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Metaheuristic Algorithm for Solving Biobjective Possibility Planning Model of Location-Allocation in Disaster Relief Logistics

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  • Farnaz Barzinpour
  • Mohsen Saffarian
  • Ahmad Makoui
  • Ebrahim Teimoury

Abstract

Thousands of victims and millions of affected people are hurt by natural disasters every year. Therefore, it is essential to prepare proper response programs that consider early activities of disaster management. In this paper, a multiobjective model for distribution centers which are located and allocated periodically to the damaged areas in order to distribute relief commodities is offered. The main objectives of this model are minimizing the total costs and maximizing the least rate of the satisfaction in the sense of being fair while distributing the items. The model simultaneously determines the location of relief distribution centers and the allocation of affected areas to relief distribution centers. Furthermore, an efficient solution approach based on genetic algorithm has been developed in order to solve the proposed mathematical model. The results of genetic algorithm are compared with the results provided by simulated annealing algorithm and LINGO software. The computational results show that the proposed genetic algorithm provides relatively good solutions in a reasonable time.

Suggested Citation

  • Farnaz Barzinpour & Mohsen Saffarian & Ahmad Makoui & Ebrahim Teimoury, 2014. "Metaheuristic Algorithm for Solving Biobjective Possibility Planning Model of Location-Allocation in Disaster Relief Logistics," Journal of Applied Mathematics, Hindawi, vol. 2014, pages 1-17, April.
    • Handle: RePEc:hin:jnljam:239868
    DOI: 10.1155/2014/239868
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    Cited by:

    1. José M. Ferrer & M. Teresa Ortuño & Gregorio Tirado, 2020. "A New Ant Colony-Based Methodology for Disaster Relief," Mathematics, MDPI, vol. 8(4), pages 1-23, April.
    2. Renata Turkeš & Daniel Palhazi Cuervo & Kenneth Sörensen, 2019. "Pre-positioning of emergency supplies: does putting a price on human life help to save lives?," Annals of Operations Research, Springer, vol. 283(1), pages 865-895, December.

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