One Cool Day Job: Building Algorithms For Elevators

McGruber writes "The Wall Street Journal has an article about Theresa Christy, a mathematician who develops algorithms for Otis Elevator Company, the world's largest manufacturer and maintainer of people-moving products including elevators, escalators and moving walkways. As an Otis research fellow, Ms. Christy writes strings of code that allow elevators to do essentially the greatest good for the most people — including the building's owner, who has to allocate considerable space for the concrete shafts that house the cars. Her work often involves watching computer simulation programs that replay elevator decision-making. 'I feel like I get paid to play videogames. I watch the simulation, and I see what happens, and I try to improve the score I am getting,' she says."

Comments

Re:Mathematician?

[Samantha Wright]

A 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. 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.

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. This problem can be considered from several different angles within ML; either as a regression problem or classification, for example.

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.

Given the potential complexity of how many parameters and models can potentially be considered... yeah, you want someone with a serious background in applied optimization, statistics, or artificial intelligence.

Re:Harddisks use something like it

[Sir_Sri]

That's an undergraduate level general optimization problem.

The one in TFA is a graduate level optimization under a particular set of data constraints. So the generally optimal algorithm for elevators has to a assume a random distribution of people to be picked up and destination floors (head is in a random location wants data from some other random location) - but in practice you may be need sequential access or the like. With elevators, I would expect that in mornings in residential buildings people want to empty out so the 'resting' point would to close to 2/3rds or 3/4ths of the way up, but in the evenings it would be the reverse direction, and business would be the reverse of residential. Schools have a somewhat more random use of bursty every hour up and down, and really big businesses may want dedicated elevators between floors shared by particular companies because there's a lot of daily movement within the floors of a company but not so much outside their area.

Lunch of course adds another complication.

There's a lot of neat work into simulating the data for a building that doesn't exist yet, or measuring the data for a building that exists but has a bad algorithm. And then trying to tailor your elevator to the specific behaviours that actually exist.