Natural Hazards

, Volume 89, Issue 3, pp 1167–1184 | Cite as

Simulation-based optimization of emergency evacuation strategy in ultra-high-rise buildings

  • Ning DingEmail author
  • Hui Zhang
  • Tao Chen
Original Paper


Emergency evacuation in high-rise buildings is a crucial problem. The evacuation strategy of using stairs and evacuation elevators should be optimized. In this paper, simulation-based optimization method is used to optimize the evacuation strategy of using stairs and elevators in high-rise buildings. The stair simulation is based on a cellular automata model, and several typical pedestrians’ walk preferences are considered in this model. In the simulation, evacuation elevators can arrive at the refuge floors, and the scheduling of the elevators is optimized based on the GA algorithm. The simulation-based optimization is designed as a two-level problem: The upper level is a strategy level; the lower level is an operation level. In the study case, the evacuation strategy of a 100-floor ultra-high-rise office building is optimized. We find that if evacuees follow the traditional stair evacuation strategy, the evacuation time is 42.6 min. After optimization, the evacuation time of optimal strategy by using both stairs and elevators is 25.1 min. Compared with the traditional stair evacuation strategy, the efficiency of evacuation is improved by 41.1%. It is also found that the merging behavior in stairwells will decrease the velocity of the pedestrian flow. Stairs are still the main egress, and evacuation elevators are an assistant egress during high-rise building evacuation.


SBO Building evacuation Optimization High-rise building 



This work was partially supported by the Basic Research Program of People’s Public Security University of China (2016JKF01307), the National Natural Science Foundation of China (71373139, 91646201) and the 12th Five-Year Technology Support Program (2015BAK10B00). The authors appreciate the support for this paper by the Collaborative Innovation Center of Public Safety.


