2026-04-07T21:49:27Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/56312024-03-04T09:39:19Zcom_20.500.11984_1143col_20.500.11984_1148

00925njm 22002777a 4500 dc Arrieta, Aitor author Sagardui, Goiuria author 2021 Elevators, as other cyber-physical systems, need to deal with uncertainty during their operation due to several factors such as passengers and hardware. Such uncertainties could affect the quality of service promised by elevators and in the worst case lead to safety hazards. Thus, it is important that elevators are extensively tested by considering uncertainty during their development to ensure their safety in operation. To this end, we present an uncertainty testing methodology supported with a tool to test industrial dispatching systems at the Software-in-the-Loop (SiL) test level. In particular, we focus on uncertainties in passenger data and employ a Genetic Algorithm (GA) with specifically designed genetic operators to significantly reduce the quality of service of elevators, thus aiming to find uncertain situations that are difficult to extract by users. An initial experiment with an industrial dispatcher revealed that the GA significantly decreased the quality of service as compared to not considering uncertainties. The results can be used to further improve the implementation of dispatching algorithms to handle various uncertainties. 978-1-6654-2603-9 https://katalogoa.mondragon.edu/janium-bin/janium_login_opac.pl?find&ficha_no=167703 https://hdl.handle.net/20.500.11984/5631 Elevators genetic algorithms Quality of Service uncertainty passenger data software in the loop simulation Genetic Algorithm-based Testing of Industrial Elevators under Passenger Uncertainty