Genetic algorithm with spatial receding horizon control for the optimization of facility locations

Inspired by the temporal receding horizon control in control engineering, this paper reports a novel spatial receding horizon control (SRHC) strategy to partition the facility location optimization problem (FLOP), in order to reduce the complexity caused by the problem scale. Traditional problem partitioning methods can be viewed as a special case of the proposed SRHC, i.e., one-step-wide SRHC, whilst the method in this paper is a generalized N-step-wide SRHC, which can make a better use of global information of the route network where a given number of facilities need to be set up. With SRHC to partition the FLOP, genetic algorithm (GA) is integrated as optimizer to resolve the partitioned problem within each spatial receding horizon. On one hand, SRHC helps to improve the scalability of GA. On the other, the population feature of GA helps to reduce the shortsighted performance of SRHC. The effectiveness and efficiency of the reported SRHC and GA for the FLOP are demonstrated by comparative simulation results.