Volume 12 Issue 3
Dec.  2021
Turn off MathJax
Article Contents
Yanyan Wang. Multiperiod Optimal Allocation of Emergency Resources in Support of Cross-Regional Disaster Sustainable Rescue[J]. International Journal of Disaster Risk Science, 2021, 12(3): 394-409. doi: 10.1007/s13753-021-00347-5
Citation: Yanyan Wang. Multiperiod Optimal Allocation of Emergency Resources in Support of Cross-Regional Disaster Sustainable Rescue[J]. International Journal of Disaster Risk Science, 2021, 12(3): 394-409. doi: 10.1007/s13753-021-00347-5

Multiperiod Optimal Allocation of Emergency Resources in Support of Cross-Regional Disaster Sustainable Rescue

doi: 10.1007/s13753-021-00347-5
Funds:

This work is supported by the Postdoctoral Science Foundation of China Grant Nos. 2020M670363 and 2020T130340, the National Natural Science Foundation of China Grant Nos. 71790611 and 71774042, and the Humanities and Social Sciences of the Ministry of Education of China Grant No. 20YJC630243.

  • Available Online: 2021-12-25
  • Publish Date: 2021-12-25
  • Cross-regional allocation is necessary for the rational utilization and optimal allocation of resources. It is also the key to effective and sustainable disaster relief. Existing research, however, generally centers on emergency resource allocation only within territories or regions. This article proposes a multiperiod allocation optimization model for emergency resources based on regional selfrescue and cross-regional collaborative rescue efforts. The model targets the shortest delivery time and lowest allocation costs as its efficiency goals and the maximum coverage rate of resource allocation in the disaster-affected locations as its equity goal. An objective weighting fuzzy algorithm based on two-dimensional Euclidean distance is designed to solve the proposed model. A case study based on the Wenchuan Earthquake of 12 May 2008 was conducted to validate the proposed model. The results indicate that our proposed model allows for optimal, multiperiod cross-regional resource allocation by combining interterritorial and nearby allocation principles. Cross-regional relief makes resource allocation more equitable, minimizes dissatisfaction, and prevents losses. Different decision preferences appear to significantly affect the choice of resource allocation scheme employed, which provides flexibility for decision making in different emergencies.
  • loading
  • Amailef, K., and J. Lu. 2013. Ontology-supported case-based reasoning approach for intelligent m-Government emergency response services. Decision Support Systems 55(1): 79–97.
    Ansell, C., A. Boin, and A. Keller. 2010. Managing transboundary crises: Identifying the building blocks of an effective response system. Journal of Contingencies and Crisis Management 18(4): 195–207.
    Arora, H., T.S. Raghu, and A. Vinze. 2010. Resource allocation for demand surge mitigation during disaster response. Decision Support Systems 50(1): 304–315.
    Arrubla, J.A.G., L. Ntaimo, and C. Stripling. 2014. Wildfire initial response planning using probabilistically constrained stochastic integer programming. International Journal of Wildland Fire 23(6): 825–838.
    Balcik, B., and B.M. Beamon. 2008. Facility location in humanitarian relief. International Journal of Logistics Research and Applications 11(2): 101–121.
    Barbarosoğlu, G., L. Özdamar, and A. Çevik. 2002. An interactive approach for hierarchical analysis of helicopter logistics in disaster relief operations. European Journal of Operational Research 140(1): 118–133.
    Berkoune, D., J. Renaud, M. Rekik, and A. Ruiz. 2012. Transportation in disaster response operations. Socio-Economic Planning Sciences 46(1): 23–32.
    Bertsimas, D., V.F. Farias, and N. Trichakis. 2012. On the efficiency-fairness trade-off. Management Science 58(12): 2234–2250.
    Boin, A., M. Rhinard, and M. Ekengren. 2014. Managing transboundary crises: The emergence of European union capacity. Journal of Contingencies and Crisis Management 22(3): 131–142.
    Calixto, E., and E.L. Larouvere. 2010. The regional emergency plan requirement: Application of the best practices to the Brazilian case. Safety Science 48(8): 991–999.
    Cao, C.J., C.D. Li, T. Qu, and Q. Yang. 2019. A bi-level programming model for relief trans-regional scheduling: Taking into consideration survivors’ perceived satisfaction and risk acceptability. Journal of Management Sciences in China 22(9): 111–126 (in Chinese).
    Cao, J., and L. Zhu. 2014. Super-network model of urban agglomeration emergency coordination considering decision preferences. Journal of Management Sciences in China 17(11): 33–42 (in Chinese).
    Chang, M.S., Y.L. Tseng, and J.W. Chen. 2007. A scenario planning approach for the flood emergency logistics preparation problem under uncertainty. Transportation Research Part E: Logistics and Transportation Review 43(6): 737–754.
    China Earthquake Administration. 2012. Earthquake Emergency Rescue Plan. http://www.cea.gov.cn/publish/dizhenj/119/100136/20121130104213119243137/index.html. Accessed 16 May 2020 (in Chinese).
