scholarly journals Using of Sensitivity Analysis in Road Transport Pricing

2018 ◽  
Vol 9 (1) ◽  
pp. 32-39 ◽  
Author(s):  
Jindřich Ježek ◽  
Jiří Nožička ◽  
Árpád Török
Keyword(s):  
Sensitivity Analysis ◽  
Road Transport ◽  
Price Formation ◽  
Input Variables ◽  
Passenger Road Transport

Abstract The paper deals with the issue of sensitivity analysis and its possible use in price formation in passenger road transport. The input variables are selected on the model example. Based on the calculation, the sensitivity of the output to these input variables is determined, and the question how these inputs affect the overall result is answered.

scholarly journals A Novel Global Sensitivity Measure Based on Probability Weighted Moments

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 90
Author(s):  
Shufang Song ◽  
Lu Wang
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Small Samples ◽  
Double Loop ◽  
Numerical Estimation ◽  
Global Sensitivity ◽  
Sensitivity Measure ◽  
Probability Weighted Moments ◽  
Input Variables ◽  
Single Set

Global sensitivity analysis (GSA) is a useful tool to evaluate the influence of input variables in the whole distribution range. Variance-based methods and moment-independent methods are widely studied and popular GSA techniques despite their several shortcomings. Since probability weighted moments (PWMs) include more information than classical moments and can be accurately estimated from small samples, a novel global sensitivity measure based on PWMs is proposed. Then, two methods are introduced to estimate the proposed measure, i.e., double-loop-repeated-set numerical estimation and double-loop-single-set numerical estimation. Several numerical and engineering examples are used to show its advantages.

Budget Impact Analysis of Rapid Screening for Staphylococcus aureus Colonization Among Patients Undergoing Elective Surgery in US Hospitals

2008 ◽  
Vol 29 (1) ◽  
pp. 16-24 ◽  
Author(s):  
Gary A. Noskin ◽  
Robert J. Rubin ◽  
Jerome J. Schentag ◽  
Jan Kluytmans ◽  
Edwin C. Hedblom ◽  
...  
Keyword(s):  
Staphylococcus Aureus ◽  
Sensitivity Analysis ◽  
Budget Impact ◽  
Elective Surgery ◽  
Cost Savings ◽  
Nasal Carriage ◽  
Rapid Screening ◽  
Marginal Effect ◽  
Primary Input ◽  
Input Variables

Objective.To evaluate the economic impact of performing rapid testing for Staphylococcus aureus colonization before admission for all inpatients who are scheduled to undergo elective surgery and providing subsequent decolonization therapy for those patients found to be colonized with S. aureus.Methods.A budget impact model that used probabilistic sensitivity analysis to account for the uncertainties in the input variables was developed. Primary input variables included the marginal effect of S. aureus infection on patient outcomes among patients who underwent elective surgery, patient demographic characteristics, the prevalence of nasal carriage of S. aureus, the sensitivity and specificity of the rapid diagnostic test for S. aureus colonization, the efficacy of decolonization therapy for nasal carriage of S. aureus, and cost data. Data sources for the input variables included the 2003 Nationwide Inpatient Sample data and the published literature.Results.In 2003, there were an estimated 7,181,484 patients admitted to US hospitals for elective surgery. Our analysis indicated preadmission testing and subsequent decolonization therapy for patients colonized with S. aureus would have produced a mean annual cost savings to US hospitals of $231,538,400 (95% confidence interval [CI], -$300 million to $1.3 billion). The mean annual number of hospital-days that could have been eliminated was estimated at 364,919 days (95% CI, 67,893-926,983 days), and a mean of 935 in-hospital deaths (95% CI, 88-3,691) could have been avoided per year. Sensitivity analysis indicated a 64.5% probability that there would be cost savings to US hospitals as a result of preadmission testing and subsequent decolonization therapy.Conclusion.The addition of preadmission testing and decolonization therapy to standard care would result in significant cost savings, even after accounting for variations in the model input values.

scholarly journals Prediction Model of Iron Ore Pellet Ambient Strength and Sensitivity Analysis on the Influence Factors

