Self-organizing feature maps for solving location-allocation problems with rectilinear distances
This study deals with solving uncapacitated location-allocation (LA) problems with rectilinear distances by using a method based on Kohonen self-organizing feature maps (SOFMs). By treating LA problems as clustering problems, this method has the advantage of extracting the structure of the input data by a self-organizing process based on adaptation rules. In this paper, a heuristic method is constructed by using SOFMs with a guided refining procedure, and its performance is compared with simulated annealing. The experimental results using the proposed guided refining procedure to reinforce the SOFM method show that the proposed method is excellent in terms of quality of solution and speed of computation. In addition, the experimental results suggest that SOFMs may provide an excellent approach when generating initial solutions for other heuristic or exact algorithms. This conjecture is made because most of the solutions yielded by SOFM are close to the optimal solution in all experiments.
location-allocation problem combinatorial optimization problem neural networks self-organizing map