ELEVATE is an industrial elevator simulator that can be applied to examine the performance of elevator installations and test scheduling algorithms in a realistic environment. Elevate is a GUI-based application and requires manual steps to perform simulations. In this thesis, we have designed, developed and implemented a program (Simuloop) to facilitate a simulator loop with Elevate. Simuloop enables automatic simulations without manual interference. We propose 2 experiments that apply Simuloop to generate demanding passenger traffic to test an industrial elevator dispatcher. We apply a genetic algorithm and reinforcement learning, respectively. Simuloop is used to give feedback to the algorithms by simulating the passenger traffic. The experiment with the genetic algorithm performs stochastic updates on a lunch peak profile with 948 passengers. The updates are based on varying the passenger weight, entry/exit time to identify patterns that yield a high waiting time. The algorithm is able to increase the average waiting time from 20 to 44.5 seconds. The experiment with reinforcement learning has higher requirements to Simuloop since it depends on frequent feedback to guide the learning. We design a small-scale experiment to train the algorithm to select the arrival and destination floors for passengers. The algorithm is able to increase the cumulative waiting time, suggesting that the experiment is applicable for the use case. Due to the limitations of the pipeline, we conclude that using reinforcement learning is unpractical. The experiments prove that Simuloop can successfully be applied for automatic testing of an elevator system. This opens up the opportunity for performing exhaustive testing without the need for manual steps.
Authors: Torbjørn Ruud
Title of the source: Master’s thesis
Publisher: University of Oslo