Network interdiction to minimize the maximum probability of evasion with synergy between applied resources

In this paper, we model and solve the network interdiction problem of minimizing the maximum probability of evasion by an entity traversing a network from a given source to a designated terminus, while incorporating novel forms of superadditive synergy between resources applied to arcs in the network. Inspired primarily by operations to coordinate Iraqi and U.S. security forces seeking to interdict an evader attempting to avoid detection while transiting part of the nearly rectilinear street network in East Baghdad, this study motivates and examines either linear or concave (nonlinear) synergy relationships between the applied resources within our formulations. We also propose an alternative model for sequential overt and covert deployment of subsets of interdiction resources, and conduct theoretical as well as empirical comparative analyses between models for purely overt (with or without synergy) and composite overt-covert strategies to provide insights into absolute and relative threshold criteria for recommended resource utilization. Our empirical results confirm the value of tactical patience regarding decisions on the covert utilization of resources for network interdiction. Furthermore, considering non-integral and integral resource allocations, we identify (theoretically and empirically) parametric characteristics of instances that exhibit the relative worth of employing partially covert operations. Under the relatively more practical scenario involving integral resource allocations, we demonstrate that the composite overt-covert strategy of deploying resources has a greater potential to improve over a purely overt resource deployment strategy, both with and without synergy, particularly when costs are positively correlated, resources are plentiful, and a sufficiently high ratio of covert to overt resources exists. Moreover, should an interdictor be able to ascertain an optimal evader path, the potential and magnitude of this relative improvement for the overt-covert resource allocation strategy is significantly greater.

[1]  D. R. Fulkerson,et al.  Maximal Flow Through a Network , 1956 .

[2]  Jyrki Wallenius,et al.  Bibliometric Analysis of Multiple Criteria Decision Making/Multiattribute Utility Theory , 2008, MCDM.

[3]  Hanif D. Sherali,et al.  A Dynamic Network Interdiction Problem , 2010, Informatica.

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

[5]  Andrew J. Schaefer,et al.  SPAR: stochastic programming with adversarial recourse , 2006, Oper. Res. Lett..

[6]  Anna Nagurney,et al.  Environmental and Cost Synergy in Supply Chain Network Integration in Mergers and Acquisitions , 2008, MCDM.

[7]  Mokhtar S. Bazaraa,et al.  Nonlinear Programming: Theory and Algorithms , 1993 .

[8]  Omur Unsal,et al.  Two-Person Zero-Sum Network-Interdiction Game with Multiple Inspector Types , 2010 .

[9]  J. C. Smith,et al.  Algorithms for discrete and continuous multicommodity flow network interdiction problems , 2007 .

[10]  Delbert Ray Fulkerson,et al.  Maximizing the minimum source-sink path subject to a budget constraint , 1977, Math. Program..

[11]  Churlzu Lim,et al.  Algorithms for Network Interdiction and Fortification Games , 2008 .

[12]  Matthew D. Bailey,et al.  Shortest path network interdiction with asymmetric information , 2008 .

[13]  R. Kevin Wood,et al.  Deterministic network interdiction , 1993 .

[14]  William M. Rogers,et al.  Modelling Synergy using Manifest Categorical Variables , 1998 .

[15]  David L. Woodruff,et al.  A decomposition algorithm applied to planning the interdiction of stochastic networks , 2005 .

[16]  Kjell Hausken,et al.  Strategic defense and attack of series systems when agents move sequentially , 2011 .

[17]  D. R. Fulkerson,et al.  A Simple Algorithm for Finding Maximal Network Flows and an Application to the Hitchcock Problem , 1957, Canadian Journal of Mathematics.

[18]  M. Nehme Two-person games for stochastic network interdiction : models, methods, and complexities , 2009 .

[19]  B. Golden A problem in network interdiction , 1978 .

[20]  David P. Morton,et al.  A Stochastic Program for Interdicting Smuggled Nuclear Material , 2003 .

[21]  Scott Shorey Brown,et al.  Optimal Search for a Moving Target in Discrete Time and Space , 1980, Oper. Res..

[22]  Kelly James Cormican Computational Methods for Deterministic and Stochastic Network Interdiction Problems. , 1995 .

[23]  E. Meijers Polycentric Urban Regions and the Quest for Synergy: Is a Network of Cities More than the Sum of the Parts? , 2005 .

[24]  David L. Woodruff,et al.  Interdicting Stochastic Networks with Binary Interdiction Effort , 2003 .

[25]  Hanif D. Sherali,et al.  Equitable apportionment of railcars within a pooling agreement for shipping automobiles , 2011 .

[26]  David L. Woodruff,et al.  Network Interdiction and Stochastic Integer Programming , 2013 .

[27]  Alan Washburn,et al.  Two-Person Zero-Sum Games for Network Interdiction , 1995, Oper. Res..

[28]  Gerald G. Brown,et al.  Interdicting a Nuclear-Weapons Project , 2009, Oper. Res..

[29]  David P. Morton,et al.  Models for nuclear smuggling interdiction , 2007 .

[30]  F. Dattilio,et al.  Groups: Theory and Experience , 1986 .

[31]  David P. Morton,et al.  Stochastic Network Interdiction , 1998, Oper. Res..

[32]  Johannes O. Royset,et al.  Solving the Bi-Objective Maximum-Flow Network-Interdiction Problem , 2007, INFORMS J. Comput..

[33]  Stephan Dempe,et al.  Foundations of Bilevel Programming , 2002 .

[34]  R. Kevin Wood,et al.  Shortest‐path network interdiction , 2002, Networks.

[35]  B. Lunday Resource Allocation on Networks: Nested Event Tree Optimization, Network Interdiction, and Game Theoretic Methods , 2010 .

[36]  B. O. Koopman,et al.  Search and its Optimization , 1979 .

[37]  P. Pardalos,et al.  Pareto optimality, game theory and equilibria , 2008 .