example, the elevators should rest on the ground floor, so that they can collect people going up similarly, toward the end of the day, they should rest at the top, since the overwhelming majority of people would be going down.
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Yes, the equipment I designed and wrote the software for is obsolete now, but there's a lot of it out there so I'm anticipating writing updates for a while longer as I head toward eventual retirement.Ī professor once described to me an elevator system at his former place of employment that used machine learning to try and anticipate where the elevator should be when not in use. You have to take into account for car locations, direction, speed, where car and hall calls are locatedand have to figure in such things as door times to calculate which car can service a hall call soonest.Īs the author says, it's a set of interesting problems and I've had fun with it. Optimizing is worthwhile, but adds a lot of complexity. Each elevator is independent and can run on its own, but they communicate with each other to handle dispatching so multiple cars coordinate their activity. I've done up to 26 stops in a multi-car group with that setup. It doesn't take much to handle the basics when you're using assembly language. All written in assembly language running on an 8085 CPU with 256 bytes (Yes, bytes) if RAM and 8K bytes of EPROM.
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I designed the hardware and wrote the code for a much smaller elevator company for 25 years.