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

Autor:	Sandro Moscatelli, Libertad Tansini, Omar Viera, Pablo Romero, Jorge Graneri
Rok vydání:	2018
Předmět:	Risk analysis Training set Computer science Simple (abstract algebra) Existential quantification Monte Carlo method Rare events Management Science and Operations Research Algorithm Port of entry
Zdroj:	International Journal of Operational Research. 32:41
ISSN:	1745-7653 1745-7645
DOI:	10.1504/ijor.2018.10012184
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0d0aca6ef41ceeaa66aa0ad1725c1d4a https://doi.org/10.1504/ijor.2018.10012184 Zobrazit plný text záznamu