Abstract
A naive exhaustive manual inspection of port-of-entry is the most secure inspection policy. However, the number of within containers allows only to check a limited number of containers each day. The aim of this paper is to offer an automatic, simple and intuitive algorithm to select which containers should be inspected, following a given training set of classifications as close as possible. We prove that there exists an optimal deterministic inspection policy for the classification problem, called mirror solution. Inspired by the strength of Monte Carlo-based methods for simulation of rare events, we add randomisation to the mirror solution. We first show that the randomised mirror solution is useful in practice and computationally efficient, since it depends linearly on the size of the training set, for a given number of sensors and risk levels. Finally, we present the results of the proposed port-of-entry inspection policy in a real-life scenario.
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