About: Emergency logistics distribution of disasters and accidents is an effective means to reduce the loss of lives and property. On the condition of meeting the timeliness requirement of emergency logistics, the study of emergency logistics distribution can rationally schedule vehicle, substantially reduce the vehicle allocation time and the logistical cost. Through the analysis of the characteristics of rescue emergency logistics, the system structure of emergency logistics distribution is proposed. After the material distribution optimization model for emergency logistics is established, an improved genetic algorithm is designed to solve this problem. In improved genetic algorithm, the best individual reservations, roulette selection, blend crossover, and blend mutation have been adopted to avoid premature convergence and enhance the process efficiency. A numeric example is presented to validate the feasibility and effectiveness of the model and its algorithm.   Goto Sponge  NotDistinct  Permalink

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  • Emergency logistics distribution of disasters and accidents is an effective means to reduce the loss of lives and property. On the condition of meeting the timeliness requirement of emergency logistics, the study of emergency logistics distribution can rationally schedule vehicle, substantially reduce the vehicle allocation time and the logistical cost. Through the analysis of the characteristics of rescue emergency logistics, the system structure of emergency logistics distribution is proposed. After the material distribution optimization model for emergency logistics is established, an improved genetic algorithm is designed to solve this problem. In improved genetic algorithm, the best individual reservations, roulette selection, blend crossover, and blend mutation have been adopted to avoid premature convergence and enhance the process efficiency. A numeric example is presented to validate the feasibility and effectiveness of the model and its algorithm.
subject
  • Mutation
  • Logistics
  • Machine learning
  • Business terms
  • Cybernetics
  • Human activities with impact on the environment
  • Systems engineering
  • Genetic algorithms
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