A heuristic approach based on ant colony optimization algorithm for solving uncapacitated facility location problem

The uncapacitated Facility location problem (UFLP) is one of the most widely studied location problems in combinatorial optimization. The UFLP seeks to determine a set of façıkties to open such that all customers are serviced by a Facility and the sum of the fixed costs of opening and operating the façıkties and the variable costs of supplying the customers from the opened façıkties is minimized. Since UFLP is NP-hard problem, various algorithms based on meta-heuristics have been proposed to solve this problem in the literature. In this thesis, a heuristic algorithm based on ant colony optimization for the UFLP is proposed. The performance of the proposed heuristic algorithm, which is the first application of ACO to the UFLP, is investigated using benchmark problems and compared with other heuristic algorithms in the literature. The experimental analysis indicates that the proposed algorithm is an effective and efficient as well as other heuristic algorithms in the literature.