Developing a Multi-agent Based Modeling for Smart Search and Rescue Operation

  • Sanaz Azimi
  • Mahmoud Reza DelavarEmail author
  • Abbas Rajabifard
Conference paper
Part of the Lecture Notes in Geoinformation and Cartography book series (LNGC)


One important issue aftermath of disasters is the optimum allocation of the medical assistance to the demanded locations. In this paper, the optimum allocation of the medical assistance to the injured according to a multi-criteria decision making is performed by Multiplicatively Weighted Network Voronoi Diagram (MWNVD). Particle Swarm Optimization (PSO) is applied to optimize the MWNVDs. In this paper, two types of multi-agent rescue models for incorporating the allocation of the medical supplies to the injured locations according to the generated PSO-MWNVDs, wayfinding of emergency vehicles as well as using smart city facilities were proposed. In one of the proposed model, the priority of the injured for receiving the medical assistance, information transfer about the condition of the injured to the hospitals prior to ambulance arrival and updating of ambulance route were considered. Another proposed model has facilities of coordination of emergency vehicles with traffic lights in the intersection and updating of fire engine route compared to the facilities of the first one. The partial difference between the estimated and expected population for receiving the medical assistance in MWNVDs is computed as 37%, while the PSO-MWNVD decreased the mentioned difference to 6%. Also, the time evaluation of the mentioned proposed models and another multi-agent rescue operation model, which uses MWNVD and does not have the studied smart facilities was performed. The results show that the response time of ambulances to the injured and the ambulance mission duration in the proposed model, that has more smart facilities, is improved to other models.


  1. Alami-Kamouri S, Orhanou G, Elhajji, S (2017) Mobile agent service model for smart ambulance. In Cloud Infrastructures, services, and IoT systems for smart cities, Springer, pp 105–111Google Scholar
  2. Alazawi Z, Alani O, Abdljabar MB, Altowaijri S, Mehmood R (2014) A smart disaster management system for future cities. In Proceedings of the 2014 ACM international workshop on wireless and mobile technologies for smart cities, ACM, pp 1–10Google Scholar
  3. Andersson T, Varbrand P (2007) Decision support tools for ambulance dispatch and relocation. J Oper Res Soc 58(2):195–201CrossRefGoogle Scholar
  4. Aurenhammer F, Klein R (2000) Voronoi diagrams. Handb Comput Geom 5:201–290Google Scholar
  5. Azimi S, Delavar MR, Rajabifard A (2017) Multi-agent simulation of allocating and routing ambulances under condition of street blockage after natural disaster. Paper presented at the international archives of the photogrammetry, remote sensing and spatial information sciences, SMPR and EOEC 2017, 7–10 Oct 2017Google Scholar
  6. Azimi S, Delavar MR, Rajabifard A (2018) An optimized multi agent-based modeling of smart rescue operation, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Paper presented at the 2018 GeoInformation for disaster management (Gi4DM), Istanbul, Turkey, 18–21 Mar 2018Google Scholar
  7. Bae JW, Shin K, Lee HR, Lee HJ, Lee T, Kim CH, Cha WC, Kim GW, Moon IC (2018) Evaluation of disaster response system using agent-based model with geospatial and medical details. IEEE Trans Syst Man Cybern Syst 48:1454–1469CrossRefGoogle Scholar
  8. Bai Q (2010) Analysis of particle swarm optimization algorithm. Comput Inf Sci 3(1):180Google Scholar
  9. Bakici T, Almirall E, Wareham J (2013) A smart city initiative: the case of Barcelona. J Knowl Econ 4(2):135–148CrossRefGoogle Scholar
  10. Bandara D, Mayorga ME, Mclay LA (2014) Priority dispatching strategies for EMS systems. J Oper Res Soc 65(4):572–587CrossRefGoogle Scholar
