Design of a reliable logistics network with hub disruption under uncertainty

• We provide models for logistics network using hub location topology under disruption.
• We considered both complete and partial disruptions at hubs.
• Disruptions at hubs and re-allocating non-hub nodes to backup hubs are considered.
• We proposed an efficient lower bound approach for finding near optimal solution.
• We developed a new hybrid self-adaptive meta-heuristic algorithm based on ICA and GA.

In this study, we design a reliable logistics network based on a hub location problem, which is less sensitive to disruption and it performs efficiently when disruption occurs. A new mixed-integer programming model is proposed to minimize the total sum of the nominal and expected failure costs. This model considers complete and partial disruption at hubs. In addition, we propose a new hybrid meta-heuristic algorithm based on genetic and imperialist competitive algorithms. We compare the performance of the proposed algorithm with a new lower bound method in terms of the CPU time and solution quality. Furthermore, we conclude that a considerable improvement in the reliability of the network can be achieved with only a slight increase in the total cost. Finally, we demonstrate that the networks designed using our model are less conservative and more robust to disruption compared with those designed based on other robustness measures.