  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).

Machine Learning;Artificial Intelligence;Optimization and Control