  11. Billhardt H, LujaKk M, Sanchez-Brunete V, Fernandez A, Ossowski S (2014) Dynamic coordination of ambulances for emergency medical assistance services. Knowl Based Syst 70:268–280CrossRefGoogle Scholar
  12. Bostick NA, Subbarao I, Burkle FM, Hsu EB, Armstrong JH, James JJ (2008) Disaster triage systems for large-scale catastrophic events. Disaster Med Public Health Preparedness 2(S1):S35–S39CrossRefGoogle Scholar
  13. Chavoshi SH, Delavar MR, Malek MR, Frank A (2008) Landmark-based simulation for agent Wayfinding after earthquakeGoogle Scholar
  14. Djahel S, Jabeur N, Barrett R, Murphy J (2015) Toward V2I communication technology-based solution for reducing road traffic congestion in smart cities. Paper presented at the International symposium on Networks, computers and communications (ISNCC), IEEE, 13–15 May 2015aGoogle Scholar
  15. Djahel S, Smith N, Wang S, Murphy J (2015) Reducing emergency services response time in smart cities: An advanced adaptive and fuzzy approach. Paper presented at the 2015 IEEE First international smart cities conference (ISC2), 25–28 Oct 2015bGoogle Scholar
  16. Engelbrecht AP (2006) Fundamentals of computational swarm intelligence. John Wiley and SonsGoogle Scholar
  17. Fiedrich F, Gehbauer F, Rickers U (2000) Optimized resource allocation for emergency response after earthquake disasters. Saf Sci 35(1–3):41–57CrossRefGoogle Scholar
  18. Ghomian Z, Yousefian S (2017) Natural disasters in the Middle-East and North Africa With a focus on Iran: 1900 to 2015. Health Emerg Disasters Q 2(2):53–62CrossRefGoogle Scholar
  19. Gong Q, Batta R (2007) Allocation and reallocation of ambulances to casualty clusters in a disaster relief operation. IIE Trans 39(1):27–39CrossRefGoogle Scholar
  20. Haghani A, Hu H, Tian Q (2003) An optimization model for real-time emergency vehicle dispatching and routing. Paper presented at the transportation research board 82nd annual meeting, Washington, DC, 12–16 Jan 2003Google Scholar
  21. Hawe GI, Coates G, Wilson DT, Crouch RS (2015) Agent-based simulation of emergency response to plan the allocation of resources for a hypothetical two-site major incident. Eng Appl Artif Intell 46:336–345CrossRefGoogle Scholar
  22. Hirokawa N, Osaragi T (2016) Access time of emergency vehicles under the condition of street blockages after a large earthquake. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Paper presented at the 1st international conference on smart data and smart cities, 7–9 Sep 2016CrossRefGoogle Scholar
  23. Hooshangi N, Alesheikh AA (2018) Developing an agent-based simulation system for post-earthquake operations in uncertainty conditions: a proposed method for collaboration among agents. ISPRS Int J Geo-Inf 7(1):27CrossRefGoogle Scholar
  24. Ibrion M, Mokhtari M, Parsizadeh F, Nadim F (2015) Timescape of the earthquake disasters in Iran: the intricacies of earthquake time and earthquake disaster risk reduction. Geografiska Annaler Ser A Phys Geogr 97(1):197–216CrossRefGoogle Scholar
  25. Imawan A, Indikawati FI, Kwon J, Rao P (2016) Querying and extracting timeline information from road traffic sensor data. Sensors 16(9):1340CrossRefGoogle Scholar
  26. Karimi F, Delavar M, Mostafavi M (2009) Space allocation of educational centers using multiplicatively weighted voronoi diagram. In WG II/2, II/3, II/4: Workshop on Quality, Scale and Analysis Aspects of City ModelsGoogle Scholar
  27. Li D, Shan J, Shao Z, Zhou X, Yao Y (2013) Geomatics for smart cities-concept, key techniques, and applications. Geo-spatial Inf Sci 16(1):13–24CrossRefGoogle Scholar
  28. Li X, Zhao Z, Zhu X, Wyatt T (2011) Covering models and optimization techniques for emergency response facility location and planning: a review. Math Methods Oper Res 74(3):281–310CrossRefGoogle Scholar
