Multi-objective Stackelberg game model for water supply networks against interdictions with incomplete information

Abstract Water supply networks are infrastructures pivotal to economic development and living standards, of which the increasing complexity and interdependencies have brought challenges for the protection and enhancement of water supplies. We address the problem on how to defend water supply networks with hydraulic characteristics against an interdictor by building a multi-objective Stackelberg game model with incomplete information. In this model, the defender and the interdictor, both considered as rational players, choose a subset of network components to defend or interdict based on their payoffs. The defender, with incomplete information on the interdictor's efforts, initiates to trade off the two objectives of maximizing the expected network satisfaction rate and of minimizing defense efforts, whereas the interdictor, with no information on network operational capacity, follows to trade off the objectives of minimizing the expected network efficiency and of minimizing interdiction efforts. The algorithm of determining the final optimal defense strategies is presented, which consists of three steps: (1) develop the strategy sets by the assessments of network vulnerability and resilience; (2) analyze the equilibrium through a nested heuristic genetic algorithm; and (3) determine the final optimal defense strategy based on the minimax regret approach. A case study of D-town water supply network demonstrates the practical significance of the proposed approach. Furthermore, the impacts of the incomplete information are analyzed to provide suggestions on the defense strategy making.

[1]  Jose Emmanuel Ramirez-Marquez,et al.  Protecting critical infrastructures against intentional attacks: a two-stage game with incomplete information , 2013 .

[2]  Qiang Qiang,et al.  A network efficiency measure with application to critical infrastructure networks , 2008, J. Glob. Optim..

[3]  Vicki M. Bier,et al.  Balancing Terrorism and Natural Disasters - Defensive Strategy with Endogenous Attacker Effort , 2007, Oper. Res..

[4]  Rae Zimmerman,et al.  Optimal Resource Allocation for Defense of Targets Based on Differing Measures of Attractiveness , 2008, Risk analysis : an official publication of the Society for Risk Analysis.

[5]  Claudio M. Rocco Sanseverino,et al.  Bi and tri-objective optimization in the deterministic network interdiction problem , 2010, Reliab. Eng. Syst. Saf..

[6]  Avi Ostfeld,et al.  Battle of the Water Networks II , 2014 .

[7]  J. Hirshleifer Conflict and rent-seeking success functions: Ratio vs. difference models of relative success , 1989 .

[8]  S. Skaperdas Contest success functions , 1996 .

[9]  Linjun Lu,et al.  Optimal allocation of protective resources in urban rail transit networks against intentional attacks , 2015 .

[10]  Kjell Hausken,et al.  Defending against multiple different attackers , 2011, Eur. J. Oper. Res..

[11]  Gerald G. Brown,et al.  Defending Critical Infrastructure , 2006, Interfaces.

[12]  Kjell Hausken,et al.  Defending and attacking a network of two arcs subject to traffic congestion , 2013, Reliab. Eng. Syst. Saf..

[13]  George E Apostolakis,et al.  A Screening Methodology for the Identification and Ranking of Infrastructure Vulnerabilities Due to Terrorism , 2005, Risk analysis : an official publication of the Society for Risk Analysis.

[14]  Maria Paola Scaparra,et al.  Optimizing dynamic investment decisions for railway systems protection , 2016, Eur. J. Oper. Res..

[15]  David K. Y. Yau,et al.  Markov Game Analysis for Attack-Defense of Power Networks Under Possible Misinformation , 2013, IEEE Transactions on Power Systems.

[16]  C. Zhang,et al.  A two-stage resource allocation model for lifeline systems quick response with vulnerability analysis , 2016, Eur. J. Oper. Res..

[17]  John V. Farr,et al.  Infrastructure Risk Analysis of Municipal Water Distribution System , 2000 .

[18]  R. Alvarez,et al.  Trilevel Optimization in Power Network Defense , 2007, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[19]  James H. Lambert,et al.  Reducing vulnerability of water supply systems to attack , 1998 .

[20]  Jesse R. O'Hanley,et al.  Optimizing system resilience: A facility protection model with recovery time , 2012, Eur. J. Oper. Res..

[21]  Rajesh Gupta,et al.  Noniterative Application of EPANET for Pressure Dependent Modelling Of Water Distribution Systems , 2015, Water Resources Management.

[22]  Hugh R. Medal,et al.  Allocating Protection Resources to Facilities When the Effect of Protection is Uncertain , 2016 .

[23]  Natalia Alguacil,et al.  A trilevel programming approach for electric grid defense planning , 2014, Comput. Oper. Res..

[24]  S. Zhao,et al.  Hybrid Hidden Markov Models for resilience metrics in a dynamic infrastructure system , 2017, Reliab. Eng. Syst. Saf..

[25]  Dulcy M. Abraham,et al.  Allocating security resources to a water supply network , 2007 .

[26]  Mustapha Nourelfath,et al.  Critical supply network protection against intentional attacks: A game-theoretical model , 2013, Reliab. Eng. Syst. Saf..

[27]  Chi Zhang,et al.  Critical infrastructure protection using secrecy - A discrete simultaneous game , 2015, Eur. J. Oper. Res..

[28]  Maria Paola Scaparra,et al.  Analysis of facility protection strategies against an uncertain number of attacks: The stochastic R-interdiction median problem with fortification , 2011, Comput. Oper. Res..

[29]  Jun Zhuang,et al.  European Journal of Operational Research on the Value of Exposure and Secrecy of Defense System: First-mover Advantage vs. Robustness , 2022 .

[30]  Gregory Levitin,et al.  False targets vs. redundancy in homogeneous parallel systems , 2009, Reliab. Eng. Syst. Saf..

[31]  Oguzhan Alagöz,et al.  Modeling secrecy and deception in a multiple-period attacker-defender signaling game , 2010, Eur. J. Oper. Res..

[32]  R. K. Wood,et al.  Bilevel Network Interdiction Models: Formulations and Solutions , 2011 .

[33]  Jun Zhuang,et al.  Robust Allocation of a Defensive Budget Considering an Attacker's Private Information , 2012, Risk analysis : an official publication of the Society for Risk Analysis.

[34]  M. Gümüş,et al.  On the value of terrorist’s private information in a government’s defensive resource allocation problem , 2015 .

[35]  J. Salmeron,et al.  Analysis of electric grid security under terrorist threat , 2004, IEEE Transactions on Power Systems.

[36]  Jr. Louis Anthony Cox,et al.  Game Theory and Risk Analysis , 2009 .

[37]  Juan Carlos,et al.  Vulnerability: a conceptional and methodological review , 2006 .

[38]  Wei Yuan,et al.  Optimal power grid protection through a defender-attacker-defender model , 2014, Reliab. Eng. Syst. Saf..

[39]  Majid Salari,et al.  A bi-level programming model for protection of hierarchical facilities under imminent attacks , 2015, Comput. Oper. Res..