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.
Highlights
It is inevitable to have an integrated scientific system for crisis’ logistics management with clearly defined functions and duties
It is clear that logistic activities are very important in response phase of the disaster management
Precise programming can improve the efficacy of the system
Summary
It is inevitable to have an integrated scientific system for crisis’ logistics management with clearly defined functions and duties. Balcik and Beamon [2] considered the facility location problem for humanitarian relief chains in order to respond to quick onset disasters Their proposed model aimed to locate and determine the situation and the number of distribution centers in relief network and the number of stored commodities in order to meet the demand of affected people. Mete and Zabinsky [12] introduced a stochastic optimization model for disaster preparedness and response under demand/cost uncertainty in order to assist deciding on the location and allocation of medical supplies which are used during emergencies They offered a mixedinteger programming transportation model that is potentially useful in routing decisions during the response phase. We propose a genetic algorithm and a simulated annealing algorithm to biobjective location–allocation problem addressed
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