JERAJournal of Electronic Research and Application2208-35022208-3510Bio-Byword Scientific Publishing Pty. Ltd.10.26689/JERA.v10i9.15932ArticleQ-AWED: Adaptive-Weight Elevator Dispatching with Q-Learning Across Multiple Traffic Scenarioshttps://artdesignp.com/journal/JERA/10/9/10.26689/JERA.v10i9.15932LiuTianyun20261092026-10-30In different elevator transportation modes, there is a trade-off among waiting times, movements, and load balancing. This article uses adaptive-weight Q-learning, based on traffic conditions, to choose a weight scheme and use a weighted dispatch cost function, travel distance, stop proxy, load, and direction to dispatch a specific elevator. The method used 40 validation episodes and 40 test episodes to compare with seven main elevator dispatching approaches in six scenarios. Results show that Q-AWED improved by 3.88% compared with the untrained default policy, and the relevant differences were corrected using the Holm correction.Elevator dispatching, Q-learning, Adaptive weights, Multi-objective optimization, Traffic simulation

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