Responding to a natural disaster is a short-lived and complex task requiring fast coordination and analytical skills. Responses to natural disasters must be fast, and resources need to be used in an efficient and effective manner. An automated system can significantly reduce human decision errors, increase speed, and lower operational costs. This study presents an automated system that leverages real-time mobile network data to optimize team deployment and coordination in order to repair base station alarms and re-deploy existing cellular communication networks whose core nodes have been damaged or congested. Algorithms presenting solutions from optimization to fast heuristics are adopted for automated assignment of technical teams to alarms as a response to a natural disaster. Algorithms are evaluated and tested for their multi-objective assignment performance, subject to constraints. A real alarm dataset logged from base stations is used. The average number of alarms assigned, assignment rate, average latency of the automated assignment system, and travel distance of the technical teams to the base station are used as the performance metrics. Cellular communication has to be re-deployed to sustain coordination and resilience as an immediate response to a natural disaster. The presented approach and comparative results show that technical teams located in neighboring cities can be assigned an immediate response, and automatic assignment of new arrival alarms with high priority can be assured by minimizing the travel distance of technical teams to the level of a few kilometers. This study fills specific gaps in comparison to prior studies by using the real alarm data logged after a destructive earthquake event, adopting a multi-objective optimization along with the performance metrics used by telecom operators.
Dimez et al. (Sat,) studied this question.
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