A simulation-based optimization approach for healthcare facility location allocation decision

The transition from deterministic to probabilistic demand when analyzing specialized healthcare services leads to a more realistic situation where there is variability in demand for services which cannot be exactly determined. This paper integrates Monte Carlo simulation with particle swarm optimization to solve a modified capacitated maximal covering location problem (CMCLP) considering stochastic demand for such services. Results of the proposed approach prove that considering the probabilistic nature of demand is very important as it has a direct influence on the facility planning decision; not taking into consideration this characteristic will lead to increasing the probability of unsatisfied demand.