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Utilizing microblogs for optimized real-time resource allocation in post-disaster scenarios.

Moumita Basu1, Sipra Das Bit2, Saptarshi Ghosh3

  • 1Present Address: Amity University Kolkata, Kolkata, 700156 India.

Social Network Analysis and Mining
|December 13, 2021
PubMed
Summary

This study introduces a utility-driven model for optimizing emergency resource allocation post-disaster using real-time microblog data. The model efficiently and fairly distributes critical supplies to need-locations, outperforming baseline methods.

Keywords:
DisasterOptimizationPost-disaster reliefResource utilityResource-availabilityResource-need

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Area of Science:

  • Disaster Management
  • Information Science
  • Operations Research

Background:

  • Efficient emergency resource allocation is critical post-disaster.
  • Real-time identification of needs and resource availability presents significant challenges.
  • Social media, particularly microblogs, offers a valuable source of real-time disaster information.

Purpose of the Study:

  • To develop a utility-driven model for optimized, real-time resource allocation in post-disaster scenarios.
  • To leverage microblogging data for improved decision-making in emergency logistics.
  • To enhance the efficiency and fairness of resource distribution.

Main Methods:

  • Extraction of real-time information on resource needs and availability from microblogs.
  • Development of a utility-driven model for optimized resource allocation.
  • Experimental comparison against baseline allocation models.

Main Results:

  • The proposed model significantly improves resource allocation efficiency.
  • The model effectively reduces resource deficits at need-locations.
  • The allocation strategy demonstrates increased fairness in resource distribution.

Conclusions:

  • A utility-driven model based on real-time microblog data can optimize post-disaster resource allocation.
  • The model provides a more efficient and equitable distribution of emergency supplies.
  • This approach enhances disaster response capabilities through data-driven logistics.