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arXiv · 2210.00065

Application of Deep Q Learning with Simulation Results for Elevator Optimization

Zheng Cao·Raymond Guo·Caesar M. Tuguinay·Mark Pock·Jiayi Gao·Ziyu Wang
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Abstract

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).

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Zheng Cao, Raymond Guo, Caesar M. Tuguinay, Mark Pock, Jiayi Gao, Ziyu Wang. 2022-09-30. Application of Deep Q Learning with Simulation Results for Elevator Optimization. https://arxiv.org/abs/2210.00065

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