Designing A Resilience Logistics Network Using Metaheuristics

The global manufacturing sector faces significant logistic challenges because of rising costs, longer lead times for material deliveries, and demand uncertainties. Logistics network design is a complex combinatorial optimization problem that involves intermodal transportation options. The problem has been addressed through linear programming, non-linear integer programming, simulation techniques, and metaheuristics. A US-based manufacturer wants to redesign its logistics network to receive supplies from Asia and distribute materials across its worldwide facilities. The network includes suppliers consolidating their materials at a distribution center, transferring them to the manufacturing facility, and distributing them to other facilities. This research aims to optimize an intermodal logistics network using metaheuristics. The objective function involves minimizing transportation time and costs and designing a more reliable network. The model employs simulated annealing to address transportation complexities and optimize the network. Additionally, sensitivity analysis is used to estimate the model's robustness by assessing the effect of parameter variations on the network. This results in designing a more robust, efficient, and diversified logistics network with reduced costs and transportation lead times for the manufacturing company.