Multicrew Scheduling and Routing in Road Network Restoration Based on Deep Q-learningDownload PDF

24 Nov 2021, 18:02ML4OR-22 PosterReaders: Everyone
Keywords: Road Network Restoration, Multicrew Scheduling and Routing, Network Partition, Deep Q-learning, Shared Experience Buffer
Abstract: Road network restoration is an important issue in the post-disaster disposal and rescue, especially when extraor-dinarily serious natural disasters (e.g., floods and earth-quakes) occur. Central to this endeavor is the problem of determining how to reasonably schedule and route the re-pair crew to quickly restore the damaged road network and establish reliable supply lines from supply nodes to demand nodes. However, most existing work focuses on the activities of the single repair crew, and rarely consid-ers the problem of continuously damaged road sections, especially for the road network with enormous demand nodes and serious damage. Consequently, this work is concentrated on multicrew scheduling and routing for the damaged road network with enormous demand nodes. Specifically, a model of multicrew scheduling and routing is first presented. Next, a road network partition strategy is first proposed to make different repair crews responsible for different subnets. Then, a deep Q-learning based mul-ticrew scheduling and routing algorithm is proposed for the damaged road network with enormous demand nodes, which utilizes the learning experience of multiple repair crews to achieve hybrid learning. Finally, experimental re-sults demonstrate that the proposed method can make re-pair crews adjust their scheduling and routing strategies according to the damaged road network and provides a useful attempt to restore the damaged road network in complex emergency scenarios of post-disaster.
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