2012/10/19 by Daniel Nikovski, Daniel N. Nikovski, Nikovski, Daniel N. +2 · 1 voice
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Elevator Systems and Control #FOS: Computer and information sciences #FOS: Electrical engineering #Smart Parking Systems Research #Systems and Control (eess.SY) #Traffic control and management #cs.AI #eess.SY #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.1212.2499
openalex publication_date 2012/10/19 · arxiv published 2012/10/19 · arxiv updated 2012/10/19 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Group elevator scheduling is an NP-hard sequential decision-making problem with unbounded state spaces and substantial uncertainty. Decision-theoretic reasoning plays a surprisingly limited role in fielded systems. A new opportunity for probabilistic methods has opened with the recent discovery of a tractable solution for the expected waiting times of all passengers in the building, marginalized over all possible passenger itineraries. Though commercially competitive, this solution does not contemplate future passengers. Yet in up-peak traffic, the effects of future passengers arriving at the lobby and entering elevator cars can dominate all waiting times. We develop a probabilistic model of how these arrivals affect the behavior of elevator cars at the lobby, and demonstrate how this model can be used to very significantly reduce the average waiting time of all passengers.