Q-AWED: Adaptive-Weight Elevator Dispatching with Q-Learning Across Multiple Traffic Scenarios
In 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.
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