Towards Evacuation Planning of Groups with Genetic Algorithms

Bjørnar Hansen
Department of ICT, University of Agder, Grimstad, Norway

Leonard Loland
Department of ICT, University of Agder, Grimstad, Norway

Morten Goodwin
Department of ICT, University of Agder, Grimstad, Norway

Ole-Christoffer Granmo
Department of ICT, University of Agder, Grimstad, Norway

Ladda ner artikel

Ingår i: The 29th Annual Workshop of the Swedish Artificial Intelligence Society (SAIS), 2–3 June 2016, Malmö, Sweden

Linköping Electronic Conference Proceedings 129:3, s. 8

Visa mer +

Publicerad: 2016-06-20

ISBN: 978-91-7685-720-5

ISSN: 1650-3686 (tryckt), 1650-3740 (online)


In crisis situations on board ships, it is of utmost importance to have the passengers safely evacuate to the lifeboats in an efficient manner. Existing methods such as marked escape routes and maps are not optimal as pre-planned escape routes may become heavily congested by passengers. Further, the closest lifeboat is not always feasible as lifeboat capacity can be exceeded. Also considering that some evacuees are strongly affiliated, such as families, and that they prefer to evacuate together as a group, it becomes a difficult problem to solve. This paper models the area to be evacuated as a time-expanded graph with hazard severities as probabilities of survivability for each node. The presented approach applies a multi-objective genetic algorithm with multiple fitness functions to maximize the over all survivability. Finally, the proposed method picks the best evacuation plan from a pool of potential solutions returned by the genetic algorithm. The solution generates better routing plans than comparable methods, specially in situations where grouping and congestions are considered. In essence this is an essential step towards automatic planning of evacuations which in turn contributes to smoother evacuations of crises situations and saving lives.


artificial intelligence


[1] Maged N Kamel Boulos, Bernd Resch, David N Crowley, John G Breslin, Gunho Sohn, Russ Burtner, William A Pike, Eduardo Jezierski, and Kuo-Yu Slayer Chuang. Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, ogc standards and application examples. International journal of health geographics, 10(1):67, 2011.

[2] Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and TAMT Meyarivan. A fast and elitist multiobjective genetic algorithm: Nsgaii. Evolutionary Computation, IEEE Transactions on, 6(2):182-197, 2002.

[3] Thomas E Drabek and David A McEntire. Emergent phenomena and the sociology of disaster: lessons, trends and opportunities from the research literature. Disaster Prevention and Management, 12(2):97-112, 2003.

[4] Lester R Ford and Delbert R Fulkerson. Maximal flow through a network. Canadian Journal of Mathematics, 8(3):399-404, 1956.

[5] Morten Goodwin, Ole-Christo er Granmo, and Jaziar Radianti. Escape planning in realistic re scenarios with ant colony optimisation. Applied Intelligence, 42(1):24-35, 2015.

[6] H.W. Hamacher and S.A. Tjandra. Mathematical modelling of evacuation problems-a state of the art. Pedestrian and Evacuation Dynamics, 2002:227-266, 2002.

[7] Sirisak Kongsomsaksakul, Chao Yang, and Anthony Chen. Shelter location-allocation model for fl ood evacuation planning. Journal of the Eastern Asia Society for Transportation Studies, 6(1):4237-4252, 2005.

[8] Nicholas D Lane, Emiliano Miluzzo, Hong Lu, Daniel Peebles, Tanzeem Choudhury, and Andrew T Campbell. A survey of mobile phone sensing. Communications Magazine, IEEE, 48(9):140-150, 2010.

[9] Gino J Lim, Shabnam Zangeneh, M Reza Baharnemati, and Tiravat Assavapokee. A capacitated network flow optimization approach for short notice evacuation planning. European Journal of Operational Research, 223(1):234- 245, 2012.

[10] Anthony R Mawson. Understanding mass panic and other collective responses to threat and disaster. Psychiatry: Interpersonal and biological processes, 68(2):95-113, 2005.

[11] Adam J Pel, Michiel CJ Bliemer, and Serge P Hoogendoorn. A review on travel behaviour modelling in dynamic trac simulation models
for evacuations. Transportation, 39(1):97-123, 2012.

[12] Jaziar Radianti, Ole-Christo er Granmo, Noureddine Bouhmala, Parvaneh Sarshar, Anis Yazidi, and Jose Gonzalez. Crowd models for emergency evacuation: A review targeting human-centered sensing. In In Proceeding of 46 th Hawaii International Conference for System Sciences, 2013.

[13] Mohammad Saadatseresht, Ali Mansourian, and Mohammad Taleai. Evacuation planning using multiobjective evolutionary optimization approach. European Journal of Operational Research, 198(1):305-314, 2009.

[14] Parvaneh Sarshar, Jaziar Radianti, and Jose J Gonzalez. On the impacts of utilizing smart-phones on organizing rescue teams and evacuation procedures. In Proceedings of the 11th International Conference on Information Systems for Crisis Response and Management, ISCRAM, pages 24-27, 2015.

[15] James MacGregor Smith. Evacuation networks. In Encyclopedia of optimization Ed. by Christodoulos A. Floudas and Panos M. Pardalos., pages 940-950. Springer, 2009.

[16] Eckart Zitzler, Kalyanmoy Deb, and Lothar Thiele. Comparison of multiobjective evolutionary algorithms: Empirical results. Evolutionary computation, 8(2):173-195, 2000.

Citeringar i Crossref