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浙江大学学报(工学版)
浙江大学学报(工学版)  2024, Vol. 58 Issue (1): 96-108    DOI: 10.3785/j.issn.1008-973X.2024.01.011
交通工程、土木工程     
道路网络多阶段抗灾能力优化模型构建与应用
刘鹏1(),路庆昌1,*(),秦汉1,崔欣2
1. 长安大学 电子与控制工程学院，陕西 西安 710064
2. 大连海事大学 航运经济与管理学院，辽宁 大连 116026
Road network multi-stage disaster resistance optimization model and its application
Peng LIU1(),Qingchang LU1,*(),Han QIN1,Xin CUI2
1. School of Electronics and Control Engineering, Chang’an University, Xi’an 710064, China
2. School of Maritime Economics and Management, Dalian Maritime University, Dalian 116026, China
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摘要：

为了降低路网抗灾成本并保障路网快速连通，研究道路交通网络多阶段灾害应对能力优化的问题. 建立综合灾前应急工作站点选址和灾后路网恢复决策的3层规划模型，其中特别考虑了应急救援设施和后勤保障资源在耗尽性、运输方式和恢复作用上的差异，定量建模了两者间的相互协作关系. 通过综合双层遗传算法和Frank-Wolfe算法，获得模型的近似最优解. 研究结果表明，与未考虑灾后恢复过程的灾前布设决策和未考虑后勤保障资源的灾前布设决策相比，最优决策可以分别降低10.96%的后勤保障资源运输成本和11.51%的加权恢复成本. 后勤保障资源和应急救援设施布设数量共同影响路网的恢复效果，忽略两者间的协作关系，将会高估应急救援设施布设数量增加对恢复效果的影响.
关键词： 交通工程;  道路交通网络;  抗灾能力优化;  3层优化模型;  遗传算法    
Abstract:

The optimization problem of multi-stage disaster response capacity of road transportation network was analyzed in order to reduce the cost of disaster response for road network and ensure the rapid connectivity of road network. A three-layer planning model for the selection of comprehensive pre-disaster emergency workstations and post-disaster road network recovery decisions was established. The differences in exhaustibility, transportation mode and recovery effect of emergency rescue equipments and logistics support resources were specially considered, and the interdependent relationship between the two was quantitatively modeled. An approximate optimal solution for the model was obtained by combining the bi-level genetic algorithm and the Frank-Wolfe algorithm. The research results show that the optimal decisions can respectively reduce the transportation cost of logistics support resources by 10.96% and the weighted recovery cost by 11.51% compared with the pre-disaster deployment decisions not considering the post-disaster recovery process and the decisions not considering the pre-disaster layout decision of logistics support resources. The quantity of logistics support resources and emergency rescue equipments layout jointly affect the recovery effect of the road network. The impact of increasing the quantity of emergency rescue equipments on the recovery effect will be overestimated if the interdependent relationship between the two is neglected.
Key words: traffic engineering    road traffic network    disaster resilience optimization    tri-level programming model    genetic algorithm
收稿日期: 2023-03-08 出版日期: 2023-11-07
CLC:  U 491  
基金资助: 国家自然科学基金资助项目（71971029）；霍英东教育基金会高等院校青年教师基金资助项目（171069）；陕西省自然科学基础研究计划资助项目（2021JC-28）
通讯作者: 路庆昌     E-mail: pliu@chd.edu.cn;qclu@chd.edu.cn
作者简介: 刘鹏（1998—），男，硕士生，从事交通网络建模与优化的研究. orcid.org/0009-0008-3934-8991. E-mail： pliu@chd.edu.cn
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引用本文:

刘鹏,路庆昌,秦汉,崔欣. 道路网络多阶段抗灾能力优化模型构建与应用[J]. 浙江大学学报(工学版), 2024, 58(1): 96-108.

Peng LIU,Qingchang LU,Han QIN,Xin CUI. Road network multi-stage disaster resistance optimization model and its application. Journal of ZheJiang University (Engineering Science), 2024, 58(1): 96-108.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2024.01.011    或    https://www.zjujournals.com/eng/CN/Y2024/V58/I1/96

图 1  3层模型求解算法的流程图
图 2  下层遗传算法解码过程的流程图
图 3  贵州省区域高速公路网
$ a $ $ {z_{a,{t_0}}} $/min $ {C_{a,{t_0}}} $/( $ {\text{pcu}} \cdot {{\text{h}}^{ - 1}} $) $ {l_a} $/km $ a $ $ {z_{a,{t_0}}} $/min $ {C_{a,{t_0}}} $/( $ {\text{pcu}} \cdot {{\text{h}}^{ - 1}} $) $ {l_a} $/km $ a $ $ {z_{a,{t_0}}} $/min $ {C_{a,{t_0}}} $/( $ {\text{pcu}} \cdot {{\text{h}}^{ - 1}} $) $ {l_a} $/km
1 33 4 200 40 10 62 4 200 89 19 18 4 200 23
2 65 4 200 104 11 23 4 200 32 20 46 4 200 52
3 25 4 200 32 12 76 4 200 95 21 45 4 200 61
4 22 4 200 28 13 56 4 200 70 22 29 4 200 39
5 50 4 200 69 14 56 4 200 74 23 61 4 200 79
6 76 4 200 92 15 45 4 200 56 24 81 4 200 106
7 20 4 200 26 16 59 4 200 71 25 61 4 200 74
8 42 4 200 54 17 44 4 200 61 26 85 4 200 108
9 34 4 200 47 18 28 4 200 33 27 45 4 200 60
表 1  贵州省区域高速公路网的路段参数
图 4  路网各节点的客流介数
$ a $ $ {d_a} $/% $ a $ $ {d_a} $/% $ a $ $ {d_a} $/%
1 100 6 100 13 100
2 50 7 75 14 25
3 25 8 25 15 50
4 75 9 75 16 25
5 100 11 75 17 50
表 2  受损路段的参数
B 站点选址
最优决策P 决策P1 决策P2
1 11 11 9
2 10,17 10,11 4,8
3 4,7,11 4,11,17 3,5,8
4 4,7,8,11 4,10,13,17 3,5,7,9
5 3,4,7,8,9 4,7,10,11,17 3,5,7,8,9
表 3  不同预算下的灾前工作站点选址决策
图 5  不同预算下不同灾前布设方案的灾后路网恢复效果
图 6  B = 2下灾前布设决策P和决策P2对应的灾后路网恢复排程甘特图
图 7  B = 2下不同 $ {R_{{\text{E}},u}} $最优恢复决策的灾后路网恢复效果
图 8  B = 2下不同 $ {R_{{\text{M}},u}} $最优恢复决策的灾后路网恢复效果
图 9  B = 2下不同 $ {R_{{\text{E}},u}} $的未考虑后勤保障资源的恢复决策灾后路网恢复效果
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