A tri-level r -interdiction median model for a facility location problem under imminent attack

Abstract

Abstract When systems lose their critical linkage and key facilities due to natural disasters or intentional attacks, their performance may be at risk. This paper focuses on reducing the effect of these events, in which facilities can be located and fortified with a limited budget. To address this issue, a conceptual framework is proposed for contributing to strategic decisions-making by considering the worst possible condition in interdiction problems. Hence, we apply a tri-level facility location r-interdiction median (TFLRIM) model based on leader-follower games to minimize the total cost before and after interdiction. Beside, locating the facilities is carried out based on the worst case scenario when an attacker disrupts the system under uncertainty. To cope with uncertain parameters in the presented model, the Me method is utilized. We propose four hybrid meta-heuristics based on three algorithms, namely Tabu Search (TS), Rainfall Optimization (RFO) and Random Greedy Search (RGS). These algorithms are employed to solve 24 random instances, in which their performances are evaluated based on the comparison of their solutions with the obtained exact solutions by the explicit enumeration method. The results show that the hybrid algorithm of RFO and RGS outperforms the current algorithms. Finally, sensitivity analyses of the model shows that proper design of defensive systems has an effective role in reducing the losses of such these systems.