Management of Emergency Response Teams under Stochastic Demands

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.