COMPARISON OF RISK ANALYSIS METHODOLOGIES IN A GEOSTATISTICAL CONTEXT: MONTE CARLO WITH JOINT PROXY MODELS AND DISCRETIZED LATIN HYPERCUBE

2018 ◽  
Vol 8 (1) ◽  
pp. 23-41 ◽  
Author(s):  
Susana Santos ◽  
Ana Teresa F. S. Gaspar ◽  
Denis J. Schiozer
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Latin Hypercube ◽  
Proxy Models

RISK ANALYSIS OF OFFSHORE TRANSPORTATION ACCIDENT IN ARCTIC WATERS

2021 ◽  
Vol 159 (A3) ◽  
Author(s):  
R Abbassi ◽  
F Khan ◽  
N Khakzad ◽  
B Veitch ◽  
S Ehlers
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Historical Data ◽  
Fault Trees ◽  
Northern Sea Route ◽  
Accident Scenarios ◽  
Oil Tanker ◽  
Harsh Conditions ◽  
Monte Carlo Framework ◽  
Transportation Accident

A methodology for risk analysis applicable to shipping in arctic waters is introduced. This methodology uses the Bowtie relationship to represent an accident causes and consequences. It is further used to quantify the probability of a ship accident and also the related accident consequences during navigation in arctic waters. Detailed fault trees for three possible ship accident scenarios in arctic transits are developed and represented as bowties. Factors related to cold and harsh conditions and their effects on grounding, foundering, and collision are considered as part of this study. To illustrate the application of the methodology, it is applied to a case of an oil-tanker navigating on the Northern Sea Route (NSR). The methodology is implemented in a Markov Chain Monte Carlo framework to assess the uncertainties arisen from historical data and expert judgments involved in the risk analysis.

Risk Analysis by Monte Carlo Simulation over Large Offshore Projects

2021 ◽  
Author(s):  
Agostino Bruzzone ◽  
Kirill Sinelshchikov ◽  
Federico Tarone ◽  
Federica Grosso
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Risk Analysis

The Case for Truly Integrated Cost and Schedule Risk Analysis

2017 ◽  
pp. 76-108
Author(s):  
Colin H. Cropley
Keyword(s):  
Monte Carlo ◽  
Risk Analysis ◽  
Complex Projects ◽  
Schedule Risk ◽  
Project Risks ◽  
Mc Simulation ◽  
Integrated Cost ◽  
Schedule Risk Analysis ◽  
Relationship Of ◽  
Cost Outcomes

Time and cost outcomes of large and complex projects are forecast poorly across all sectors. Over recent years, Monte Carlo (MC) simulation has increasingly been adopted to forecast project time and cost outcomes more realistically. It is recognised that the simultaneous analysis of time and cost impacts makes sense as a modelling objective, due to the well-known relationship of time and money in projects. But most MC practitioners advocate the use of Schedule Risk Analysis (SRA) feeding into Cost Risk Analysis (CRA) because they believe it is too hard to perform Integrated Cost & Schedule Risk Analysis (IRA) realistically. This chapter elaborates an IRA methodology that produces realistic forecasts without relying on questionable assumptions and enables identification and ranking of all sources of cost uncertainty for risk optimisation as part of the process. It also describes an extension of IRA methodology to include assessment of the assets produced by the project as well as the project itself, thus enabling the analysis of business risks as well as project risks.

A Weighted Monte Carlo Simulation Approach to Risk Assessment of Information Security Management System

2015 ◽  
Vol 11 (4) ◽  
pp. 63-78 ◽  
Author(s):  
Seyed Mojtaba Hosseini Bamakan ◽  
Mohammad Dehghanimohammadabadi
Keyword(s):  
Risk Assessment ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Information Security ◽  
Risk Analysis ◽  
Management System ◽  
Security Management ◽  
International Standards ◽  
Information Security Management ◽  
Information Security Management System

In recent decades, information has become a critical asset to various organizations, hence identifying and preventing the loss of information are becoming competitive advantages for firms. Many international standards have been developed to help organizations to maintain their competitiveness by applying risk assessment and information security management system and keep risk level as low as possible. This study aims to propose a new quantitative risk analysis and assessment methodology which is based on AHP and Monte Carlo simulation. In this method, AHP is used to create favorable weights for Confidentiality, Integrity and Availability (CIA) as security characteristic of any information asset. To deal with the uncertain nature of vulnerabilities and threats, Monte Carlo simulation is utilized to handle the stochastic nature of risk assessment by taking into account multiple judges' opinions. The proposed methodology is suitable for organizations that require risk analysis to implement ISO/IEC 27001 standard.

scholarly journals Monte Carlo approach to fuzzy AHP risk analysis in renewable energy construction projects

PLoS ONE ◽  
2019 ◽  
Vol 14 (6) ◽  
pp. e0215943 ◽  
Author(s):  
Luis Serrano-Gomez ◽  
Jose Ignacio Munoz-Hernandez
Keyword(s):  
Monte Carlo ◽  
Renewable Energy ◽  
Risk Analysis ◽  
Construction Projects ◽  
Fuzzy Ahp ◽  
Monte Carlo Approach

Scrambling non-uniform nets

2009 ◽  
Vol 59 (3) ◽  
Author(s):  
Shu Tezuka
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Methods ◽  
Exact Formula ◽  
Sufficient Condition ◽  
Necessary And Sufficient Condition ◽  
Latin Hypercube ◽  
Necessary And Sufficient

AbstractIn this paper, we consider Owen’s scrambling of an (m−1, m, d)-net in base b which consists of d copies of a (0, m, 1)-net in base b, and derive an exact formula for the gain coefficients of these nets. This formula leads us to a necessary and sufficient condition for scrambled (m − 1, m, d)-nets to have smaller variance than simple Monte Carlo methods for the class of L 2 functions on [0, 1]d. Secondly, from the viewpoint of the Latin hypercube scrambling, we compare scrambled non-uniform nets with scrambled uniform nets. An important consequence is that in the case of base two, many more gain coefficients are equal to zero in scrambled (m − 1, m, d)-nets than in scrambled Sobol’ points for practical size of samples and dimensions.

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