For this paper, we explored the implementation of the cuckoo search algorithm applied to the capacitated vehicle routing problem. The cuckoo search algorithm was implemented with Lévy flights with the 2-opt and double-bridge operations, and with 500 iterations for each run. The algorithm was tested on the problem instances from the Augerat benchmark dataset. The algorithm did not perform well on the problem instances, save for a select few on which the algorithm achieved the close to near-optimal result and one on which the algorithm achieved the optimal result. Increasing the number of iterations for each run of the algorithm on the two large-scale problem instances led to obtaining solutions closer to the optimal solution compared to the ones obtained with fewer number iterations. This gives an idea that the larger the problem instance becomes, the slower the algorithm converges to the optimal solution. Several other factors may also have contributed to the overall performance of the algorithm. Regardless of its performance, the algorithm was able to obtain routes that satisfied the constraints of the capacitated vehicle routing problem. The potential of the cuckoo search algorithm in solving combinatorial problems is demonstrated in this study in which the performance of the algorithm on routing problems was explored.

Facility location problem (FLP) is a mathematical way to optimally locate facilities within a set of candidates to satisfy the requirements of a given set of clients. This study addressed the uncapacitated FLP as it assures that the capacity of every selected facility is finite. Thus, even if the demand is not known, which often is the case, in reality, organizations may still be able to take strategic decisions such as locating the facilities. There are different approaches relevant to the uncapacitated FLP. Here, the cuckoo search via Lévy flight (CS-LF) was used to solve the problem. Though hybrid methods produce better results, this study employed CS-LF to determine first its potential in finding solutions for the problem, particularly when applied to a real-world problem. The method was applied to the data set obtained from a department store in Davao City, Philippines. Results showed that applying CS-LF yielded better facility locations compared to particle swarm optimization and other existing algorithms. Although these results showed that CS-LF is a promising method to solve this particular problem, further studies on other FLP are recommended to establish a strong foundation of the capability of CS-LF in solving FLP.