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).
翻译:本文介绍了将编程和数学结合起来以优化电梯等候时间的方法。根据根据电梯交通的卡通三峰模型生成的模拟用户数据,我们首先根据对电梯背后逻辑的直观理解来开发一个天真的模型。我们考虑到一系列广泛的特征,包括能力、加速度和最大等待时间阈值,以充分模拟现实环境。我们利用同样的评价框架,着手开发一个深Q学习模型,以与硬码天真的电梯控制方法相匹配。我们在整个文件中,在马尔科夫决策程序(MDP)的模型下工作,但后来探索了假设如何未能描述高度随机的总体电梯组合控制系统(EGCS)的特点。