Metals ◽  
2018 ◽  
Vol 8 (8) ◽  
pp. 593 ◽  
Author(s):  
Qiangjian Gao ◽  
Yingyi Zhang ◽  
Xin Jiang ◽  
Haiyan Zheng ◽  
Fengman Shen
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Prediction Model ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Influence Factors ◽  
Principal Component ◽  
Bp Network ◽  
Input Variables ◽  
Artificial Neural Network Ann

The Ambient Compressive Strength (CS) of pellets, influenced by several factors, is regarded as a criterion to assess pellets during metallurgical processes. A prediction model based on Artificial Neural Network (ANN) was proposed in order to provide a reliable and economic control strategy for CS in pellet production and to forecast and control pellet CS. The dimensionality of 19 influence factors of CS was considered and reduced by Principal Component Analysis (PCA). The PCA variables were then used as the input variables for the Back Propagation (BP) neural network, which was upgraded by Genetic Algorithm (GA), with CS as the output variable. After training and testing with production data, the PCA-GA-BP neural network was established. Additionally, the sensitivity analysis of input variables was calculated to obtain a detailed influence on pellet CS. It has been found that prediction accuracy of the PCA-GA-BP network mentioned here is 96.4%, indicating that the ANN network is effective to predict CS in the pelletizing process.

Bayesian and Sensitivity Analysis Approaches to Modelling the Direct Solar Irradiance

Solar Energy ◽  
2005 ◽  
Author(s):  
Philippe Lauret ◽  
Mathieu David ◽  
Eric Fock ◽  
Laetitia Adelard
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Solar Irradiance ◽  
Second Step ◽  
Practical Implementation ◽  
Bayesian Techniques ◽  
Systematic Methodology ◽  
Important Input ◽  
Input Variables ◽  
Selection Of

In this paper, emphasis is put on the design of a neural network to model the direct solar irradiance. Since unfortunately a neural network (NN) is not a statistician in-a-box, building a NN for a particular problem is a non trivial task. As a consequence, we argue that in order to properly model the direct solar irradiance, a systematic methodology must be employed. For this purpose, we propose a two-step approach to building the NN model. The first step deals with a probabilistic interpretation of the NN learning by using Bayesian techniques. The Bayesian approach to modelling offers significant advantages over the classical NN learning process. Among others, one can cite a) automatic complexity control of the NN using all the available data b) selection of the most important input variables. The second step consists in using a new sensitivity analysis-based pruning method in order to infer the optimal NN structure. We show that the combination of the two approaches makes the practical implementation of the Bayesian techniques more reliable.

scholarly journals Use of the correlation between input variables in estimating the risk of feedlot finishing of steers and young steers

2014 ◽  
Vol 86 (2) ◽  
pp. 945-954 ◽  
Author(s):  
PAULO S. PACHECO ◽  
JOÃO RESTLE ◽  
LEONIR L. PASCOAL ◽  
FABIANO N. VAZ ◽  
RICARDO Z. VAZ ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Stochastic Dominance ◽  
Net Present Value ◽  
Latin Hypercube Sampling ◽  
Risk Estimate ◽  
Present Value ◽  
Random Input ◽  
Financial Indicator ◽  
Input Variables ◽  
Diet Intake

The objective of this study was to evaluate the risk of feedlot finishing of steers (22.8 months) and young steers (15.2 months), using or not a correlation between the random input variables (data collected from 2004 to 2010) in the simulation of the Net Present Value (NPV) financial indicator. The animals were fed a diet containing roughage:concentrate ratio of 60:40 for 34 and 143 days, respectively, until they had reached a predetermined slaughter weight of 430 kg. For the NPV simulation, Latin Hypercube sampling was used, with 2000 interactions. The stochastic dominance analysis, test of differences between pairs of curves of cumulative distributions and sensitivity analysis were carried out. The NPV simulation using the correlation resulted in the best option for risk estimate. The confinement of young steers was the alternative of investment most viable than confinement of steers (NPV ≥ 0 of 80.4 vs. 62.3% in the simulation with correlation, respectively). Sensitivity analysis determined the following items had the greatest impact on the estimate of NPV: prices of fat and thin cattle, initial and final weights, diet costs, minimum rate of attractiveness and diet intake.

scholarly journals Global Sensitivity Analysis to Assess Salt Precipitation for CO2 Geological Storage in Deep Saline Aquifers