    China News Network. 2008. Sichuan Wenchuan earthquake has confirmed 69,227 people killed, 17,923 missing. http://www.chinanews.com/gn/news/2008/09-25/1394600.shtml. Accessed 16 May 2020 (in Chinese).
    Cotes, N., and V. Cantillo. 2019. Including deprivation costs in facility location models for humanitarian relief logistics. Socio-Economic Planning Sciences 65: 89–100.
    Equi, L., G. Gallo, S. Marziale, and A. Weintraub. 1997. A combined transportation and scheduling problem. European Journal of Operational Research 97(1): 94–104.
    Eshghi, K., and R.C. Larson. 2008. Disasters: Lessons from the past 105 years. Disaster Prevention and Management 17(1): 62–82.
    Green, L.V., and P.J. Kolesar. 2004. Improving emergency responsiveness with management science. Management Science 50(8): 1001–1014.
    Green, H.K., O. Lysaght, D.D. Saulnier, K. Blanchard, A. Humphrey, B. Fakhruddin, and V. Murray. 2019. Challenges with disaster mortality data and measuring progress towards the implementation of the Sendai framework. International Journal of Disaster Risk Science 10(4): 449–461.
    Groothedde, B., C. Ruijgrok, and L. Tavasszy. 2015. Towards collaborative, intermodal hub networks: A case study in the fast moving consumer goods market. Transportation Research Part E: Logistics and Transportation Review 41(6): 567–583.
    Guo, Y., Y. Ye, Q. Yang, and K. Yang. 2019. A multi-objective INLP model of sustainable resource allocation for long-range Maritime search and rescue. Sustainability 11(3): Article 929.
    Haghani, A., and S.-C. Oh. 1996. Formulation and solution of a multi-commodity, multi-modal network flow model for disaster relief operations. Transportation Research Part A: Policy and Practice 30(3): 231–250.
    Holguín-Veras, J., N. Pérez, M. Jaller, L.N. Van Wassenhove, and F. Aros-Vera. 2013. On the appropriate objective function for post-disaster humanitarian logistics models. Journal of Operations Management 31(5): 262–280.
    Hoyos, M.C., R.S. Morales, and R. Akhavan-Tabatabaei. 2015. OR models with stochastic components in disaster operations management: A literature survey. Computers & Industrial Engineering 82: 183–197.
    Hu, C.L., X. Liu, and Y.K. Hua. 2016. A bi-objective robust model for emergency resource allocation under uncertainty. International Journal of Production Research 54(24): 7421–7438.
    Hu, X.B., M. Wang, T. Ye, and P. Shi. 2016. A new method for resource allocation optimization in disaster reduction and risk governance. International Journal of Disaster Risk Science 7(2): 138–150.
    Huang, K., and R. Rafiei. 2019. Equitable last mile distribution in emergency response. Computers & Industrial Engineering 127(1): 887–900.
    Kutanoglu, E., and M. Mahajan. 2009. An inventory sharing and allocation method for a multi-location service parts logistics network with time-based service levels. European Journal of Operational Research 194(3): 728–742.
    Li, A.N., X.Q. Deng, and Q.H. Zhao. 2017. Unconventional emergency coordinated organization based on fractal perspective. Systems Engineering—Theory & Practice 37(4): 937–948 (in Chinese).
    Li, J., Q.R. Li, C. Liu, S. Ullah Khan, and N. Ghani. 2014. Community-based collaborative information system for emergency management. Computers & Operations Research 42: 116–124.
    Liu, Y., N. Cui, and J.H. Zhang. 2019. Integrated temporary facility location and casualty allocation planning for post-disaster humanitarian medical service. Transportation Research Part E: Logistics and Transportation Review 128: 1–16.
    Liu, D.H., N. Zhao, and H.W. Zou. 2018. Multi-period reputation effect model of governmental emergency strategy in environmental pollution incidents. Management Review 30(9): 239–245 (in Chinese).
    Luss, H. 1999. On equitable resource allocation problems: A lexicographic minimax approach. Operations Research 47(3): 361–378.
    Lv, T., Y. Nie, C.L. Wang, and J. Gao. 2018. Cross-regional emergency scheduling planning for petroleum based on the supernetwork model. Petroleum Science 15: 666–679.
    Minas, J., J. Hearne, and D. Martell. 2015. An integrated optimization model for fuel management and fire suppression preparedness planning. Annals of Operations Research 232(1): 201–215.
    Najafi, M., K. Eshghi, and W. Dullaert. 2013. A multi-objective robust optimization model for logistics planning in the earthquake response phase. Transportation Research Part E: Logistics and Transportation Review 49(1): 217–249.
    Ogie, R.I., and B. Pradhan. 2019. Natural hazards and social vulnerability of place: The strength-based approach applied to Wollongong, Australia. International Journal of Disaster Risk Science 10(3): 404–420.
    Olsson, E.K. 2015. Transboundary crisis networks: The challenge of coordination in the face of global threats. Risk Management 17(2): 91–108.
    Özdamar, L., and M.A. Ertem. 2015. Models, solutions and enabling technologies in humanitarian logistics. European Journal of Operational Research 244(1): 55–65.
