A Multi-Objective Optimization Method for Shelter Site Selection Based on Deep Reinforcement Learning

Abstract

Urban emergency shelters are a special type of public service facility, and their planning and construction are directly related to the safety of urban residents' lives and property, as well as to the sustainable development of the city. Existing research on the site selection of shelters is not ideal when dealing with large-scale scenarios and fails to accurately reflect the actual situation. To address this issue, this study proposes an emergency shelter site selection model based on deep reinforcement learning, IAM-PPO. This model constructs the site selection problem as a Markov Decision Process and uses deep learning to extract information from the site selection scenario. It finds the final solution for shelter locations through continuous exploration and learning. To improve the training efficiency of the model, the action masking process is innovatively applied to the model. The research results and ablation experiments using Shanghai as a case study prove that, owing to the diversity of shelter service ranges and action masking mechanism, the model proposed in this study can provide efficient and accurate shelter location services. Moreover, the customizability of this model provides meaningful reference value for other public facility location problems.