A Hybrid Fuzzy Optimization Model to minimize logistics cost

This paper presents a supply chain network in which supplier selection, lateral transshipment, and vehicle routing can be involved. We develop a Hybrid Fuzzy Optimization Model (HFOM) based on the integration of fuzzy logic and genetic algorithms to solve the problem. In order to demonstrate the effectiveness of the HFOM, several approaches including branch and bound, standard GA, simulated annealing, and tabu search, are utilized to compare with the HFOM through simulations. Results show that the HFOM outperforms other search methods.