Robust clustering approach for the shortest path problem on finite capacity fuzzy queuing network

This paper introduces a new model in the field of fuzzy queuing networks based on the clustering and finite capacity concepts. The proposed model includes fuzzy queuing systems which are located at the nodes of the network. Arc lengths, interarrival times, and service times are all fuzzy triangular fuzzy numbers. In order to find the shortest path on this network, queuing systems should be transformed to waiting times. The waiting times of each system are calculated by a conditional transformation. On the other hand, a robust fuzzy method is proposed for clustering of the arriving customers to the network. Robustness of this method prevents noisy data to affect results. Outputs of the clustering reduce shortest path calculations drastically. Based on a simulation process, the fuzzy queuing network is reduced to a deterministic network. A robust simulated annealing is designed for this network to find the shortest path. Numerical results showed that the clustering process is successful in eliminating outliers, and could be addressed as an efficient method. The convergence and solution time of the algorithm is reasonably better in comparison with published methods. Several experiments are conducted to compare the proposed method with corresponding researches.