Assignment strategy selection for multi-car elevator group control using reinforcement learning

Authors:
Taichi Uraji;Kenichi Takahashi
Affiliations:
Graduate school of Information Science, Hiroshima City University, 3-4-1, Ozukahigashi, Asaminami-ku, Hiroshima, 731-3194, Japan.;Graduate school of Information Science, Hiroshima City University, 3-4-1, Ozukahigashi, Asaminami-ku, Hiroshima, 731-3194, Japan
Venue:
International Journal of Knowledge and Web Intelligence
Year:
2012

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Abstract

This paper discusses the group control of elevators in the web monitoring system for improving efficiency and saving energy; an efficient control method for multi-car elevator using reinforcement learning is proposed. In the method, the control agent selects the best strategy among three strategies, namely distance-strategy, passenger-strategy, and zone-strategy, according to traffic flow. The control agent takes the number of total passengers and the distance from the departure floor to the destination floor of a call into account. Through experiments, the performance of the proposed method is shown; the average service time of the proposed method is compared with the average service time for the cases where the car assignment is made by each of the three strategies.