Advertisement

Snow removal resource location and allocation optimization for urban road network recovery: a resilience perspective

  • Jing WangEmail author
  • Haotian Liu
Original Research
  • 105 Downloads

Abstract

Recently, the vulnerability of urban road network has become increasingly obvious in the face of natural emergencies. The extreme snow weather, a kind of natural emergencies, can severely reduce the service capability of road network. It has attracted a wide attention that how the urban road network system get recovered after such unexpected events. In this paper, we proposed the urban road network resilience evacuation method under snow event. In order to improve the resilience of road network, we establish the mathematical model for road network recovery under extreme weather to solve the snow removal resource location-allocation problem (LAP) with uncertain weather information. The routes for snow removal vehicle are determined as several Vehicle Routing Problems (VRPs). The corresponding tabu search algorithm is designed. Finally, we verify the effectiveness of proposed model and algorithm by a real case to provide decision-making support for the city traffic management departments and enhance the resilience of city in the extreme snow weather.

Keywords

Resilience Snow removal problem Urban road network Location and allocation problem 

Notes

Acknowledgements

This Project has received funding from National Social Science Foundation of China (16CGL033); Funding Program for High-level Talents of Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality of China (CIT&TCD201704033).

References

  1. Aven T (2011) On Some Recent Definitions and Analysis Frameworks for Risk, Vulnerability, and Resilience. Risk Anal 31(4),515–522CrossRefGoogle Scholar
  2. Bles T, Bessembinder J, Chevreuilc M, Danielsson P, Falemo S, Venmans A, Ennesserc Y, Löfroth H (2016) Climate change risk assessments and adaptation for roads-results of the ROADAPT project. Transp Res Procedia 14:58–67CrossRefGoogle Scholar
  3. Boonmee C, Arimura M, Asada T (2017) Facility location optimization model for emergency humanitarian logistics. Int J Disaster Risk Reduct 24:485–498CrossRefGoogle Scholar
  4. Campbell JF (1995) Operations management for urban snow removal and disposal. Transp Res Part A Policy Pract 29(5):359–370CrossRefGoogle Scholar
  5. Cui JX, Liu F, Hu J, Janssens D, Wets G, Cools M (2016) Identifying mismatch between urban travel demand and transport network services using GPS data: a case study in the fast growing Chinese city of Harbin. Neurocomputing 181:4–18CrossRefGoogle Scholar
  6. Dussault B, Golden B, Wasil E (2014) The Downhill plow problem with multiple plows. J Oper Res Soc 65:1465–1474CrossRefGoogle Scholar
  7. Francis R, Bekera B (2014) A metric and frameworks for resilience analysis of engineered and infrastructure systems. Reliab Eng Syst Saf 121:90–103CrossRefGoogle Scholar
  8. Fu L, Trudel M, Kim V (2009) Optimization winter road maintenance operations under real time information. Eur J Oper Res 196:332–341CrossRefzbMATHGoogle Scholar
  9. Holling CS (1973) Resilience and stability of ecological systems. Ann Rev Ecol Syst 4:1–23CrossRefGoogle Scholar
  10. Hu SL, Han CF, Meng LP (2015) A scenario planning approach for propositioning rescue centers for urban waterlog disasters. Comput Ind Eng 87:425–435CrossRefGoogle Scholar
  11. Hualou L, Tu S, Ge D, Li T, Liu Y (2016) The allocation and management of critical resources in rural China under restructuring: problems and prospects. J Rural Stud 47(Part B):392–412Google Scholar
  12. Ip WH, Wang D (2011) Resilience and friability of transportation networks: evaluation, analysis and optimization. IEEE Syst J 5(2):189–198CrossRefGoogle Scholar
  13. Jägerbrand AK, Sjöbergh J (2016) Effects of weather conditions, light conditions, and road lighting on vehicle speed. SpringerPlus 5:505CrossRefGoogle Scholar
  14. Levalle RR, Nof SY (2017) Resilience in supply networks: definition, dimensions, and levels. Ann Rev Control 43:224–236CrossRefGoogle Scholar
  15. Lv Y, Huang GH, Guo L et al (2013) A scenario-based modeling approach for emergency evacuation management and risk analysis under multiple uncertainties. J Hazard Mater 246:234–244CrossRefGoogle Scholar
  16. Nan C, Sansavini G (2017) A quantitative method for assessing resilience of independent infrastures. Reliab Eng Syst Saf 157:35–53CrossRefGoogle Scholar
  17. Perrier N, Langevin A, Campbell JF (2008) The sector design and assignment problem for snowdisposal operations. Eur J Oper Res 189:508–525CrossRefzbMATHGoogle Scholar
  18. Salman FS, Yücel E (2015) Emergency facility location under random network damage: Insights from the Istanbul case. Comput Oper Res 62:266–281MathSciNetCrossRefzbMATHGoogle Scholar
  19. Starita S, Scaparra M, O’Hanley P (2017) A dynamic model for road protection against flooding. J Oper Res Soc 68(1):174–188.  https://doi.org/10.1057/s41274-016-0019-0 CrossRefGoogle Scholar
  20. Tran HT, Balchanos M, Domerçant JC et al (2017) A framework for the quantitative assessment of performance-based system Resilience. Reliab Eng Syst Saf 158:73–84CrossRefGoogle Scholar
  21. Warszawska K, Kraslawski A (2016) Method for quantitative assessment of safety culture. J Loss Prev Process Ind 42:27–34CrossRefGoogle Scholar
  22. Zhang W, Wang N (2016) Resilience-based risk mitigation for road networks. Struct Saf 62:57–65CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Beijing Technology and Business UniversityBeijingChina

Personalised recommendations