疫情事件下多灾点应急资源最优化配置研究

王付宇; 汤涛; 李艳; 王小牛; 王付宇; 汤涛; 李艳; 王小牛

doi:10.13306/j.1672-3813.2021.01.008

复杂系统与复杂性科学 >

2021 , Vol. 18 >Issue 1: 53 - 62

DOI: https://doi.org/10.13306/j.1672-3813.2021.01.008

疫情事件下多灾点应急资源最优化配置研究

王付宇 ,
汤涛 ,
李艳 ,
王小牛 ,
王付宇 ,
汤涛 ,
李艳 ,
王小牛

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1.安徽工业大学 a.管理科学与工程学院 b.复杂系统多学科管理与控制安徽普通高校重点实验室,安徽马鞍山 243002;
2.马鞍山市人民医院普外科,安徽马鞍山 243000

王付宇(1977),男,河南泌阳人,博士,教授,主要研究方向为生产运作管理、智能优化算法。

收稿日期: 2020-07-24

修回日期: 2020-10-13

网络出版日期: 2020-12-28

基金资助

安徽省哲学社会科学规划项目(AHSKY2018D15)

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

WANG Fuyu ,
TANG Tao ,
LI Yan ,
WANG Xiaoniu ,
WANG Fuyu ,
TANG Tao ,
LI Yan ,
WANG Xiaoniu

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1. a.School of Management Science and Engineering, b.Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes, Anhui University of Technology, Maanshan 243002, China;
2. General Surgery Department, Maanshan People's Hospital, Maanshan 243000, China

Received date: 2020-07-24

Revised date: 2020-10-13

Online published: 2020-12-28

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摘要

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。

关键词： 新冠疫情; 应急资源精准供给; 需求紧迫度; 多目标优化; 人工蜂群算法

本文引用格式

王付宇 , 汤涛 , 李艳 , 王小牛 , 王付宇 , 汤涛 , 李艳 , 王小牛 . 疫情事件下多灾点应急资源最优化配置研究[J]. 复杂系统与复杂性科学, 2021 , 18(1) : 53 -62 . DOI: 10.13306/j.1672-3813.2021.01.008

Abstract

The outbreak of COVID19 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.

Key words： COVID19; precise supply of emergency resources; demand urgency; multi-objective optimization; artificial bee colony algorithm

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