  1. Abdelsalam HME, Bao HP (2006) A simulation-based optimization framework for product development cycle time reduction. IEEE Trans Eng Manag 53(1):69–85CrossRefGoogle Scholar
  2. Al-Aomar R (2006) Designing machine operating strategy with simulated annealing and Monte Carlo simulation. J Frankl Inst 343(4):372–388CrossRefGoogle Scholar
  3. Al-Dhaheri N, Jebali A, Diabat A (2016) A simulation-based Genetic Algorithm approach for the quay crane scheduling under uncertainty. Simul Model Pract Theory 66:122–138CrossRefGoogle Scholar
  4. Alrabghi A, Tiwari A (2013) A review of simulation-based optimisation in maintenance operations. In: 2013 UKSim 15th international conference on computer modelling and simulation 10–12 April 2013, Cambridge, IEEE, Piscataway, pp 353–358Google Scholar
  5. Alrabghi A, Tiwari A (2015) State of the art in simulation-based optimisation for maintenance systems. Comput Ind Eng 82:167–182CrossRefGoogle Scholar
  6. Amaran S, Sahinidis NV, Sharda B et al (2014) Simulation optimization: a review of algorithms and applications. 4OR 12(4):301–333CrossRefGoogle Scholar
  7. Asano M, Iryo T, Kuwahara M (2010) Microscopic pedestrian simulation model combined with a tactical model for route choice behaviour. Transp Res Part C Emerg Technol 18(6):842–855CrossRefGoogle Scholar
  8. Delgarm N, Sajadi B, Kowsary F et al (2016) Multi-objective optimization of the building energy performance: a simulation-based approach by means of particle swarm optimization (PSO). Appl Energy 170:293–303CrossRefGoogle Scholar
  9. Ding Y, Yang L, Weng F et al (2015) Investigation of combined stairs elevators evacuation strategies for high rise buildings based on simulation. Simul Model Pract Theory 53:60–73CrossRefGoogle Scholar
  10. Duh JD, Brown DG (2007) Knowledge-informed Pareto simulated annealing for multi-objective spatial allocation. Comput Environ Urban Syst 31(3):253–281CrossRefGoogle Scholar
  11. Fu L, Luo J, Deng M et al (2012) Simulation of evacuation processes in a large classroom using an improved cellular automaton model for pedestrian dynamics. Procedia Eng 31:1066–1071CrossRefGoogle Scholar
  12. Galea ER, Hulse L, Day R et al (2010) The UK WTC 9/11 evacuation study: an overview of the methodologies employed and some preliminary analysis. In: Pedestrian and evacuation dynamics 2008. Springer, Berlin, pp 3–24Google Scholar
  13. Georgoudas IG, Kyriakos P, Sirakoulis GC et al (2010) An FPGA implemented cellular automaton crowd evacuation model inspired by the electrostatic-induced potential fields. Microprocess Microsyst 34(7):285–300CrossRefGoogle Scholar
  14. Hao QY, Jiang R, Hu MB et al (2011) Pedestrian flow dynamics in a lattice gas model coupled with an evolutionary game. Phys Rev E 84(3):036107CrossRefGoogle Scholar
  15. He Q, Wang L, Liu B (2007) Parameter estimation for chaotic systems by particle swarm optimization. Chaos Solitons Fractals 34(2):654–661CrossRefGoogle Scholar
  16. Helbing D, Farkas I (2002) Simulation of pedestrian crowds in normal and evacuation situations. In: Schreckenberg M, Sharma SD (eds) Pedestrian and evacuation dynamics. Springer, Berlin, pp 21–58Google Scholar
  17. Helbing D, Molnar P (1995) Social force model for pedestrian dynamics. Phys Rev E 51(5):4282CrossRefGoogle Scholar
  18. Helbing D, Farkas IJ, Vicsek T (2000) Freezing by heating in a driven mesoscopic system. Phys Rev Lett 84(6):1240CrossRefGoogle Scholar
  19. Helbing D, Farkas IJ, Molnar P, Vicsek T (2002) Simulation of pedestrian crowds in normal and evacuation situations. Pedestr Evacuation Dyn 21(2):21–58Google Scholar
  20. Henderson LF (1971) The statistics of crowd fluids. Nature 229:381–383CrossRefGoogle Scholar
  21. Heyes E, Spearpoint M (2012) Lifts for evacuation-human behaviour considerations. Fire Mater 36(4):297–308CrossRefGoogle Scholar
  22. Hoogendoorn SP, Bovy PHL (2005) Pedestrian travel behavior modeling. Netw Spat Econ 5(2):193–216CrossRefGoogle Scholar
  23. Huo F, Song W, Lv W et al (2014) Analyzing pedestrian merging flow on a floor–stair interface using an extended lattice gas model. Simulation 90(5):501–510CrossRefGoogle Scholar
  24. Jacobs PHM, Verbraeck A, Mulder JBP (2005) Flight scheduling at KLM. In: Proceedings of the winter simulation conference, IEEE, p 8Google Scholar
  25. Lovas GG (1994) Modeling and simulation of pedestrian traffic flow. Transp Res Part B Methodol 28(6):429–443CrossRefGoogle Scholar
