A swarm intelligence based sample average approximation algorithm for the capacitated reliable facility location problem

We present a novel hybrid method, swarm intelligence based sample average approximation (SIBSAA), for solving the capacitated reliable facility location problem (CRFLP). The CRFLP extends the well-known capacitated fixed-cost facility problem by accounting for the unreliability of facilities. The standard SAA procedure, while effectively used in many applications, can lead to poor solution quality if the selected sample sizes are not sufficiently large. With larger sample sizes, however, the SAA method is not practical due to the significant computational effort required. The proposed SIBSAA method addresses this limitation by using smaller samples and repetitively applying the SAA method while injecting social learning in the solution process inspired by the swarm intelligence of particle swarm optimization. We report on experimental study results showing that the SIBSAA improves the computational efficiency significantly while attaining same or better solution quality than the SAA method.