Optimal attack and reinforcement of a network
Journal of the ACM (JACM)
Integer and combinatorial optimization
Integer and combinatorial optimization
The network inhibition problem
STOC '93 Proceedings of the twenty-fifth annual ACM symposium on Theory of computing
Heuristics for Multi-Stage Interdiction of Stochastic Networks
Journal of Heuristics
Defending Critical Infrastructure
Interfaces
Solving the Bi-Objective Maximum-Flow Network-Interdiction Problem
INFORMS Journal on Computing
Deterministic network interdiction
Mathematical and Computer Modelling: An International Journal
Maximum dynamic network flow interdiction problem: New formulation and solution procedures
Computers and Industrial Engineering
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A modified shortest path network interdiction model is approximated in this work by a constrained binary knapsack which uses aggregated arc maximum flow as the objective function coefficient. In the modified shortest path network interdiction problem, an attacker selects a path of highest non-detection probability on a network with multiple origins and multiple available targets. A defender allocates a limited number of resources within the geographic region of the network to reduce the maximum network non-detection probability between all origin-target pairs by reducing arc non-detection probabilities and where path non-detection probability is modeled as a product of all arc non-detection probabilities on that path. Traditional decomposition methods to solve the shortest path network interdiction problem are sensitive to problem size and network/regional complexity. The goal of this paper is to develop a method for approximating the regional allocation of defense resources that maintains accuracy while reducing both computational effort and the sensitivity of computation time to network/regional properties. Statistical and spatial analysis methods are utilized to verify approximation performance of the knapsack method in two real-world networks.