Modelling Ambulance Deployment with CarmaCARMA

  • Vashti Galpin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9686)


Carma is a process-algebra influenced language for the quantitative modelling of collective adaptive systems which involve collaboration and coordination. These systems consist of multiple components that interact to achieve certain goals and that adapt to changes in the environment. As a case study for the application of Carma, this paper presents an ambulance deployment system where ambulances go to medical incidents and either treat patients at the scene or transfer them to hospital. The Eclipse Carma Plug-in is used to simulate the system, and demonstrate its behaviour in different circumstances.


Short Route Process Algebra Heuristic Function Late Rate Ambulance Base 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is supported by the EU project QUANTICOL, 600708. The author thanks Jane Hillston and Yehia Abd Alrahman for their useful comments.


  1. 1.
    Alanis, R., Ingolfsson, A., Kolfal, B.: A Markov chain model for an EMS system with repositioning. Prod. Oper. Manag. 22, 216–231 (2013)CrossRefGoogle Scholar
  2. 2.
    Alrahman, Y.A., De Nicola, R., Loreti, M., Tiezzi, F., Vigo, R.: A calculus for attribute-based communication. In: Proceedings of SAC, pp. 1840–1845 (2015)Google Scholar
  3. 3.
    Bortolussi, L., De Nicola, R., Galpin, V., Gilmore, S., Hillston, J., Latella, D., Loreti, M., Massink, M.: Carma: collective adaptive resource-sharing markovian agents. In: Proceedings of QAPL 2015. EPTCS, vol. 194, pp. 16–31 (2015)Google Scholar
  4. 4.
    Church, R., Velle, C.: The maximal covering location problem. Pap. Reg. Sci. Assoc. 32, 101–118 (1974)CrossRefGoogle Scholar
  5. 5.
    Ciancia, V., De Nicola, R., Hillston, J., Latella, D., Loreti, M., Massink, M.: CAS-SCEL semantics and implementation. QUANTICOL Deliverable D4.2 (2015)Google Scholar
  6. 6.
    Daskin, M.: A maximum expected covering location model: formulation, properties and heuristic solution. Transp. Sci. 17, 48–70 (1983)CrossRefGoogle Scholar
  7. 7.
    De Nicola, R., Latella, D., Loreti, M., Massink, M.: A uniform definition of stochastic process calculi. ACM Comput. Surv. 46, 5 (2013)CrossRefzbMATHGoogle Scholar
  8. 8.
    De Nicola, R., Loreti, M., Pugliese, R., Tiezzi, F.: A formal approach to autonomic systems programming: the SCEL language. ACM TAAS 9, 7:1–7:29 (2014)Google Scholar
  9. 9.
    Feng, C., Hillston, J.: PALOMA: a process algebra for located Markovian agents. In: Norman, G., Sanders, W. (eds.) QEST 2014. LNCS, vol. 8657, pp. 265–280. Springer, Heidelberg (2014)Google Scholar
  10. 10.
    Gendreau, M., Laporte, G., Semet, F.: A dynamic model and parallel tabu search heuristic for real-time ambulance relocation. Parallel Comput. 27, 1641–1653 (2001)CrossRefzbMATHGoogle Scholar
  11. 11.
    Gillespie, D.: A general method for numerically simulating the stochastic time evolution of coupled chemical reactions. J. Comput. Phys. 22, 403–434 (1976)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hillston, J.: A Compositional Approach to Performance Modelling. CUP. Cambridge University Press, New York (1996)CrossRefzbMATHGoogle Scholar
  13. 13.
    Jagtenberg, C.: Personal communication (2016)Google Scholar
  14. 14.
    Jagtenberg, C., Bhulai, S., van der Mei, R.: An efficient heuristic for real-time ambulance redeployment. Oper. Res. Health Care 4, 27–35 (2015)CrossRefGoogle Scholar
  15. 15.
    Maxwell, M., Henderson, S., Topaloglu, H.: Tuning approximate dynamic programming policies for ambulance redeployment via direct search. Stoch. Syst. 3, 322–361 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Toregas, C., Swain, R., ReVelle, C., Bergman, L.: The location of emergency service facilities. Oper. Res. 19, 1363–1373 (1971)CrossRefzbMATHGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2016

Authors and Affiliations

  1. 1.Laboratory for Foundations of Computer ScienceSchool of Informatics, University of EdinburghEdinburghUK

Personalised recommendations