In this study, we consider a variant of the Bilevel Uncapacitated Facility
Location Problem (BLUFLP), in which the clients choose suppliers based on
their own preferences. We propose and compare three metaheuristic approaches
for solving this problem: Particle Swarm Optimization (PSO), Simulated
Annealing (SA), and a combination of Reduced and Basic Variable Neighborhood
Search Method (VNS). We used the representation of solutions and objective
function calculation that are adequate for all three proposed methods.
Additional strategy is implemented in order to provide significant time
savings when evaluating small changes of solution's code in improvement
parts. Constructive elements of each of the proposed algorithms are adapted
to the problem under consideration. The results of broad computational tests
on modified problem instances from the literature show good performance of
all three proposed methods, even on large problem dimensions. However, the
obtained results indicate that the proposed VNS-based has significantly
better performance compared to SA and PSO approaches, especially when solving
large-scale problem instances. Computational experiments on large scale
benchmarks demonstrate that the VNS-based method is fast, competitive, and
able to find high-quality solutions, even for large-scale problem instances
with up to 2000 clients and 2000 potential facilities within reasonable CPU
times.
Metaheuristic approaches to solving large-scale Bilevel Uncapacitated Facility Location Problem with clients' preferences
