为了提高应急物资需求预测的精度,基于应急物资需求预测问题的特点,引入对称三角模糊数表示影响因素的模糊特征,建立基于多元模糊线性回归的应急物资需求预测模型,并给出多元模糊线性回归预测模型的参数估计方法,通过实证案例分析,验证预测方法的有效性.结果表明,灾害级别、受灾人口、受灾面积是影响应急物资需求预测的重要因素,针对灾害级别、受灾人口、受灾面积等因素的不确定性特征,用对称三角模糊数表征有关模糊属性,有助于提高应急物资需求预测的准确性.
In order to improve the prediction accuracy, based on the characteristics of emergency supplies demand prediction, the symmetric triangle fuzzy numbers were introduced in this paper to describe fuzzy feature of effect factors of emergency demand.The emergency supplies demand prediction method based on multiple fuzzy linear regression model was proposed, the parameter estimation method of the proposed model was presented.Finally, a numerical example shows that the method is valid.The results show that the disaster level, the affected population and the damage area are important factors influencing the emergency supplies demand prediction.To deal with the uncertainty attribute in the process of predicting emergency demands, using the symmetric triangular fuzzy number to represent the fuzzy attributes can improve the accuracy of emergency supplies demand prediction.
[1] 赵小柠,马昌喜.基于范例推理的灾害性地震应急物资需求预测研究[J].中国安全科学学报,2012,22(8):3-9.DOI:10.16265/j.cnki.issn1003-3033.2012.08.001. ZHAO X N,MA C X.Research on predicting emergency material demand after disastrous earthquake based on case-based reasoning[J].China Safety Science Journal, 2012, 22(8):3-9.DOI:10.16265/j.cnki.issn1003-3033.2012.08.001.
[2] 刘德元,朱昌锋.基于案例模糊推理的应急物资需求预测研究[J].兰州交通大学学报,2013,32(1):138-141.DOI:10.3969/j.issn.1001-4373.2013.01.031. LIU D Y, ZHU C F.Forecasting research of emergency supplies demand on the case of fuzzy reasoning[J].Journal of Lanzhou Jiaotong University, 2013, 32(1):138-141.DOI:10.3969/j.issn.1001-4373.2013.01.031.
[3] 王兰英,郭子雪,张玉芬,等.基于直觉模糊案例推理的应急物资需求预测模型[J].中国矿业大学学报,2015,44(4):775-780.DOI:10.13247/j.cnki.jcumt.000375. WANG L Y,GUO Z X,ZHANG Y F,et al.An emergency supplies demand prediction model based on intuitionistic fuzzy Case-based reasoning[J].Journal of China University of Mining &Technology, 2015,44(4):775-780.DOI:10.13247/j.cnki.jcumt.000375.
[4] 赵一兵,高虹霓,冯少博.基于支持向量机回归的应急物资需求预测[J].计算机仿真,2013,30(8):408-412.DOI:10.3969/j.issn.1006-9348.2013.08.095. ZHAO Y B, GAO H N, FENG S B.Emergency material demand prediction based on support vector machine regression[J].Computer Simulation, 2013, 30(8):408-412.DOI:10.3969/j.issn.1006-9348.2013.08.095.
[5] 王正新, 刘思峰.基于Fourier-GM(1, 1)模型的灾害应急物资需求量预测[J].系统工程,2013,31(8):60-64. WANG Z X, LIU S F.Forecasting demand of the disaster emergency supplies based on Fourier-GM(1,1)model[J].Systems Engineering, 2013, 31(8):60-64.
[6] 钱枫林,崔健.BP神经网络模型在应急需求预测中的应用——以地震伤亡人数预测为例[J].中国安全科学学报,2013,23(4):20-25.DOI:10.16265/j.cnki.issn1003-3033.2013.04.017. QIAN F L, CUI J.Application of BP neural network analysis in forecasting emergency demand:A case study on earthquake casualty forecasting[J].China Safety Science Journal, 2013, 23(4):20-25.DOI:10.16265/j.cnki.issn1003-3033.2013.04.017.
[7] SUN B Z, MA W M, ZHAO H Y.A fuzzy rough set approach to emergency material demand prediction over two universes[J].Applied Mathematical Modelling, 2013,37: 7062-7070.
[8] SHEU J B.Dynamic relief-demand management for emergency logistics operations under large-scale disasters[J].Transportation Research Part E,2010,46(1):1-17.
[9] MOHAMMADI R, GHOMI S M T F, ZEINALI F.A new hybrid evolutionary based RBF networks method for forecasting time series: A case study of forecasting emergency supply demand time series[J].Engineering Applications of Artificial Intelligence, 2014(36): 204-214.
[10] 韩立岩,汪培庄.应用模糊数学[M].首都经贸大学出版社,1998. HAN L Y, WANG P Z.Applied fuzzy mathematics[M].Capital University of Economics and Business press, 1998.