Skip to main content
Log in

Risk-based models for emergency shelter and exit design in buildings

  • S.I.: Risk Management Approaches in Engineering Applications
  • Published:
Annals of Operations Research Aims and scope Submit manuscript

Abstract

Mathematical models are presented that support the design of shelters and exits in buildings, along with hallway fortification strategies and associated evacuation paths. The objective of these models is to optimally protect building users and prevent casualties during emergencies by minimizing the risk to which evacuees are exposed during evacuation and after reaching their destinations. The models involve stochastic programming and robust optimization concepts under both user equilibrium (selfish) and system optimal (altruistic) conditions. These approaches are compared in a case study involving a single-story building. A multi-hazard approach is utilized in which the performance of a design is tested given various possible future emergency scenarios.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Alcada-Almeida, L., Tralhao, L., Santos, L., & Countinho-Rodrigues, J. (2009). A multiobjective approach to locate emergency shelters and identify evacuation routes in urban areas. Geographical Analysis, 41(1), 9–27.

    Article  Google Scholar 

  • Antonini, G., Bierlaire, M., & Weber, M. (2006). Discrete choice models of pedestrian walking behavior. Transportation Research Part B-Methodological, 40, 667–687.

    Article  Google Scholar 

  • Bayram, V., Tansel, B., & Yaman, H. (2015). Compromising system and user interests in shelter location and evacuation planning. Transportation Research Part B, 72, 146–163.

    Article  Google Scholar 

  • Beckmann, M. J., McGuire, B. C., & Winsten, C. B. (1956). Studies in the economics of transportation. New Haven, CT: Yale University Press.

    Google Scholar 

  • Bierlaire, M., & Robin, T. (2009). Pedestrians choices. In H. Timmermans (Ed.), Pedestrian behavior: Models (pp. 1–26). Data Collection and Applications: Emerald Group Publishing Limited.

    Google Scholar 

  • Bish, D., Agca, E., & Glick, R. (2014). Decision support for hospital evacuation and emergency response. Annals of Operations Research, 221, 89–106.

    Article  Google Scholar 

  • Chattaraj, U., Seyfried, A., & Chakroborty, P. (2009). Comparison of pedestrian fundamental diagram across cultures. Advances in Complex Systems, 12, 393–405.

    Article  Google Scholar 

  • Dimitriou, L., & Stathopoulous, A. (2008). Reliable stochastic design of road network systems. International Journal Industrial and Systems Engineering, 3(5), 549–574.

    Article  Google Scholar 

  • Fan, Y., & Liu, C. (2010). Solving stochastic transportation network protection problems using the progressive hedging-based method. Networks and Spatial Economics, 10, 623–640.

    Article  Google Scholar 

  • Farvaresh, H., & Sepehri, M. M. (2011). A single-level mixed integer linear formulation for a bi-level discrete network design problem. Transportation Research E, 47(5), 623–640.

    Article  Google Scholar 

  • Feng, L., & Miller-Hooks, E. (2014). A network optimization-based approach for crowd management in large public gatherings. Transportation Research Part C: Emerging Technologies, 42, 182–199.

    Article  Google Scholar 

  • Hamacher, H. W., Heller, S., & Rupp, B. (2013). Flow location (flowloc) problems: dynamic network flows and location models for evacuation planning. Annals of Operations Research, 207(1), 161–180.

    Article  Google Scholar 

  • In Weidmann, U., Kirsch, U., Schreckenberg, M., (eds) Pedestrian and evacuation dynamics 2012. Springer, New York, p. 713–724.

  • Fortuny-Amat, J., & McCarl, B. (1981). A representation and economic interpretation of a two-level programming problem. Journal of the Operational Research Society, 32(9), 783–792.

    Article  Google Scholar 

  • Kongsomsaksakul, S., Yang, C., & Chen, A. (2005). Shelter location-allocation for flood evacuation planning. Journal of the Eastern Asia Society for Transportation Studies, 6, 4237–4252.

    Google Scholar 

  • Koutsoupias, E., & Papadimitriuou, C. (1999). Worst-case Equilibria. In C. Meinel & S. Tison (Eds.), Proceedings of the 16th Annual Symposium on theoretical aspects of computer science (STACs) (Vol. 1563, pp. 404–413)., Lecture notes in computer science, Trier Heidelberg: Germany. Springer.

