An Integrated Disaster Preparedness Model for Retrofitting and Relief Item Transportation

  • Alper Döyen
  • Necati ArasEmail author


In this study, a two-stage stochastic integer programming model is developed with a centralized planning perspective to simultaneously address mitigation and response decisions in humanitarian logistics, where the mitigation decisions involve both building and transportation infrastructure retrofitting. The objective is to minimize the total cost of retrofitting, relief item transportation and relief item shortage under a limited mitigation budget. Due to the excessive number of binary decision variables, solving the model becomes computationally difficult. Therefore, we propose Lagrangean relaxation to decouple the overall model and solve it by Lagrangean heuristics. Computational results indicate the efficiency of the solution approaches in providing high quality feasible solutions to problem instances of realistic size and complexity.


Humanitarian logistics Disaster mitigation Disaster response Two-stage stochastic programming Lagrangean relaxation 



  1. Anaya-Arenas AM, Renaud J, Ruiz A (2014) Relief distribution networks: a systematic review. Ann Oper Res 223(1):53–79CrossRefGoogle Scholar
  2. Bana e Costa C, Oliveira C, Vieira V (2008) Prioritization of bridges and tunnels in earthquake risk mitigation using multicriteria decision analysis: application to Lisbon. Omega 36(3):442–450CrossRefGoogle Scholar
  3. Chen A, Yang H, Lo H, Tang W (2002) Capacity reliability of a road network: an assessment methodology and numerical results. Transp Res B Methodol 36(3):225–252CrossRefGoogle Scholar
  4. Chen A, Yang C, Kongsomsaksakul S, Lee M (2007) Network-based accessibility measures for vulnerability analysis of degradable transportation networks. Networks and Spatial Economics 7(3):241–256CrossRefGoogle Scholar
  5. Dantzig G (1957) Discrete-variable extremum problems. Oper Res 5(2):266–288CrossRefGoogle Scholar
  6. Dodo A, Davidson R, Xu N, Nozick L (2007) Application of regional earthquake mitigation optimization. Comput Oper Res 34(8):2478–2494CrossRefGoogle Scholar
  7. Döyen A, Aras N, Barbarosoğlu G (2012) A two-echelon stochastic facility location model for humanitarian relief logistics. Optim Lett 6(6):1123–1145CrossRefGoogle Scholar
  8. Du L, Peeta S (2014) A stochastic optimization model to reduce expected post-disaster response time through pre-disaster investment decisions. Networks and Spatial Economics 14(2):271–295CrossRefGoogle Scholar
  9. Fan Y, Liu C (2008) Solving stochastic transportation network protection problems using the progressive hedging-based method. Networks and Spatial Economics 10(2):193–208CrossRefGoogle Scholar
  10. Faturechi R, Miller-Hooks E (2014) Measuring the performance of transportation infrastructure systems in disasters: a comprehensive review. Journal of Infrastructure Systems 21(1):04014025CrossRefGoogle Scholar
  11. Grass E, Fischer k. (2016) Two-stage stochastic programming in disaster management: a literature survey. Surveys in Operations Research and Management Science 21(2):85–100Google Scholar
  12. Günneç D, Salman F (2007) A two stage multi-criteria stochastic programming model for location of emergency response and distribution centers. In: Euro winter institute on location and logistics, Estoril, Portugal, vol 35, pp 209-227Google Scholar
  13. Günneç D, Salman F (2011) Assessing the reliability and the expected performance of a network under disaster risk. OR Spectrum 33(3):499–523CrossRefGoogle Scholar
  14. Habib MS, Lee YH, Memon MS (2016) Mathematical models in humanitarian supply chain management: a systematic literature review. Math Probl Eng 2016 (Article ID 3212095):20Google Scholar
  15. Haneveld W, Van Der Vlerk M (2001) Optimizing electricity distribution using two-stage integer recourse models. Appl Opt 54:137–154CrossRefGoogle Scholar
  16. Held M, Wolfe P, Crowder H (1974) Validation of subgradient optimization. Math Program 6(1):62–88CrossRefGoogle Scholar
