TY - JOUR
T1 - Algorithms for optimizing the placement of stationary monitors
AU - Romich, Andrew
AU - Lan, Guanghui
AU - Smith, J. Cole
N1 - Funding Information:
Guanghui (George) Lan obtained his Ph.D. degree in Industrial and Systems Engineering from the Georgia Institute of Technology in August 2009. He then joined the Department of Industrial and Systems Engineering at the University of Florida as an Assistant Professor. His main research interests lie in stochastic optimization, nonlinear programming, simulation-based optimization, and their applications in data analysis. His research has been supported by the National Science Foundation and Office of Naval Research. The academic honors that he has received include the INFORMS Computing Society Student Paper Competition First Place (2008), INFORMS George Nicholson Paper Competition Second Place (2008), Mathematical Optimization Society Tucker Prize Finalist (2012), INFORMS Junior Faculty Interest Group (JFIG) Paper Competition First Place (2012), and the National Science Foundation CAREER Award (2013).
Funding Information:
J. Cole Smith is Professor and Chair of Industrial Engineering at Clem-son University. His research has been supported by the NSF, DARPA, AFOSR, and the ONR, and he has spent one summer as a distinguished visiting professor with the Department of Defense. His research regards mathematical optimization models and algorithms, especially those arising in combinatorial optimization. He has collaborated with colleagues across many different disciplines, including mathematics, ecology, psychology, computer science, and biomedical engineering. His awards include the Young Investigator Award from the ONR, the Hamid K. Elden Outstanding Young Industrial Engineer in Education award, the Operations Research Division Teaching Award, and the best paper award from IIE Transactions in 2007.
Publisher Copyright:
Copyright © 2015 "IIE".
Copyright:
Copyright 2016 Elsevier B.V., All rights reserved.
PY - 2015/6/3
Y1 - 2015/6/3
N2 - This article examines the problem of placing stationary monitors in a continuous space, with the goal of minimizing an adversarys maximum probability of traversing an origin-destination route without being detected. The problem arises, for instance, in defending against the transport of illicit material through some area of interest. In particular, we consider the deployment of monitors whose probability of detecting an intruder is a function of the distance between the monitor and the intruder. Under the assumption that the detection probabilities are mutually independent, a two-stage mixed-integer nonlinear programming formulation is constructed for the problem. An algorithm is provided that optimally locates monitors in a continuous space. Then, this problem is examined for the case where the monitor locations are restricted to two different discretized subsets of continuous space. The analysis provides optimization algorithms for each case and derives bounds on the worst-case optimality gap between the restrictions and the initial (continuous-space) problem. Empirically, it is shown that discretized solutions can be obtained whose worst-case and actual optimality gaps are well within practical limits.
AB - This article examines the problem of placing stationary monitors in a continuous space, with the goal of minimizing an adversarys maximum probability of traversing an origin-destination route without being detected. The problem arises, for instance, in defending against the transport of illicit material through some area of interest. In particular, we consider the deployment of monitors whose probability of detecting an intruder is a function of the distance between the monitor and the intruder. Under the assumption that the detection probabilities are mutually independent, a two-stage mixed-integer nonlinear programming formulation is constructed for the problem. An algorithm is provided that optimally locates monitors in a continuous space. Then, this problem is examined for the case where the monitor locations are restricted to two different discretized subsets of continuous space. The analysis provides optimization algorithms for each case and derives bounds on the worst-case optimality gap between the restrictions and the initial (continuous-space) problem. Empirically, it is shown that discretized solutions can be obtained whose worst-case and actual optimality gaps are well within practical limits.
KW - Network optimization
KW - Nonlinear programming
KW - Shortest-path network interdiction
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U2 - 10.1080/0740817X.2014.953646
DO - 10.1080/0740817X.2014.953646
M3 - Article
AN - SCOPUS:84926421290
SN - 2472-5854
VL - 47
SP - 556
EP - 576
JO - IIE Transactions (Institute of Industrial Engineers)
JF - IIE Transactions (Institute of Industrial Engineers)
IS - 6
ER -