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浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2024, Vol. 58 Issue (1): 96-108    DOI: 10.3785/j.issn.1008-973X.2024.01.011
    
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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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     
Received: 08 March 2023      Published: 07 November 2023
CLC:  U 491  
Fund:  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）
Corresponding Authors: Qingchang LU     E-mail: pliu@chd.edu.cn;qclu@chd.edu.cn
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Peng LIU
Qingchang LU
Han QIN
Xin CUI
Cite this article:

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.

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


道路网络多阶段抗灾能力优化模型构建与应用

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 
Fig.1 Flowchart of three-layer model for solving algorithm
Fig.2 Flowchart of decoding process for lower-level genetic algorithm
Fig.3 Guizhou province regional expressway network
$ 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
Tab.1 Link parameters of Guizhou regional expressway network
Fig.4 Passenger betweennesses of each node of road network
$ 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
Tab.2 Parameters of damaged road section
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
Tab.3 Pre-disaster work site deployment decisions under different budgets
Fig.5 Post-disaster road network recovery effects of different pre-disaster deployment decisions under different budgets
Fig.6 Post-disaster road network recovery effects of pre-disaster deployment decision P and decision P2 under budget B = 2
Fig.7 Post-disaster road network recovery effect of optimal recovery decision under different $ {R_{{\text{E}},u}} $ with B = 2
Fig.8 Post-disaster road network recovery effect of optimal recovery decision under different $ {R_{{\text{M}},u}} $ with B = 2
Fig.9 Post-disaster road network recovery effects of recovery decisions without considering logistical support resources for different $ {R_{{\text{E}},u}} $ under B = 2
[1]   ÇOBAN B, SCAPARRA M P, O'HANLEY J R Use of OR in earthquake operations management: a review of the literature and roadmap for future research[J]. International Journal of Disaster Risk Reduction, 2021, 65: 102539
doi: 10.1016/j.ijdrr.2021.102539
[2]   AKBARI V, SADATI M E H, KIAN R A decomposition-based heuristic for a multicrew coordinated road restoration problem[J]. Transportation Research Part D: Transport and Environment, 2021, 95: 102854
doi: 10.1016/j.trd.2021.102854
[3]   AJAM M, AKBARI V, SALMAN F S Routing multiple work teams to minimize latency in post-disaster road network restoration[J]. European Journal of Operational Research, 2022, 300 (1): 237- 254
doi: 10.1016/j.ejor.2021.07.048
[4]   LI X, ZHAO Z, ZHU X, et al Covering models and optimization techniques for emergency response facility location and planning: a review[J]. Mathematical Methods of Operations Research, 2011, 74 (3): 281- 310
doi: 10.1007/s00186-011-0363-4
[5]   GUO J, DU Q, HE Z A method to improve the resilience of multimodal transport network: location selection strategy of emergency rescue facilities[J]. Computers and Industrial Engineering, 2021, 161: 107678
doi: 10.1016/j.cie.2021.107678
[6]   BABABEIK M, KHADEMI N, CHEN A Increasing the resilience level of a vulnerable rail network: the strategy of location and allocation of emergency relief trains[J]. Transportation Research Part E: Logistics and Transportation Review, 2018, 119: 110- 128
doi: 10.1016/j.tre.2018.09.009
[7]   FENG J R, GAI W, LI J Multi-objective optimization of rescue station selection for emergency logistics management[J]. Safety Science, 2019, 120: 276- 282
doi: 10.1016/j.ssci.2019.07.011
[8]   LI Z, JIN C, HU P, et al Resilience-based transportation network recovery strategy during emergency recovery phase under uncertainty[J]. Reliability Engineering and System Safety, 2019, 188: 503- 514
doi: 10.1016/j.ress.2019.03.052
[9]   LIU K, ZHAI C, DONG Y Optimal restoration schedules of transportation network considering resilience[J]. Structure and Infrastructure Engineering, 2021, 17 (8): 1141- 1154
doi: 10.1080/15732479.2020.1801764
[10]   MAYA-DUQUE P A, DOLINSKAYA I S, SÖRENSEN K Network repair crew scheduling and routing for emergency relief distribution problem[J]. European Journal of Operational Research, 2016, 248 (1): 272- 285
doi: 10.1016/j.ejor.2015.06.026
[11]   MORENO A, MUNARI P, ALEM D A branch-and-benders-cut algorithm for the crew scheduling and routing problem in road restoration[J]. European Journal of Operational Research, 2019, 275 (1): 16- 34
doi: 10.1016/j.ejor.2018.11.004
[12]   ZHANG Z, JI T, WEI H H Assessment of post-earthquake resilience of highway–bridge networks by considering downtime due to interaction of parallel restoration actions[J]. Structure and Infrastructure Engineering, 2023, 19 (5): 589- 605
doi: 10.1080/15732479.2021.1961826
[13]   YAN S, LIN C K, CHEN S Y Optimal scheduling of logistical support for an emergency roadway repair work schedule[J]. Engineering Optimization, 2012, 44 (9): 1035- 1055
doi: 10.1080/0305215X.2011.628389
[14]   CHANG Y, WILKINSON S, SEVILLE E, et al Resourcing for a resilient post-disaster reconstruction environment[J]. International Journal of Disaster Resilience in the Built Environment, 2010, 1 (1): 65- 83
doi: 10.1108/17595901011026481
[15]   LI S, MA Z, TEO K L A new model for road network repair after natural disasters: integrating logistics support scheduling with repair crew scheduling and routing activities[J]. Computers and Industrial Engineering, 2020, 145: 106506
doi: 10.1016/j.cie.2020.106506
[16]   HACKL J, ADEY B T, LETHANH N Determination of near-optimal restoration programs for transportation networks following natural hazard events using simulated annealing[J]. Computer-Aided Civil and Infrastructure Engineering, 2018, 33 (8): 618- 637
doi: 10.1111/mice.12346
[17]   SANCI E, DASKIN M S Integrating location and network restoration decisions in relief networks under uncertainty[J]. European Journal of Operational Research, 2019, 279 (2): 335- 350
doi: 10.1016/j.ejor.2019.06.012
[18]   NOGAL M, O'CONNOR A, CAULFIELD B, et al Resilience of traffic networks: from perturbation to recovery via a dynamic restricted equilibrium model[J]. Reliability Engineering and System Safety, 2016, 156: 84- 96
doi: 10.1016/j.ress.2016.07.020
[19]   左逢源, 王晓峰, 牛进, 等 求解最小费用最大流问题的信念传播算法[J]. 计算机应用研究, 2021, 38 (7): 1998- 2002
ZUO Feng-yuan, WANG Xiao-feng, NIU Jin, et al Belief propagation algorithm for solving minimum cost maximum flow problem[J]. Application Research of Computers, 2021, 38 (7): 1998- 2002
doi: 10.19734/j.issn.1001-3695.2020.10.0357
[20]   李兆隆. 基于弹复性的公路网络灾后恢复决策优化研究[D]. 大连: 大连理工大学, 2019: 1-172.
LI Zhao-long. Research on the resilience-based decision optimization of the post-disaster road network recovery [D]. Dalian: Dalian University of Technology, 2019: 1-172.
[21]   路庆昌, 崔欣, 谢驰, 等 城市轨道交通网络关键站点识别方法对比与分析[J]. 北京交通大学学报, 2022, 46 (3): 18- 25
LU Qing-chang, CUI Xin, XIE Chi, et al Comparison and analysis of identification methods for critical stations in urban rail transit networks[J]. Journal of Beijing Jiaotong University, 2022, 46 (3): 18- 25
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