One Cool Day Job: Building Algorithms For Elevators

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.

Source: http://rss.slashdot.org/~r/Slashdot/slashdotScience/~3/PnjTTdCt660/story01.htm

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