The Assessment and Comparison of a Genetic Algorithm, Simulated Annealing and Cuckoo Optimization Algorithm for Optimization of the Facility Location under Competitive Conditions (Case Study: Banks)

This paper determines the location of bank branches under competitive conditions with different attractive conditions. Finding an optimum location of branches depends on many factors and these problems are known as NP-hard problems. Despite being approximate methods, meta-heuristic algorithms seem suitable tools for solving NP-hard problems. In this paper, Genetic Algorithm (GA), Simulated Annealing (SA) and Cuckoo Optimization Algorithm (COA) are applied for finding the best location of bank branches. From marketing point of view, the aim is to attract more customers while the number of attracted persons to a new branch should be acceptable. The new methods have capability to find the optimum location of new branches under competitive conditions. The location of a new branch should be as far away as possible from branches of the same bank. The other condition is that the total number of customers for the new branch should not be less than a specified number, while the new branch should not attract customers of old branches of the same bank more than a threshold. To fulfill this propose a part of the Tabriz city was selected for implementation. Finally, to evaluate quality and accuracy of the algorithms, several iterations with different seeds are performed. The results of statistical and final tests indicate that the accuracy and convergence speed of Cuckoo Optimization Algorithm are more than the Simulated Annealing and Genetic Algorithms in finding optimal location of bank branches under competitive conditions.