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Multiobjective Emergency Resource Allocation under the Natural Disaster Chain with Path Planning.

Feiyue Wang1, Ziling Xie1, Hui Liu1

  • 1Institute of Disaster Prevention Science and Safety Technology, School of Civil Engineering, Central South University, Changsha 410075, China.

International Journal of Environmental Research and Public Health
|July 9, 2022
PubMed
Summary

This study introduces a multiobjective emergency resource allocation model for disaster chains, integrating resource allocation with path planning. It enhances rescue timeliness, efficiency, and fairness, increasing total supply rate by 22.2%.

Keywords:
emergency resource allocationmultiobjective optimizationnatural disaster chainpath planning

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Area of Science:

  • Disaster Management
  • Operations Research
  • Public Health

Background:

  • Securing public safety and health necessitates comprehensive disaster recognition and effective emergency response schemes for disaster chains.
  • Traditional emergency resource allocation for single disasters is insufficient; dynamic response, periodic supply, and assisted decision-making are crucial for disaster chains.

Purpose of the Study:

  • To propose a multiobjective emergency resource allocation model that considers uncertainty within natural disaster chains.
  • To integrate resource allocation with path planning for optimized emergency response.
  • To analyze the impact of logistics, disaster coupling, and government regulation on emergency resource allocation.

Main Methods:

  • Developed a multiobjective emergency resource allocation model incorporating uncertainty under natural disaster chains.
  • Employed a multiobjective cellular genetic algorithm (MOCGA) for optimizing timeliness, efficiency, and fairness in rescue operations.
  • Utilized an improved A* algorithm for creative combination of resource allocation with path planning, including avoidance of unexpected road elements and risk areas.

Main Results:

  • The proposed model and algorithm effectively provide optimal solutions for regional coordination and resilient supply in disaster scenarios.
  • Achieved a 22.2% increase in the total supply rate through optimized resource allocation and path planning.
  • Identified infrastructure disruption, cascading disaster effects, and time urgency as significant environmental challenges.

Conclusions:

  • The developed multiobjective emergency resource allocation model and algorithms are effective in optimizing emergency response during disaster chains.
  • Cooperative allocation strategies, supported by political regulation, significantly enhance the success rate of responding to complex disaster chains.
  • The integration of resource allocation with intelligent path planning is vital for improving logistics performance and overall emergency management.