Abstract
This paper presents a flexible emergency rescue system which is chiefly composed of three parts, namely, disaster assistance center, relief vehicles, and disaster areas. A novel objective of utility maximization is used to evaluate the entire system in disasters. Considering the uncertain road conditions in the relief distribution, we implement triangular fuzzy number to calculate the vehicle velocity. As a consequence, a fuzzy mathematical model is built to maximize the utility of emergency rescue system and then converted to the crisp counterpart. Finally, the results of numerical experiments obtained by particle swarm optimization (PSO) prove the validity of this proposed mathematical model.
Highlights
The vehicle routing problem (VRP) can usually be described as choosing a better route travelled by the given vehicle so as to meet the customer demands efficiently
This paper presents a flexible emergency rescue system which is composed of three parts, namely, disaster assistance center, relief vehicles, and disaster areas
The flexible emergency rescue system is presented in this paper, including disaster assistance center, relief vehicles, and disaster areas
Summary
The vehicle routing problem (VRP) can usually be described as choosing a better route travelled by the given vehicle so as to meet the customer demands efficiently. Tian et al [2] considered the fuzzy demands and applied particle swarm optimization (PSO) to tackle the multiobjective problem. Sheu [3] introduced a three-layer supply network and applied a hybrid fuzzy grouping method to cope with the distribution operation from perspectives of minimizing the costs and maximizing the demand fill rate synchronously. Sun et al [4] proposed an emergency location-routing problem and aimed at minimizing the total time and cost. Ji and Zhu [5] introduced the salvable degree to emergency supply chain and a multiobjective optimization model is developed to minimize travel time and satisfy the demands of affected areas as much as possible. Yi and Kumar [9] introduced a metaheuristic of ant colony optimization to solve the emergency logistics problem and obtained a series of excellent solutions.
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