疫情事件下多灾点应急资源最优化配置研究

新冠疫情的爆发,使许多地区成为灾区,为了及时对灾区进行救援,灾后应急资源精准供给成为保障灾区人民安全的首要因素。本文利用SEIR预测决策时刻各灾区感染人数,由此计算灾区紧迫程度权重与物资需求量。基于紧迫程度构建以灾民满意度最大化、总成本最小化和考虑分配公平的应急资源调度多目标优化模型。提出多目标人工蜂群算法。针对人工蜂群算法易早熟等缺点,利用动态参数思想与Pareto解集来定义新的蜂群位置更新公式,利用教学优化思想对蜂群位置进行扰动,以避免算法陷入局部极值。通过算例进行模拟实验,结果表明,所提出的模型和算法可以有效解决疫情事件下多灾点应急资源最优化配置问题,且改进算法的性能更优。

Study on Optimal Allocation of Emergency Resources in Multiple Disaster Sites Under Epidemic Events

The outbreak of COVID-19 has turned many areas into disaster areas.In order to provide timely relief to the disaster areas,accurate supply of post-disaster emergency resources has become the primary factor to ensure the safety of the people in the disaster areas.In this paper,SEIR was used to predict the number of infected people in each disaster area at the decision-making moment,and then the weight of urgency degree and material demand in the disaster area were calculated.Based on the degree of urgency,a multi-objective optimization model of emergency resource scheduling was constructed to maximize the satisfaction of the victims,minimize the total cost and consider the fairness of distribution.A multi-objective artificial bee colony algorithm is proposed.Aiming at the disadvantages of artificial bee colony algorithm such as precocity,the dynamic parameter and Pareto solution set are used to define the new bee colony location updating formula,and the teaching optimization is used to disturb the bee colony location,so as to avoid the algorithm falling into local extremum.The simulation results show that the proposed model and algorithm can effectively solve the problem of optimal allocation of emergency resources at multiple disaster points under epidemic events,and the improved algorithm has better performance.