  26. Lu WZ, Lo SM, Fang Z et al (2001) A preliminary investigation of airflow field in designated refuge floor. Build Environ 36(2):219–230CrossRefGoogle Scholar
  27. Ma J, Lo SM, Song WG (2012) Cellular automaton modeling approach for optimum ultra high-rise building evacuation design. Fire Saf J 54:57–66CrossRefGoogle Scholar
  28. Meijing G (2009) Simulation based optimization approaches for multi-echelon inventory control of supply China distribution networks. Doctoral dissertation, Northeastern University, ChinaGoogle Scholar
  29. Ning Ding, Hui Zhang, Tao Chen et al (2015) Stair evacuation simulation based on cellular automata considering evacuees’ walk preferences. Chin Phys B 24(6):687–693Google Scholar
  30. Paul RJ, Chanev TS (1998) Simulation optimisation using a genetic algorithm. Simul Pract Theory 6(6):601–611CrossRefGoogle Scholar
  31. Pelechano N, Malkawi A (2008) Evacuation simulation models: challenges in modeling high rise building evacuation with cellular automata approaches. Autom Constr 17(4):377–385CrossRefGoogle Scholar
  32. Peng YC, Chou CI (2011) Simulation of pedestrian flow through a “T” intersection: a multi-floor field cellular automata approach. Comput Phys Commun 182(1):205–208CrossRefGoogle Scholar
  33. Proulx G, Johnson P, Heyes E et al (2009) The use of elevators for egress: discussion panel. In: Proceedings of human behaviour in fire conference, pp 97–110Google Scholar
  34. Reneke PA, Peacock RD, Hoskins BL (2013) Combined stairwell and elevator use during building evacuation. US Department of Commerce, National Institute of Standards and Technology, GaithersburgGoogle Scholar
  35. Ronchi E, Reneke PA, Peacock RD (2014) A method for the analysis of behavioural uncertainty in evacuation modelling. Fire Technol 50:1545–1571CrossRefGoogle Scholar
  36. Santé I, García AM, Miranda D et al (2010) Cellular automata models for the simulation of real-world urban processes: a review and analysis. Landsc Urban Plan 96(2):108–122CrossRefGoogle Scholar
  37. Sun J, Zhao QC, Luh PB (2010) Optimization of group elevator scheduling with advance information. IEEE Trans Autom Sci Eng 7(2):352–363CrossRefGoogle Scholar
  38. Tampère C, Van Arem B, Hoogendoorn S (2003) Gas-kinetic traffic flow modeling including continuous driver behavior models. Transp Res Rec J Transp Res Board 1852:231–238CrossRefGoogle Scholar
  39. Tekin E, Sabuncuoglu I (2004) Simulation optimization: a comprehensive review on theory and applications. IIE Trans 36(11):1067–1081CrossRefGoogle Scholar
  40. Truong TH, Azadivar F (2003) Simulation optimization in manufacturing analysis: simulation based optimization for supply chain configuration design. In: Proceedings of the 35th conference on winter simulation: driving innovation. winter simulation conference, pp 1268–1275Google Scholar
  41. Tsai SC, Fu SY (2014) Genetic-algorithm-based simulation optimization considering a single stochastic constraint. Eur J Oper Res 236(1):113–125CrossRefGoogle Scholar
  42. Wang JY, Weng WG, Zhang XL (2014) New insights into the crowd characteristics in Mina. J Stat Mech Theory Exp 2014:P11003CrossRefGoogle Scholar
  43. Was J (2005) Cellular automata model of pedestrian dynamics for normal and evacuation conditions. In: 5th international conference on intelligent systems design and applications (ISDA’05), IEEE, pp 154–159Google Scholar
  44. Wąs J, Lubaś R (2014) Towards realistic and effective agent-based models of crowd dynamics. Neurocomputing 146:199–209CrossRefGoogle Scholar
  45. Yue H, Guan H, Zhang J et al (2010) Study on bi-direction pedestrian flow using cellular automata simulation. Physica A 389(3):527–539CrossRefGoogle Scholar
  46. Zhang XL, Weng WG, Yuan HY (2012) Empirical study of crowd behavior during a real mass event. J Stat Mech Theory Exp 2012(08):P08012CrossRefGoogle Scholar
  47. Zhang XL, Weng WG, Yuan HY, Chen JG (2013) Empirical study of a unidirectional dense crowd during a real mass event. Physica A 392:2781–2791CrossRefGoogle Scholar
  48. Zou N, Yeh ST, Chang GL et al (2005) Simulation-based emergency evacuation system for Ocean City, Maryland, during hurricanes. Transp Res Rec J Transp Res Board 1922:138–148CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  1. 1.School of Criminal Investigation and CounterterrorPeople’s Public Security University of ChinaBeijingChina
  2. 2.Institute of Public Safety ResearchTsinghua UniversityBeijingChina

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