EN|RU
English version:
Journal of Applied and Industrial Mathematics, 2020, 14:3, 416-429

Volume 27, No 3, 2020, P. 5-27

UDC 519.8+518.25
V. L. Beresnev and A. A. Melnikov
Planning a defense that minimizes a resource deficit in the worst-case scenario of supply network
destruction

Abstract:
We consider same model of planning the defense of edges of a supply network. The vertices of the network represent the consumers and the providers of a resource, while the edges allow us to transmit the resource without delays and capacity constraints. The Defender commits a bounded budget to protect some of the edges, aiming to minimize the damage that is caused by the destruction of the unprotected edges. To measure the damage, we apply the value of the total resource deficit caused by the worst-case scenario of partial network destruction. The Defender’s problem falls into the family of “Defender–Attacker” problems that are formalized as the minimax mixed-integer programming problems. To find an optimal Defender’s solution, we suggest some two cut generation schemes based on a reformulation of the problem as a mixed-integer problem with exponentially many constraints.
Tab. 2, illustr. 4, bibliogr. 13.

Keywords: “defender–attacker” problem, total deficit, cut generation.

DOI: 10.33048/daio.2020.27.687

Vladimir L. Beresnev 1,2
Andrey A. Melnikov 1,2

1. Sobolev Institute of Mathematics,
4 Koptyug Ave., 630090 Novosibirsk, Russia
2. Novosibirsk State University,
2 Pirogov St., 630090 Novosibirsk, Russia
e-mail: beresnev@math.nsc.ru, melnikov@math.nsc.ru

Received May 9, 2020
Revised May 22, 2020
Accepted May 25, 2020

References

[1] M. Grötschel, C. L. Monma, and M. Stoer, Design of survivable networks, Handb. Oper. Res. Manage. Sci. 7, 617–672 (1995).

[2] D. S. Callaway, M. E. J. Newman, S. H. Strogatz, and D. J. Watts, Network robustness and fragility: Percolation on random graphs, Phys. Rev. Lett. 85, 5468–5471 (2000).

[3] A. Nagurney and Q. Qiang, Fragile networks: Identifying vulnerabilities and synergies in an uncertain world, Int. Trans. Oper. Res. 19 (1–2), 123–160 (2009).

[4] G. Brown, M. Carlyle, J. Salmerón, and K. Wood, Defending critical infrastructure, Interfaces 36 (6), 530–544 (2006).

[5] M. P. Scaparra and R. L. Church, A bilevel mixed-integer program for critical infrastructure protection planning, Comput. Oper. Res. 35, 1905–1923 (2008).

[6] B. Golden, A problem in network interdiction, Naval Res. Logist. Q. 25 (4), 711–713 (1978).

[7] R. K. Wood, Deterministic network interdiction, Math. Comput. Model. 17 (2), 1–18 (1993).

[8] S. Sadeghi, A. Seifi, and E. Azizi, Trilevel shortest path network interdiction with partial fortification, Comput. Ind. Eng. 106, 400–411 (2017).

[9] L. Dong, L. Xu-chen, Y. Xiang-tao, and W. Fei, A model for allocating protection resources in military logistics distribution system based on maximal covering problem, in 2010 Int. Conf. Logist. Syst. Intell. Manage., Harbin, China, Jan. 9–10, 2010, Vol. 1 (IEEE, Piscataway, 2010), pp. 98–101.

[10] E. V. Alekseeva and Yu. A. Kochetov, Metaheuristics and exact methods for the discrete ($r|p$)–centroid problem, in Metaheuristics for bi-level optimization (Springer, Berlin, 2013), pp. 189–219 (Stud. Comput. Intell., Vol. 482).

[11] M. C. Roboredo, L. Aizemberg, and A. A. Pessoa, An exact approach for the $r$-interdiction covering problem with fortification, Cent. Eur. J. Oper. Res. 27, 111–131 (2019).

[12] M. C. Roboredo and A. A. Pessoa, A branch-and-cut algorithm for the discrete ($r|p$)-centroid problem, Eur. J. Oper. Res. 224 (1), 101–109 (2013).

[13] Gurobi optimizer reference manual (Gurobi Optimization, 2020). Available at
http://www.gurobi.com/documentation/9.0/refman/index.html (accessed May 25, 2020).
 © Sobolev Institute of Mathematics, 2015