2022/09/30 by Zheng Cao, Raymond Guo, Cao, Zheng +9 · 1 voice
Engineering · #68T07 #90-10 #93-04 #Artificial Intelligence (cs.AI) #Elevator Systems and Control #FOS: Computer and information sciences #FOS: Mathematics #G.1.6 #I.6 #J.2 #Machine Learning (cs.LG) #Optimization and Control (math.OC)
paper · pdf · doi:10.48550/arxiv.2210.00065
openalex publication_date 2022/09/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a methodology for combining programming and mathematics to optimize elevator wait times. Based on simulated user data generated according to the canonical three-peak model of elevator traffic, we first develop a naive model from an intuitive understanding of the logic behind elevators. We take into consideration a general array of features including capacity, acceleration, and maximum wait time thresholds to adequately model realistic circumstances. Using the same evaluation framework, we proceed to develop a Deep Q Learning model in an attempt to match the hard-coded naive approach for elevator control. Throughout the majority of the paper, we work under a Markov Decision Process (MDP) schema, but later explore how the assumption fails to characterize the highly stochastic overall Elevator Group Control System (EGCS).