Risk models based on uncertainty quantification for illicit traffic time series in customs context

Hammadi, Lamia; Cursi, Eduardo Souza de; Barbu, Vlad Ștefan; Ouahman, Abdellah Ait

doi:10.1504/ijstl.2022.120672

Cited by 3 publications

(3 citation statements)

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“…Moment matching looks for an element 𝑷 𝒏 𝒁 such that a finite set of its moments coincides with those of 𝒁 . In practice, a sample is used to evaluate the moments [27,36,41,42].…”

Section: Approach By Moment Matching (Mm)mentioning

confidence: 99%

“…Uncertainty quantification has gained massive attention from many researchers in different fields, namely, applied mathematics, engineering, physics, management and economics. Uncertainty quantification in terms of state estimation of discrete/continuous time systems is a fundamental problem in engineering sciences [27]. UQ can be defined as that process of quantifying all sources of uncertainties linked to the systems and testing their influence on the accuracy of the results in order to: (i) keep their desired behavior, (ii) avoid undesired states, faults and damages, (iii) to furnish a realistic prognosis in the future, and so on.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty Quantification for Epidemic Risk Management: Case of SARS-CoV-2 in Morocco

Hammadi

Raillani

Ndiaye

et al. 2023

IJERPH

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In this paper, we propose a new method for epidemic risk modelling and prediction, based on uncertainty quantification (UQ) approaches. In UQ, we consider the state variables as members of a convenient separable Hilbert space, and we look for their representation in finite dimensional subspaces generated by truncations of a suitable Hilbert basis. The coefficients of the finite expansion can be determined by approaches established in the literature, adapted to the determination of the probability distribution of epidemic risk variables. Here, we consider two approaches: collocation (COL) and moment matching (MM). Both are applied to the case of SARS-CoV-2 in Morocco, as an epidemic risk example. For all the epidemic risk indicators computed in this study (number of detections, number of deaths, number of new cases, predictions and human impact probabilities), the proposed models were able to estimate the values of the state variables with precision, i.e., with very low root mean square errors (RMSE) between predicted values and observed ones. Finally, the proposed approaches are used to generate a decision-making tool for future epidemic risk management, or, more generally, a quantitative disaster management approach in the humanitarian supply chain.

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Section: Approach By Moment Matching (Mm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Uncertainty Quantification for Epidemic Risk Management: Case of SARS-CoV-2 in Morocco

Hammadi

Raillani

Ndiaye

et al. 2023

IJERPH

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“…UQ tools have been applied in numerous studies to evaluate and mitigate risks effectively. In [9,10], the authors proposed a model that utilizes uncertainty quantification to address risks associated with the customs supply chain. This model employs the moment matching method and takes into account the seasonality of illicit traffics at five different sites in Morocco.…”

Section: Introductionmentioning

confidence: 99%

Modeling Uncertain Travel Times in Distribution Logistics

Ait Mamoun,

Hammadi,

El Ballouti

et al. 2023

Applied Sciences

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Uncertainty quantification is a critical aspect of distribution logistics, particularly unpredictable travel times caused by traffic congestion and varying transportation conditions. This paper explores the modeling of uncertainty in dealing with travel times in the context of distribution logistics using the collocation method. First, we employ Monte Carlo simulations to assess the efficacy of the collocation method in modeling the variability and uncertainty associated with travel times. Second, we implement the collocation method in Casablanca, Morocco, a city renowned for its extensive distribution logistics operations and its dynamic traffic. Four distinct scenarios are considered: morning peak, inter-peak, evening peak, and off-peak periods. Our study explores two scenarios: one with recurrent congestion, representing typical daily conditions, and the other with unpredictable uncertainties in travel times, accounting for unexpected events that may occur during a distribution day. Our research findings enhance our understanding of the probabilistic nature of travel times in distribution logistics. This knowledge provides valuable insights applicable to both routine situations with recurrent congestion and non-recurrent congestion. The results’ findings contribute to a better understanding of the probabilistic nature of travel times in distribution logistics, offering valuable insights for optimizing route planning and scheduling.

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