Geofluids ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Yuan Wang ◽  
Jie Ren ◽  
Shaobin Hu ◽  
Di Feng
Keyword(s):  
Sensitivity Analysis ◽  
Global Sensitivity Analysis ◽  
Injection Rate ◽  
Injection Well ◽  
Saline Aquifers ◽  
Salt Precipitation ◽  
Empirical Parameter ◽  
Global Sensitivity ◽  
Morris Method ◽  
Input Variables

Salt precipitation is generated near the injection well when dry supercritical carbon dioxide (scCO2) is injected into saline aquifers, and it can seriously impair the CO2 injectivity of the well. We used solid saturation (Ss) to map CO2 injectivity. Ss was used as the response variable for the sensitivity analysis, and the input variables included the CO2 injection rate (QCO2), salinity of the aquifer (XNaCl), empirical parameter m, air entry pressure (P0), maximum capillary pressure (Pmax), and liquid residual saturation (Splr and Sclr). Global sensitivity analysis methods, namely, the Morris method and Sobol method, were used. A significant increase in Ss was observed near the injection well, and the results of the two methods were similar: XNaCl had the greatest effect on Ss; the effect of P0 and Pmax on Ss was negligible. On the other hand, with these two methods, QCO2 had various effects on Ss: QCO2 had a large effect on Ss in the Morris method, but it had little effect on Ss in the Sobol method. We also found that a low QCO2 had a profound effect on Ss but that a high QCO2 had almost no effect on the Ss value.

Long lead forecasting of spring peak runoff using Mamdani-type fuzzy logic systems at Hay River, NWT

2015 ◽  
Vol 42 (9) ◽  
pp. 665-674 ◽  
Author(s):  
L. Zhao ◽  
F.E. Hicks ◽  
A. Robinson Fayek
Keyword(s):  
Sensitivity Analysis ◽  
Fuzzy Logic ◽  
Historical Data ◽  
System Development ◽  
System Component ◽  
Fuzzy Logic Systems ◽  
Lead System ◽  
Input Variables ◽  
Peak Runoff ◽  
Error Index

In northern riverside communities, breakup ice jam flooding is an annual threat to properties and human lives. In this study, the peak snowmelt runoff during breakup was assessed as an indicator of breakup flood severity. Due to the sparse network of hydrometeorolocial data in remote northern regions, a Mamdani-type fuzzy logic system (FLS) was developed and tested with the limited historical data. Three input variables were defined from the precipitation, air temperature and daily water level data. All of these variables are known ∼3 to 4 weeks before breakup enabling a long lead-time forecast. The process of system development is demonstrated by a case study of the Town of Hay River, NWT Canada. A series of experiments were designed to select the best system configuration, which also provided a way to conduct a sensitivity analysis for different choices in each system component. It was found that the system shows very good performance on the historical data using the qualitative error index. The results of the sensitivity analysis suggest the system performance is dependent on the choices of fuzzy logic inference operators and defuzzification method. As a long lead system, the short-term meteorological factors that would affect the system output were analyzed and the possible error range was assessed. Preliminary model validation, based on three years of testing, shows promising performance.

scholarly journals Global Sensitivity Analysis of Fuzzy Distribution Parameter on Failure Probability and Its Single-Loop Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Lei Cheng ◽  
Zhenzhou Lu ◽  
Luyi Li
Keyword(s):  
Sensitivity Analysis ◽  
Failure Probability ◽  
Global Sensitivity Analysis ◽  
Computational Cost ◽  
Single Loop ◽  
Global Sensitivity ◽  
Sensitivity Indices ◽  
Distribution Parameters ◽  
Input Variables ◽  
Sampling Probability

An extending Borgonovo’s global sensitivity analysis is proposed to measure the influence of fuzzy distribution parameters on fuzzy failure probability by averaging the shift between the membership functions (MFs) of unconditional and conditional failure probability. The presented global sensitivity indices can reasonably reflect the influence of fuzzy-valued distribution parameters on the character of the failure probability, whereas solving the MFs of unconditional and conditional failure probability is time-consuming due to the involved multiple-loop sampling and optimization operators. To overcome the large computational cost, a single-loop simulation (SLS) is introduced to estimate the global sensitivity indices. By establishing a sampling probability density, only a set of samples of input variables are essential to evaluate the MFs of unconditional and conditional failure probability in the presented SLS method. Significance of the global sensitivity indices can be verified and demonstrated through several numerical and engineering examples.

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