  29. Lopez B, Innocenti B, Busquets D (2008) A multiagent system for coordinating ambulances for emergency medical services. IEEE Intell Syst 23(5)CrossRefGoogle Scholar
  30. Mustafa A (2013) Wireless applications of GIS in dynamic platforms: a case study in laptops and mobile phones. PhD Thesis, Mangalore University, IndiaGoogle Scholar
  31. Okabe A, Boots B, Sugihara K, Chiu S (1992) Spatial tessellations: concepts and applications of Voronoi diagrams. Chichester, UKGoogle Scholar
  32. Pyrga E, Schulz F, Wagner D, Zaroliagis C (2008) Efficient models for timetable information in public transportation systems. J Exp Algorithmics (JEA) 12:2–4Google Scholar
  33. Reitsma R, Trubin S, Mortensen E (2007) Weight-proportional space partitioning using adaptive Voronoi diagrams. Geoinformatica 11(3):383–405CrossRefGoogle Scholar
  34. Rezayan H, Najian A (2008) Land use allocation optimization using advanced geographic information analyzes. World Appl Sci J 3:136–142Google Scholar
  35. Rini DP, Shamsuddin SM, Yuhaniz SS (2011) Particle swarm optimization: technique, system and challenges. Int J Comput Appl 14(1):19–26Google Scholar
  36. Roozemond DA (2001) Using intelligent agents for pro-active, real-time urban intersection control. Eur J Oper Res 131(2):293–301CrossRefGoogle Scholar
  37. Rusell S, Norvig P (2003) Artificial intelligent: a modern approachGoogle Scholar
  38. Sang TX (2013) Multi-criteria decision making and task allocation in multi-agent based rescue simulation. PhD Thesis, Japan Graduate School of Science and Engineering, Saga University, JapanGoogle Scholar
  39. Santosa B (2006) Tutorial Particle Swarm Optimization. Kampus ITS, Sukolilo Surabaya, p 66Google Scholar
  40. Serafini P (1987) Some Considerations About Computational Complexity for Multi Objective Combinatorial Problems. Recent Advances and Historical Development of Vector Optimization. Springer, Berlin, Heidelberg:222–232CrossRefGoogle Scholar
  41. Talarico L, Meisel F, Sorensen K (2015) Ambulance routing for disaster response with patient groups. Comput Oper Res 56:120–133CrossRefGoogle Scholar
  42. Ticha HB, Absi N, Feillet D, Quilliot A (2017) A solution method for the Multi-destination Bi-objectives Shortest Path Problem. Ecole des Mines de Saint Etienne, CMP, Gardanne, FranceGoogle Scholar
  43. Wang Y, Colledanchise M, Marzinott A, Ogren P (2014) A distributed convergent solution to the ambulance positioning problem on a streetmap graph. IFAC Proc Vol 47(3):9190–9196CrossRefGoogle Scholar
  44. Wang z, Zlatanova S (2016) Multi-agent based path planning for first responders among moving obstacles. Comput Environ Urban Syst 56:48–58CrossRefGoogle Scholar
  45. Wood L (1974) Spatial interaction and partitions of rural market space. Mag Econ Soc Geogr 65(1):23–34CrossRefGoogle Scholar
  46. Yang XS (2010) Engineering optimization: an introduction with metaheuristic applications. John Wiley and SonsGoogle Scholar
  47. Yue Y, Marla L, Krishnan R (2012) An efficient simulation-based approach to ambulance fleet allocation and dynamic redeployment. Paper presented at the AAAI on artificial intelligence, 22–26 Jul 2012Google Scholar
  48. Zhuang J, Bier VM (2007) Balancing terrorism and natural disasters—defensive strategy with endogenous attacker effort. Oper Res 55(5):976–991CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sanaz Azimi
    • 1
  • Mahmoud Reza Delavar
    • 1
    Email author
  • Abbas Rajabifard
    • 2
  1. 1.School of Surveying and Geospatial Engineering, College of Engineering, Center of Excellence in Geomatic Engineering in Disaster ManagementUniversity of TehranTehranIran
  2. 2.Department of Infrastructure EngineeringCentre for Spatial Data Infrastructures and Land Administration, The University of MelbourneMelbourneAustralia

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