Introduction to simulation input modeling

Biller, Bahar; Gunes, Canan

doi:10.1109/wsc.2010.5679176

Cited by 25 publications

(13 citation statements)

References 32 publications

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“…A challenge limiting the practicality of MCS is the requirement that impact parameters be input as probability distributions (Step 4). Distributions can be derived using a variety of methods depending on the types and amount of data available (Biller and Gunes 2010). As a relatively new type of construction, wind farm projects typically lack the volume of historical data required to derive probability distributions using statistical means.…”

Section: Risk Identificationmentioning

confidence: 99%

Domain-specific risk assessment using integrated simulation: a case study of an onshore wind project

et al. 2022

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Although many quantitative risk assessment models have been proposed in literature, their use in construction practice remain limited due to a lack of domain-specific models, tools, and application examples. This is especially true in wind farm construction, where the state-of-the-art integrated Monte Carlo simulation and critical path method (MCS-CPM) risk assessment approach has yet to be demonstrated. The present case study is the first reported application of the MCS-CPM method for risk assessment in wind farm construction and is the first case study to consider correlations between cost and schedule impacts of risk factors using copulas. MCS-CPM provided reasonable risk assessment results for a wind farm project, and its use in practice is recommended. Aimed at facilitating the practical application of quantitative risk assessment methods, this case study provides a much-needed analytical generalization of MCS-CPM, offering application examples, discussion of expected results, and recommendations to wind farm construction practitioners.

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Section: Risk Identificationmentioning

confidence: 99%

Domain-specific risk assessment using integrated simulation: a case study of an onshore wind project

et al. 2022

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“…For this purpose, triangular distributions were assumed for each of the input variables using the low, middle, and high values from Table 11. In the absence of additional data on the statistical distribution of the variables, Biller and Gunes 52 indicate that this distribution is adequate when variation ranges, such as those provided, are known. The tornado charts (Figures 16–20) show the variation intervals of TC , defined by the 5% and 95% percentiles of the TC distribution, as a function of each of each ones of the input variables, studying their effects over the TC separately.…”

Section: Competitive Analysis Of the Expanded Panama Canalmentioning

confidence: 99%

Competitive position of the expanded Panama Canal in the shipment of liquefied natural gas

Carral

Tarrío-Saavedra

Crespo-Pereira

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part M:

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The appearance of new gas extraction technologies has led to surplus production in the United States (Gulf of Mexico). At the same time, energy consumption in Asia has increased significantly and Japan has introduced important changes to its post-Fukushima energy policies. Taken together, these factors have resulted in an imbalance in production and consumption between the Atlantic and Pacific Maritime Areas. Over the same period of time, the Panama Canal expansion opened, permitting transit by liquefied natural gas vessels for the first time. These developments are reflected in the current order book for liquefied natural gas carriers, which is composed entirely of ships in the new Neopanamax category. The canal transit fees and new propulsion systems for these ships—dual fuel diesel electric and electronically controlled gas injection—significantly impact the price of cargo at the destination. This study conducts a sensitivity analysis of the variables for transportation costs in order to determine the expanded Canal’s competitive position. In addition, the study uses a Monte Carlo simulation to obtain the most representative values for total cost based on factors such as the type of propulsion and fuel as well as the distance traveled. The analysis clearly demonstrates the competitiveness of exporting liquefied natural gas via the Panama Canal from terminals in the Gulf of Mexico and the Caribbean to consumers in Asia, as well as the competitiveness of the canal itself versus alternative routes. With respect to propulsion systems, the study demonstrates the greater competitive advantage of electronically controlled gas injection propulsion.

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“…EasyFit supports all common methods for assessing the goodness of the fit including the goodness-of-fit (GOF) tests such as Kolmogorov-Smirnov, Anderson-Darling and Chi-Squared, and plots such as densityhistogram plots and probability plots. A brief discussion of these methods can be found in Biller and Gunes (2010). The lower statistic values of GOF tests suggest smaller distance between the empirical distribution and the fitted distribution, and thus a better fit.…”

Section: Overview Of Input Analysis and Distribution Fitmentioning

confidence: 99%

Quantitative Analysis of Ambulance Location-allocation and Ambulance State Prediction

Nguyen¹

2015

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Introduction to simulation input modeling

Cited by 25 publications

References 32 publications

Domain-specific risk assessment using integrated simulation: a case study of an onshore wind project

Domain-specific risk assessment using integrated simulation: a case study of an onshore wind project

Competitive position of the expanded Panama Canal in the shipment of liquefied natural gas

Quantitative Analysis of Ambulance Location-allocation and Ambulance State Prediction

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