  17. Hoyos MC, Morales RS, Akhavan-Tabatabaei R (2015) OR Models with stochastic components in disaster operations management: a literature survey. Comput Ind Eng 82:183–197CrossRefGoogle Scholar
  18. JICA-IMM (2002) The study on a disaster prevention/mitigation basic plan in Istanbul including microzonation in the Republic of Turkey. Tech. rep., Japanese International Cooperation Agency, Local Municipality of Istanbul, Final report, vol V, Sept 2002Google Scholar
  19. Karaman H, Şahin M, Elnashai A, Pineda O (2008) Loss assessment study for the Zeytinburnu district of Istanbul using Maeviz-Istanbul (HAZTURK). J Earthq Eng 12(S2):187–198CrossRefGoogle Scholar
  20. Liu C, Fan Y, Ordóñez F (2009) A two-stage stochastic programming model for transportation network protection. Comput Oper Res 36(5):1582–1590CrossRefGoogle Scholar
  21. Mete H, Zabinsky Z (2010) Stochastic optimization of medical supply location and distribution in disaster management. Int J Prod Econ 126(1):76–84CrossRefGoogle Scholar
  22. Miller-Hooks E, Zhang X, Faturechi R (2012) Measuring and maximizing resilience of freight transportation networks. Comput Oper Res 39(7):1633–1643CrossRefGoogle Scholar
  23. Peeta S, Salman F, Gunnec D, Viswanath K (2010) Pre-disaster investment decisions for strengthening a highway network. Comput Oper Res 37(10):1708–1719CrossRefGoogle Scholar
  24. Phaup M, Kirschner C (2010) Budgeting for disasters: focusing on the good times. OECD J Budg 2010:1Google Scholar
  25. Rawls C, Turnquist M (2010) Pre-positioning of emergency supplies for disaster response. Transp Res B 44(4):521–534CrossRefGoogle Scholar
  26. Salmerón J, Apte A (2010) Stochastic optimization for natural disaster asset prepositioning. Prod Oper Manag 19(5):561–574CrossRefGoogle Scholar
  27. Sanchez-Silva M, Daniels M, Lleras G, Patiño D (2005) A transport network reliability model for the efficient assignment of resources. Transp Res B Methodol 39 (1):47–63CrossRefGoogle Scholar
  28. Sohn J (2006) Evaluating the significance of highway network links under the flood damage: an accessibility approach. Transp Res A Policy Pract 40(6):491–506CrossRefGoogle Scholar
  29. Taylor MAP, Sekhar SVC, DEste GM (2006) Application of accessibility based methods for vulnerability analysis of strategic road networks. Networks and Spatial Economics 6(3–4):267–291CrossRefGoogle Scholar
  30. Tufekci S, Wallace W (1998) Emerging area of emergency management and engineering. IEEE Trans Eng Manag 45(2):103–105CrossRefGoogle Scholar
  31. Vaziri P, Davidson R, Nozick L, Hosseini M (2010) Resource allocation for regional earthquake risk mitigation: a case study of Tehran, Iran. Nat Hazards 53(3):527–546CrossRefGoogle Scholar
  32. Viswanath K, Peeta S (2003) Multicommodity maximal covering network design problem for planning critical routes for earthquake response. Transp Res Rec J Transp Res Board 1857:1–10CrossRefGoogle Scholar
  33. Xu N, Davidson R, Nozick L, Dodo A (2007) The risk-return tradeoff in optimizing regional earthquake mitigation investment. Struct Infrastruct Eng Maint Manag Life-Cycle 3(2):133–146CrossRefGoogle Scholar
  34. Yushimito W, Jaller M, Ukkusuri S (2012) A Voronoi-Based heuristic algorithm for locating distribution centers in disasters. Networks and Spatial Economics 12(1):21–39CrossRefGoogle Scholar
  35. Yücel E., Salman FS, Arsik I (2018) Improving post-disaster road network accessibility by strengthening links against failures. Eur J Oper Res 269(2):406–422CrossRefGoogle Scholar
  36. Zolfaghari MR, Peyghaleh E (2015) Implementation of equity in resource allocation for regional earthquake risk mitigation using two-stage stochastic programming. Risk Anal 35(3):434–458CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Industrial EngineeringKonya Technical UniversityKonyaTurkey
  2. 2.Department of Industrial EngineeringBoğaziçi UniversityİstanbulTurkey

Personalised recommendations