Efficient elevator traffic management in large buildings is critical for minimizing passenger travel times and energy consumption. Because heuristic- or pattern-detection-based controllers struggle with the stochastic and combinatorial nature of dispatching, we model the six-elevator, fifteen-floor system at Vrije Universiteit Amsterdam as a Markov Decision Process and train an end-to-end Reinforcement Learning (RL) Elevator Group Control System (EGCS). Key innovations include a novel action space encoding to handle the combinatorial complexity of elevator dispatching, the introduction of infra-steps to model continuous passenger arrivals, and a tailored reward signal to improve learning efficiency. In addition, we explore various ways to adapt the discounting factor to the infra-step formulation. We investigate RL architectures based on Dueling Double Deep Q-learning, showing that the proposed RL-based EGCS adapts to fluctuating traffic patterns, learns from a highly stochastic environment, and thereby outperforms a traditional rule-based algorithm.

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).

We propose a novel approach for group elevator scheduling by formulating it as the maximization of submodular function under a matroid constraint. In particular, we propose to model the total waiting time of passengers using a quadratic Boolean function. The unary and pairwise terms in the function denote the waiting time for single and pairwise allocation of passengers to elevators, respectively. We show that this objective function is submodular. The matroid constraints ensure that every passenger is allocated to exactly one elevator. We use a greedy algorithm to maximize the submodular objective function, and derive provable guarantees on the optimality of the solution. We tested our algorithm using Elevate 8, a commercial-grade elevator simulator that allows simulation with a wide range of elevator settings. We achieve significant improvement over the existing algorithms.