  • Kulshrestha, A., Wu, D., Lou, Y., & Yin, Y. (2011). Robust shelter locations for evacuation planning with demand uncertainty. Journal of Transportation Safety and Security, 3(4), 272–288.

    Article  Google Scholar 

  • Laporte, G., & Louveaux, F. V. (1993). The integer L-shaped method for stochastic integer problems with complete recourse. Operations Research Letters, 13(3), 133–142.

    Article  Google Scholar 

  • Larsson, T., & Patriksson, M. (1995). An augmented lagrangean scheme for capacitated traffic assignment problems. Transportation Research B, 29(6), 433–455.

    Article  Google Scholar 

  • Li, A., Xu, N., Nozick, L., & Davidson, R. (2011). Bilevel optimization for integrated shelter location analysis and transportation planning for hurricane events. Journal of Infrastructure Systems, 17, 184–192.

    Article  Google Scholar 

  • Li, A., Nozick, L., Xu, N., & Davidson, R. (2012). Shelter location and transportation planning under hurricane conditions. Transportation Research Part E, 48, 715–729.

    Article  Google Scholar 

  • Lovas, G. G. (1995). On performance measures for evacuation systems. European Journal of Operational Research, 85, 352–367.

    Article  Google Scholar 

  • National Fire Protection Association. (2009). Life safety code: NFPA 101 (2nd ed.). MA: Quincy.

  • Ng, M., Park, J., & Waller, S. T. (2010). A hybrid bilevel model for the optimal shelter assignment in emergency evacuations. Computer-Aided Civil and Infrastructure Engineering, 25(8), 547–556.

    Article  Google Scholar 

  • Patriksson, M. (2008). On the applicability and solution of bilevel optimization models in transportation science: A study on the existence, stability and computation of optimal solutions to stochastic mathematical programs with equilibrium constraints. Transportation Research Part B: Methodological, 42, 843–860.

    Article  Google Scholar 

  • Proulx, G. (2002). Movement of people: The evacuation timing. In DiNenno, P., Drysdale, D., Beyler, C., Walton, W., editors, The SFPE handbook of fire protection engineering (Chapter 3). Society of Fire Protection Engineers, Bethesda, MD, third edition, p. 3–342 to 3–366.

  • Roughgarden, T. (2005). Selfish routing and the price of anarchy. Cambridge: MIT Press.

    Google Scholar 

  • Schomborg, A., Nökel, K., Seyfried, A. (2011). Evacuation assistance for a sports arena using a macroscopic network model. In: Peacock, R.D., Averill, J.D. (eds)Pedestrian and Evacuation Dynamics. Springer Science+Business Media, p. 389–398.

  • Seyfried, A., Steffen, B., Klingsch, W., & Boltes, M. (2005). The fundamental diagram of pedestrian movement revisited. Journal of Statistical Mechanics-Theory and Experiment, 10, P10002.

    Article  Google Scholar 

  • Sherali, H. (2001). On mixed-integer zero-one representations for separable lower-semicontinuous piecewise-linear functions. Operations Research Letters, 28(4), 155–160.

    Article  Google Scholar 

  • Sherali, H. D., Carter, T. B., & Hobeika, A. G. (1991). A location-allocation model and algorithm for evacuation planning under hurricane/flood conditions. Transportation Research B, 25(6), 439–452.

    Article  Google Scholar 

  • Stackelberg, V. H. (1934). Marktform und Gleichgewicht. Vienna: Springer.

    Google Scholar 

  • Wang, D. Z. W., & Lo, H. (2010). Global optimum of the linearized network design problem with equilibrium flows. Transportation Research B, 44(4), 482–492.

    Article  Google Scholar 

  • Youn, H., Hawoong, J., & Gastner, M. (2008). The price of anarchy in transportation networks: Efficiency and optimality control. Physical review letters, 101(12),

Download references

Acknowledgments

This work was funded by the National Science Foundation and the United States Department of Transportation through the Mid-Atlantic University Transportation Center. This support is gratefully acknowledged, but implies no endorsement of the findings.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shabtai Isaac.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Faturechi, R., Isaac, S., Miller-Hooks, E. et al. Risk-based models for emergency shelter and exit design in buildings. Ann Oper Res 262, 185–212 (2018). https://doi.org/10.1007/s10479-016-2223-3

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10479-016-2223-3

Keywords

Navigation