Abstract:

In recent years， emergencies have occurred frequently， causing a certain threat to the stable and prosperous development of the country and the happy and healthy life of the people. After the occurrence of emergencies， effective emergency material distribution should be taken in time to avoid more serious consequences. At the beginning of the corresponding stage， there is a shortage of supplies as well as road damage. When the supply of materials is insufficient， the dissatisfaction of the affected points is reduced as much as possible according to different needs， and the materials are distributed fairly. After the emergency， the road network will be damaged to different degrees， which in turn will affect the transportation time of supplies and aggravate the losses of the affected sites. The emergency logistics path optimization problem is addressed， taking into account the fair distribution of materials and road damage. A two-stage model of emergency logistics path optimization under the situation of road damage and lack of emergency supplies is established. And the adaptive hybrid artificial fish swarm algorithm is designed to solve the model， and the model and algorithm are validated by some data in Solomon's calculation case， revealing the influence of maximum vehicle load and driving speed. The results show that （1） compared with the artificial fish swarm algorithm， the AH-AFSA algorithm is able to obtain better results in solving the emergency logistics vehicle path optimization problem considering the fair distribution of materials and road damage， with a 12.9% improvement in total vehicle transportation time and a 29.0% improvement in algorithm running time. （2） Compared with AFSA， AH-AFSA algorithm can converge faster in the early stage of the algorithm and converge faster； it still has stronger local search capability in the late iteration of the algorithm and can find better results. （3） Finally， the changes of the maximum load and range of the vehicle affect the total vehicle transportation time. In general， the total vehicle transportation time decreases with the increase of the maximum load weight， and produces some volatility with the change of the vehicle range distance. These results provide good insights for the solution of the emergency vehicle path optimization problem under the situation of road damage and material shortage.

Key words: emergency logistics, vehicle path, early response, fairness, artificial fish swarm algorithm