    Özdamar, L., E. Ekinci, and B. Küçükyazici. 2004. Emergency logistics planning in natural disasters. Annals of Operations Research 129(1–4): 217–245.
    Qin, J., Y. Ye, B. Cheng, X. Zhao, and L. Ni. 2017. The emergency vehicle routing problem with uncertain demand under sustainability environments. Sustainability 9(2): Article 288.
    Qiu, Y., X.L. Shi, and G.W. Hua. 2019. Regional cooperative strategies for emergency response to accidents and disasters under longitudinal administrative constraint—Case study in Beijing-Tianjin-Hebei region. Management Review 31(8): 240–249 (in Chinese).
    Rose, A., and T. Kustra. 2013. Economic considerations in designing emergency management institutions and policies for transboundary disasters. Public Management Review 15(3): 446–462.
    Shao, M., Y. Song, C. Teng, and Z. Zhang. 2018. Algorithms and simulation of multi-level and multi-coverage on cross-reginal emergency facilities. Wireless Personal Communications: An International Journal 102(4): 3663–3676.
    Sheu, J.-B., and C. Pan. 2014. A method for designing centralized emergency supply network to respond to large-scale natural disasters. Transportation Research Part B: Methodological 67: 284–305.
    Tang, W.Q., W.M. Tang, and M. Zhang. 2012. Scheduling of emergency commodities: Theory and method. Beijing: Science Press (in Chinese).
    Toro-Díaz, H., M.E. Mayorga, S. Chanta, and L.A. McLay. 2013. Joint location and dispatching decisions for emergency medical services. Computers & Industrial Engineering 64(4): 917–928.
    Tüfeki, S. 1995. An integrated emergency management decision support system for hurricane emergencies. Safety Science 20(1): 39–48.
    Tzeng, G.H., H.J. Cheng, and T.D. Huang. 2007. Mufti-objective optimal planning for designing relief delivery systems. Transportation Research Part E: Logistics and Transportation Review 43(6): 673–686.
    Wang, X., and S. Lv. 2016. Research on across-regional public emergencies cooperation system based on knowledge collaboration. Science and Technology Management Research 8: 216–221 (in Chinese).
    Wang, Y., and B. Sun. 2018. A multiobjective allocation model for emergency resources that balance efficiency and fairness. Mathematical Problems in Engineering. https://doi.org/10.1155/2018/7943498.
    Wang, Y., and B. Sun. 2020. Multi-period optimization model of multi-type emergency materials allocation based on fuzzy information. Chinese Journal of Management Science 28(3): 40–51 (in Chinese).
    Wang, Y., V.M. Bier, and B. Sun. 2019. Measuring and achieving equity in multiperiod emergency material allocation. Risk Analysis 39(11): 2408–2426.
    Wex, F., G. Schryen, S. Feuerriegel, and D. Neumann. 2014. Emergency response in natural disaster management: Allocation and scheduling of rescue units. European Journal of Operational Research 235(3): 697–708.
    Xu, J., and J. Li. 2005. Theory and method of multi-objective decision making. Beijing: Tsinghua University Press (in Chinese).
    Xu, S.H., C.F. Han, L.P. Meng, and Q.D. Wu. 2017. Research on the adoption of an emergency management organization system based on the NK model. Systems Engineering—Theory & Practice 37(6): 1619–1629 (in Chinese).
    Yao, C., and X. Xiao. 2006. Method for the problem of multi-objective decision making based on fuzzy math theory. Journal of Wuhan University Technology Transportation Science & Engineering 30(4): 700–703 (in Chinese).
    Yi, W., and L. Özdamar. 2007. A dynamic logistics coordination model for evacuation and support in disaster response activities. European Journal of Operational Research 179(3): 1177–1193.
    Zhan, S.L., N. Liu, and Y. Ye. 2014. Coordinating efficiency and equity in disaster relief logistics via information updates. International Journal of Systems Science 45(8): 1607–1621.
    Zhang, F., Y. Gao, and Y.L. Li. 2016. Research on cross-regional emergency scheduling and allocating strategies. International Journal of Grid and Distributed Computing 9(5): 89–98.
    Zhao, M., and X. Liu. 2017. Reprint of: Regional risk assessment for urban major hazards based on GIS geoprocessing to improve public safety. Safety Science 97: 112–119.
    Zhou, S., and A. Erdogan. 2019. A spatial optimization model for resource allocation for wildfire suppression and resident evacuation. Computers & Industrial Engineering 138(1): 1–16.
    Zhou, Y., J. Liu, Y. Zhang, and X. Gan. 2017. A multi-objective evolutionary algorithm for multi-period dynamic emergency resource scheduling problems. Transportation Research Part E: Logistics and Transportation Review 99: 77–95.
    Zhu, L., D. Guo, J. Gu, and Y.Q. Du. 2017. System dynamics analysis of cross-regional coordinative emergency materials allocation under severe epidemics—A case study on H1N1 joint response in the Yangtze River Delta. Systems Engineering 35(6): 105–112 (in Chinese).
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (344) PDF downloads(0) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return