A Genetic Algorithm for Post-flood Relief Distribution in Kerala, South India

Advances in Intelligent Systems and Computing - ICT Systems and Sustainability ◽

10.1007/978-981-15-0936-0_11 ◽

2020 ◽

pp. 125-132

Author(s):

Georg Gutjahr ◽

Haritha Viswanath

Keyword(s):

Genetic Algorithm ◽

South India ◽

Relief Distribution

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A genetic algorithm based approach to estimate the volume of a drinking water reservoir in Chennai city, South India, using multi-spectral satellite images

Geocarto International ◽

10.1080/10106049.2019.1687590 ◽

2019 ◽

pp. 1-17

Author(s):

Heltin Genitha C. ◽

Indhumathi M. ◽

Sanjeevi S.

Keyword(s):

Genetic Algorithm ◽

Drinking Water ◽

Satellite Images ◽

South India ◽

Water Reservoir ◽

Drinking Water Reservoir ◽

Chennai City

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

Journal of Applied Mathematics ◽

10.1155/2014/239868 ◽

2014 ◽

Vol 2014 ◽

pp. 1-17 ◽

Cited By ~ 8

Author(s):

Farnaz Barzinpour ◽

Mohsen Saffarian ◽

Ahmad Makoui ◽

Ebrahim Teimoury

Keyword(s):

Genetic Algorithm ◽

Efficient Solution ◽

Simulated Annealing Algorithm ◽

Computational Results ◽

Distribution Centers ◽

Solution Approach ◽

Location Allocation ◽

Relief Distribution ◽

Annealing Algorithm ◽

Proper Response

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.

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