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A new humanitarian relief logistic network for multi-objective optimization under stochastic programming.

Peiman Ghasemi1, Fariba Goodarzian2,3, Ajith Abraham2,4

  • 1Department of Logistics, Tourism and Service Management, German University of Technology in Oman (GUtech), Muscat, Oman.

Applied Intelligence (Dordrecht, Netherlands)
|June 9, 2022
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Summary
This summary is machine-generated.

This study presents a stochastic model for humanitarian relief logistics, optimizing supply chains and evacuation routes during disasters. It aims to minimize costs, unmet demands, and evacuation failures for better disaster preparedness and response.

Keywords:
Distribution planningHumanitarian relief logisticsMeta-heuristic algorithmsSimulation-optimization model

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Area of Science:

  • Operations Research
  • Disaster Management
  • Logistics

Background:

  • Earthquakes cause significant human and material losses annually, necessitating robust preparedness and response strategies.
  • Effective humanitarian relief logistics are crucial for mitigating disaster impacts and ensuring timely aid delivery.
  • Uncertainty in demand and operational complexities challenge traditional disaster response planning.

Purpose of the Study:

  • To develop a scenario-based stochastic multi-objective location-allocation-routing model for humanitarian relief logistics.
  • To simultaneously address pre- and post-disaster strategic and operational decisions under uncertainty.
  • To minimize total relief supply chain costs, unsatisfied relief staff demands, and unsuccessful evacuation probabilities.

Main Methods:

  • A simulation approach is employed to manage demand uncertainty.
  • The proposed model integrates pre- and post-disaster phases, considering resource allocation, routing, and victim evacuation.
  • The Epsilon-constraint method and metaheuristic algorithms are used to solve the model for various problem scales.

Main Results:

  • The model effectively integrates strategic and operational decisions for disaster relief logistics.
  • It simultaneously considers relief distribution, victim evacuation, and resource allocation under uncertainty.
  • Empirical results demonstrate the model's utility in locating facilities, optimizing routes, and allocating resources.

Conclusions:

  • The developed model provides a comprehensive framework for optimizing humanitarian relief operations in uncertain disaster scenarios.
  • It supports informed decision-making for locating shelters, distribution centers, and determining efficient evacuation routes.
  • The approach enhances disaster preparedness and response by minimizing costs and maximizing aid effectiveness.