Adaptive policies for perimeter surveillance problems

Grant, James A. and Leslie, David S. and Glazebrook, Kevin and Szechtman, Roberto and Letchford, Adam (2020) Adaptive policies for perimeter surveillance problems. European Journal of Operational Research, 283 (1). pp. 265-278. ISSN 0377-2217

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Abstract

We consider the problem of sequentially choosing observation regions along a line, with an aim of maximising the detection of events of interest. Such a problem may arise when monitoring the movements of endangered or migratory species, detecting crossings of a border, policing activities at sea, and in many other settings. In each case, the key operational challenge is to learn an allocation of surveillance resources which maximises successful detection of events of interest. We present a combinatorial multi-armed bandit model with Poisson rewards and a novel filtered feedback mechanism - arising from the failure to detect certain intrusions - where reward distributions are dependent on the actions selected. Our solution method is an upper confidence bound approach and we derive upper and lower bounds on its expected performance. We prove that the gap between these bounds is of constant order, and demonstrate empirically that our approach is more reliable in simulated problems than competing algorithms.

Item Type:
Journal Article
Journal or Publication Title:
European Journal of Operational Research
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1800/1802
Subjects:
ID Code:
128151
Deposited By:
Deposited On:
12 Oct 2018 14:08
Refereed?:
Yes
Published?:
Published
Last Modified:
30 Jun 2020 14:50