A new hybrid variable-length GA and PSO algorithm in continuous facility location problem with capacity and service level constraints

This paper considers a continuous capacitated facility location problem without a priori knowledge of the desired number of facilities. The demand locations and volume are known to the decision maker. A new hybrid evolutionary algorithm combining variable-length GA (genetic algorithm) and PSO (particle swarm optimization) together is proposed to solve the problem. For variable-length GA, the chromosome in the population varies with the number of facilities to be located, and special crossover and mutation operators are designed. For PSO algorithm, it is combined with ATL (alternative transporting location) method to attain the appropriate location of each facility. Furthermore, an external population is adopted to gather the inferior solutions instead of abandoning them simply. This work has been developed as an Eclipse RCP tool and applied in business cases.