PROPOSING AN ADAPTIVE PARTICLE SWARM OPTIMIZATION FOR A NOVEL BI-OBJECTIVE QUEUING FACILITY LOCATION MODEL

With regards to the many decisions which are made every day in service and industrial applications, we focus on determination of the number of required facilities along with the relevant allocation process. Goal of this research is proposing a novel bi-objective facility location problem within batch arrival queuing framework under capacity, budget, and nearest-facility constraints. Two objective functions are considered which are minimizing sum of the travel time and waiting and minimizing maximum of ideal time pertinent to each facility, respectively. Second objective causes to obtain the best combination of the facilities which are more equilibrium for the proposed model solutions. Since this type of problem is strictly NPhard, an efficient meta-heuristic algorithm namely particle swarm optimization algorithm with considering a specific representation process has been proposed. Since the output quality of the metaheuristic algorithms is severely depending on its parameters, we proposed an adaptive version of particle swarm optimization to be tuned the parameters of the algorithm. At the end, the results analysis represents the applicability of the proposed methodology.