基于加速失效时间模型的企业停产与恢复时间研究

doi:10.16265/j.cnki.issn1003-3033.2023.06.0990

摘要/Abstract

摘要：

为了帮助企业制定灾后恢复对策,更好地应对灾害威胁,利用加速失效时间(AFT)模型,研究受灾企业停产与恢复时间的影响因素与模型参数。首先以恩施市2020年7月17日发生的暴雨洪涝灾害为例,基于实地调查收集到的377份数据样本,构建AFT模型;其次将影响企业停产与恢复的10个因素作为解释变量,估算洪涝灾害情景下商业企业的停产与恢复时间参数;然后基于恩施市数据与已有余姚市数据,探究同一灾害下不同影响区域的差异性。结果表明:淹没水深、库存损失率、垃圾清运时间和灾前预警时间等是影响企业停产的主要因素。淹没水深、停水时间和库存损失率等是影响企业恢复的主要因素;相比于其他类型的商业企业,住宿和餐饮业企业的平均恢复时间最短。

关键词: 加速失效时间(AFT)模型, 停产时间, 恢复时间, 洪涝灾害

Abstract:

In order to help enterprises develop post-disaster recovery countermeasures and better respond to disaster threats, AFT model was used to study the influencing factors and model parameters of the shutdown and recovery time of disaster-stricken enterprises. Then, taking the torrential rain and flood disaster that occurred in Enshi City on July 17, 2020, as an example, based on 377 data samples collected from field investigation, an AFT model was constructed, and 10 factors affecting the shutdown and recovery of enterprises were used as explanatory variables to estimate the shutdown and recovery time parameters of commercial enterprises under the flood disaster scenario. Then, based on the data of Enshi City and the existing data of Yuyao City, the differences between different affected areas under the same disaster were explored. The results show that the depth of submerged water, inventory loss rate, garbage removal time and pre-disaster warning time are the main factors affecting the shutdown of enterprises. The depth of submerged water, the time of water outage and the rate of inventory loss are the main factors affecting the recovery of enterprises. Compared to other types of commercial enterprises, businesses in the accommodation and restaurant industry have the shortest average recovery time.

Key words: accelerated failure time(AFT)model, enterprise's stagnation, flood disaster, affecting factors

杨丽娇, 刘岩, 蒋新宇. 基于加速失效时间模型的企业停产与恢复时间研究[J]. 中国安全科学学报, 2023, 33(6): 181-189.

YANG Lijiao, LIU Yan, JIANG Xinyu. Research on stagnation time and recovery time of enterprises based on accelerated failure time model[J]. China Safety Science Journal, 2023, 33(6): 181-189.

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分布函数	停产时间模型AIC值	恢复时间模型AIC值
Weibull分布	3 478.34	2 622.61
对数正态分布	3 493.67	2 659.25
指数分布	3 478.74	2 636.82
Log-logistic分布	3 485.21	2 639.11

变量名称	回归系数	标准误差	χ²/P	比例
居民服务业N=56
常数项	2.735^***	0.281	15.441/ 0.017	0.565
垃圾清运时间	0.055^**	0.021
灾前年营业额	1.8×10^-4*	1.5×10^-4
其他服务业N=45
常数项	0.956^***	0.172	71.592/ 4.8×10^-14	0.293
停电时间	0.021^**	0.007
灾前预警时间	-0.017^**	0.005
设备损毁率	0.011^***	0.001
停水时间	0.019^**	0.009
批发和零售业N=186
常数项	2.470^***	0.142	51.463/ 3.9×10^-11	0.745
垃圾清运时间	0.031^***	0.008
库存损失率	0.010^***	0.002
灾前预警时间	-0.022^*	0.009
淹没水深	0.115^*	0.04
住宿和餐饮业N=43
常数项	1.935^***	0.300	16.850/ 0.001	0.709
淹没水深	0.209^*	0.092
库存损失率	0.011^**	0.003
灾前预警时间	-0.037^*	0.015

变量名称	回归系数	标准误差	χ²/P	比例值
居民服务业N=43
常数项	4.481^***	0.223	8.55/ 0.036	0.639
垃圾清运时间	0.065^*	0.031
停水时间	0.046^*	0.019
灾前年营业额	2.8×10^-4*	1.7×10^-4
其他服务业N=40
常数项	3.058^***	0.474	13.81/ 0.008	0.482
垃圾清运时间	0.119^***	0.036
库存损失率	0.007^*	0.003
设备损毁率	0.011^**	0.004
停水时间	0.033^*	0.018
批发和零售业N=121
常数项	3.928^***	0.191	22.31/ 1.4×10^-5	0.671
淹没水深	0.130^*	0.045
库存损失率	0.008^***	0.002
住宿和餐饮业N=32
常数项	3.328^***	0.288	17.85/ 0.003	0.561
库存损失率	0.013^***	0.003
停电时间	0.043^*	0.018
垃圾清运时间	0.125^**	0.047
设备损毁率	0.009^*	0.004
停水时间	0.064^*	0.024
淹没水深	0.068	0.081

企业类型	停产时间		恢复时间
企业类型	λ	α	λ	α
居民服务业	1.6	21.2	1.5	88.4
其他服务业	1.3	2.9	2.1	21.2
批发和零售业	1.3	9.7	1.4	50.4
住宿和餐饮业	1.4	6.9	1.7	27.8

数据来源	批发和零售业		住宿和餐饮业
数据来源	平均停产时间	平均恢复时间	平均停产时间	平均恢复时间
恩施市数据	31	86	15	56
余姚市融合数据	30	87	17	53