Management of Emergency Response Teams under Stochastic Demands

Iliya Markov, Sacha Varone

Abstract

We propose a stochastic optimization model for the composition of emergency response teams. An emergency intervention requires first an evaluation of the situation which results in the need of different skills. People involved in the response team must therefore comply with the required skills, be available, with a past and future workload respecting contractual compliance. In addition, we must also anticipate the possibility of future interventions that will require rare skills. It is the uncertain future demand for these skills that introduces stochasticities to the system. Since shifting agents between emergencies may be impossible or impractical, we would like to ensure that rare skills are not wasted but assigned to the emergency that most needs them. We model this with a mixed integer linear program implemented in AMPL and capable of being solved in real-time on common solvers.

References

  1. Albareda-Sambola, M. and Fernández, E. (2000). The stochastic generalized assignment problem with Bernoulli demands. Sociedad de Estadística e Investigación Operativa Top, 8(2):165-190.
  2. Albareda-Sambola, M., van der Vlerk, M. H., and Fernández, E. (2006). Exact solutions to a class of stochastic generalized assignment problems. European Journal of Operational Research, 173(2):465- 487.
  3. Altay, N. and Green, W. G. (2006). OR/MS research in disaster operations management. European Journal of Operational Research, 175(1):475-493.
  4. Barbarosog?lu, G. and Arda, Y. (2004). A two-stage stochastic programming framework for transportation planning in disaster response. Journal of the Operational Research Society, 55(1):43-53.
  5. Beraldi, P., Bruni, M., and Conforti, D. (2004). Designing robust emergency medical service via stochastic programming. European Journal of Operational Research, 158(1):183-193.
  6. Boon, B. H. and Sierksma, G. (2003). Team formation: Matching quality supply and quality demand. European Journal of Operational Research, 148(2):277- 292.
  7. Brown, G. G. and Graves, G. W. (1981). Real-time dispatch of petroleum tank trucks. Management Science, 27(1):19-32.
  8. Brown, G. G. and Vassiliou, A. L. (1993). Optimizing disaster relief: Real-time operational and tactical decision support. Naval Research Logistics, 40(1):1-23.
  9. Campbell, G. M. (1999). Cross-utilization of workers whose capabilities differ. Management Science, 45(5):722-732.
  10. Campbell, G. M. (2011). A two-stage stochastic program for scheduling and allocating cross-trained workers. The Journal of the Operational Research Society, 62(6):1038-1047.
  11. Campbell, G. M. and Diaby, M. (2002). Development and evaluation of an assignment heuristic for allocating cross-trained workers. European Journal of Operational Research, 138(1):9-20.
  12. Caron, G., Hansen, P., and Jaumard, B. (1999). The assignment problem with seniority and job priority constraints. Operations Research, 47(3):449-453.
  13. Cattrysse, D. G. and Wassenhove, L. N. V. (1992). A survey of algorithms for the generalized assignment problem. European Journal of Operational Research, 60(3):260-272.
  14. Felici, G. and Mecoli, M. (2007). Resource assignment with preference conditions. European Journal of Operational Research, 180(2):519-531.
  15. Fowler, J. W., Wirojanagud, P., and Gel, E. S. (2008). Heuristics for workforce planning with worker differences. European Journal of Operational Research, 190(3):724-740.
  16. Gilberti, D. and Righini, G. (2007). Optimization of duties assignment in emergency services. In Service Operations and Logistics, and Informatics, 2007, IEEE International Conference on, Philadelphia, PA, pages 1-6.
  17. Green, L. and Kolesar, P. (2004). Improving emergency responsiveness with management science. Management Science, 50(8):1001-1014.
  18. Jenkins, L. (2000). Selecting scenarios for environmental disaster planning. European Journal of Operational Research, 121(2):275-286.
  19. Kolesar, P. and Walker, W. E. (1974). An algorithm for the dynamic relocation of fire companies. Operations Research, 22(2):249-274.
  20. Kuhn, H. W. (1955). The Hungarian method for the assignment problem. Naval Research Logistics Quarterly, 2(1-2):83-97.
  21. Mazzola, J. and Neebe, A. (1986). Resource-constrained assignment scheduling. Operations Research, 34(4):560-572.
  22. Mete, H. O. and Zabinsky, Z. B. (2010). Stochastic optimization of medical supply location and distribution in disaster management. International Journal of Production Economics, 126(1):76-84.
  23. Pentico, D. W. (2007). Assignment problems: A golden anniversary survey. European Journal of Operational Research, 176(2):774-793.
  24. Rolland, E., Patterson, R., Ward, K., and Dodin, B. (2010). Decision support for disaster management. Operations Management Research, 3(1):68-79.
  25. Sayin, S. and Karabati, S. (2007). Assigning crosstrained workers to departments: A two-stage optimization model to maximize utility and skill improvement. European Journal of Operational Research, 176(3):1643-1658.
  26. Shipley, M. F. and Johnson, M. (2009). A fuzzy approach for selecting project membership to achieve cognitive style goals. European Journal of Operational Research, 192(3):918 - 928.
  27. Simpson, N. C. and Hancock, P. G. (2009). Fifty years of operational research and emergency response. Journal of the Operational Research Society, 60(S1):126-139.
  28. Spoerl, D. and Wood, R. (2004). A stochastic generalized assignment problem. Working paper, Department of Operations Research, Naval Postgraduate School, Monterey, California.
  29. Toktas, B., Yen, J. W., and Zabinsky, Z. B. (2004). A stochastic programming approach to resourceconstrained assignment problems. Stochastic Programming E-Print Series, http://www.speps.org.
  30. Toktas, B., Yen, J. W., and Zabinsky, Z. B. (2006). Addressing capacity uncertainty in resource-constrained assignment problems. Computers & Operations Research, 33(3):724-745.
  31. Volgenant, A. (2004). A note on the assignment problem with seniority and job priority constraints. European Journal of Operational Research, 154(1):330-335.
  32. Wirojanagud, P., Gel, E. S., Fowler, J. W., and Cardy, R. (2007). Modelling inherent worker differences for workforce planning. International Journal of Production Research, 45(3):525-553.
  33. Woodruff, C. J. (2010). Multivariate optimisation for procurement of emergency services equipment - teams of the best or the best of teams? European Journal of Operational Research, 205(1):186-194.
  34. Yi, W. and Kumar, A. (2007). Ant colony optimization for disaster relief operations. Transportation Research Part E: Logistics and Transportation Review, 43(6):660-672.
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Paper Citation


in Harvard Style

Markov I. and Varone S. (2013). Management of Emergency Response Teams under Stochastic Demands . In Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-8565-40-2, pages 159-167. DOI: 10.5220/0004196001590167


in Bibtex Style

@conference{icores13,
author={Iliya Markov and Sacha Varone},
title={Management of Emergency Response Teams under Stochastic Demands},
booktitle={Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2013},
pages={159-167},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004196001590167},
isbn={978-989-8565-40-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Management of Emergency Response Teams under Stochastic Demands
SN - 978-989-8565-40-2
AU - Markov I.
AU - Varone S.
PY - 2013
SP - 159
EP - 167
DO - 10.5220/0004196001590167