Article: Monte Carlo mirror algorithm for the port-of-entry inspection problem Journal: International Journal of Operational Research (IJOR) 2018 Vol.32 No.1 pp.41 - 55 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. Inderscience Publishers - linking academia, business and industry through research

Title: Monte Carlo mirror algorithm for the port-of-entry inspection problem

Authors: Jorge Graneri; Sandro Moscatelli; Pablo Romero; Libertad Tansini; Omar Viera

Addresses: Laboratorio de Probabilidad y Estadística, Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, PC 11300, Montevideo, Uruguay ' Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, PC 11300, Montevideo, Uruguay ' Laboratorio de Probabilidad y Estadística, Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, PC 11300, Montevideo, Uruguay; Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, PC 11300, Montevideo, Uruguay ' Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, PC 11300, Montevideo, Uruguay ' Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Julio Herrera y Reissig 565, PC 11300, Montevideo, Uruguay

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.

Keywords: risk analysis; port-of-entry; inspection policy; Monte Carlo.

DOI: 10.1504/IJOR.2018.091201

International Journal of Operational Research, 2018 Vol.32 No.1, pp.41 - 55

Received: 04 Oct 2014
Accepted: 06 Jun 2015
Published online: 16 Apr 2018 *

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Title: Monte Carlo mirror algorithm for the port-of-entry inspection problem

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