Probabilistic Simulation Method Research Articles

Methodological Guidelines and Publications of Benefitrisk Assessment for Health Technology Assessment: A Scoping Review

Introduction Benefit-risk assessment (BRA) is an important component in decision-making throughout the lifecycle of a health technology. The BRA of comparative technologies was usually carried out informally, without the use of a systematized process, which can lead to inappropriate decisions. In the last two decades, efforts have been observed to use more structured, objective and transparent approaches, aiming at better communication and decision-making by stakeholders. In this sense, some frameworks have been proposed to guide BRA, and are currently available. Therefore, we conducted a scoping review to: (1) map the available methodological guidelines and documents for conducting and reporting BRA in the different phases of the lifecycle of health technologies; and (2) identify methodological guidelines on methods for BRA, which could be used as the basis for the development of a BRA guideline for the Brazilian Health Technology Assessment (HTA) context. Methods Our searches were conducted between Oct 2022 to Mar 2023 in three main sources: (1) electronic databases (EMBASE and MEDLINE); (2) grey literature (48 HTA and regulatory organizations); and (3) manual search and contacting experts in the field. We included methodological guidelines or publications presenting methods for conducting or reporting BRA of any types of health technologies developed in any context of the health technology’s lifecycle. Selection process and data charting were conducted by pairs of independent reviewers. We provided a narrative synthesis of the findings. Results From the 83 eligible documents, only six stated the HTA context, 30 were produced in the regulatory setting and 35 documents involved guidance for BRA throughout the health technology’s lifecycle. Among the 129 methodological approaches for BRA identified, the most frequently cited descriptive frameworks were the Problem, Objectives, Alternatives, Consequences, Trade-offs, Uncertainty, Risk, and Linked decisions (PrOACT-URL) and the Benefit Risk Action Team (BRAT), reported in 29 (34.9%) of the documents. Multicriteria Decision Analysis was the most common quantitative framework, cited in 52 (62.7%) of the documents. Other frequent quantitative frameworks cited were Markov Decision Process (n=18; 21.7%) and Decision Tree (n=16; 19.3%). We also identified the most cited metric indices which could be used to measure benefits and risks: numbers needed to harm (n=38; 45.8%) and to treat (n=37; 44.6%) as threshold indices, quality-adjusted life-year (n=37; 44.6%) as health index, and incremental net health benefit (n=32; 38.6%) as trade-off index. The documents also report probabilistic simulation method (n=32; 38.6%) and indirect treatment comparison (n=18; 21.7%) as the most common estimation techniques, while discrete choice experiment and conjoint analysis, mentioned in 26 (31.3%) documents, were the most mentioned utility survey techniques to elicit preferences for quantitative BRA. Discussion and conclusion The efforts for using formal structure approaches for BRA in HTA and at population level have been more modest than in the regulatory setting. We will test and explore the potential of the two most cited descriptive and quantitative frameworks in case studies in the context of HTA to evaluate their performance. The case studies using the frameworks identified in this review will support the elaboration of the Brazilian guideline of BRA for HTA.

A Long-Term Power Supply Risk Evaluation Method for China Regional Power System Based on Probabilistic Production Simulation

To qualify the risk of extreme weather events for power supply security during the long-term power system transformation process, this paper proposes a risk probability evaluation method based on probabilistic production simulation. Firstly, the internal relationship of extreme weather intensity and duration is depicted using the copula function, and the influences of extreme weather on power security are described using the guaranteed power output ability coefficient, which can provide the extreme scenario basis for probabilistic production simulation. Then, a probabilistic production simulation method is proposed, which includes a typical-year scenario and extreme weather events. Meanwhile, an index system is proposed to qualify the power security level, which applies the loss of load expectation (LOLE) and time of loss of load expectation (TOLE) under different scenarios and other indices to reveal the long-term power security trend. Finally, the long-term power supply risks for the Yunnan provincial power system are analyzed using the proposed method, validating that the proposed method is capable of characterizing the influences of extreme weather on power security. The security level of different long-term power transformation schemes is evaluated.

A probabilistic simulation method for sensitivity analysis of input epistemic uncertainties on failure probability

A probabilistic simulation method for sensitivity analysis of input epistemic uncertainties on failure probability

Investigation of an automotive subsystem submitted to vibration loads considering scatter of influence parameters

Modern vehicles contain numerous subsystems for comfort, safety, electrification and autonomous driving functions that are mounted to the body-in-white structure. These subsystems are submitted to stochastic vibrational loads induced mainly by the road unevenness. The vibration fatigue behaviour of the subsystems and their connection to the body-in-white structure depend on a great number of influence parameters and associated uncertain scatter bands that are usually unknown and difficult to consider in the design process. This study investigates the oscillation behaviour and fatigue life of a current car component excited by vi-brational loads focusing on scatter of relevant influence parameters (mass, stiffness) on a pronounced statistical sample basis. An experimental modal analysis is used to determine eigenmodes and damping characteristics for the three variants: nominal value, increased mass and reduced stiffness. As a further experimental investigation vibration fatigue tests are performed. A probabilistic approach using a simulation tool chain with the aid of commercial computer aided engineering (CAE) programs is proposed to consider scatter effects on the fatigue life of the component submitted to vibration loads and its connection to the body-in-white structure. The simulation fidelity when using uncertain broader scatter bounds is compared to simulation fidelity using both measured data and modal analysis output. Furthermore, correlations between the vibration behaviour and the fatigue of the component are derived. Finally, the hardware fatigue tests results are compared to the simulation results. A very good agreement for eigen-modes, frequency response functions and fatigue lifetime was observed between simulation model and experimental findings. A significant accuracy improvement of the fatigue life simulation using the introduced probabilistic simulation method considering measured scatter bounds for uncertain input parameters is presented.

Application of the Monte Carlo Method in Modeling and Simulation of Service Queuing Systems at PT. Pos Indonesia Persero Binjai

Queues are events that we often encounter in various places that provide services to the public, one of which is the Post Office. Service to customer satisfaction is a very important thing, so that improving the quality of customer service must always be done. A good queuing system will affect consumer behavior and satisfaction. PT. Pos Indonesia Persero Binjai is a service company where the purpose of PT. Pos Indonesia (Persero) itself is customer satisfaction oriented. The problems that occurred at PT. Pos Indonesia Persero Binjai caused long queues to build up in queues. To overcome these problems, it is necessary to improve the system such as applying the queuing model application by applying the queuing model method and Monte Carlo simulation. Monte Carlo is a probabilistic simulation method that generates random input to mimic the existing conditions of a problem by estimating the same distribution and in accordance with reality. So that with the improvement of the system, the application of the queuing model can be applied, directing customers to take queue numbers, and monitoring the queue card arrangement, adding tellers to serve customers, enlarging the waiting room so that customers are comfortable waiting and also determine the characteristics and performance measures of the queuing system in part of the payment counter and delivery of goods that will help PT. Pos Indonesia Binjai Company.

Fluctuation characteristics of probability density function and first-passage dynamic reliability of slope systems under seismic excitations

Dynamic reliability based on the full time-history domain analysis is an important result of slope dynamic analysis when random factors are considered and also a useful tool to consider the strong coupling effect of seismic ground motions and nonlinear characteristics of rock and soil parameters. However, the effect of failure time associated with the first passage event in the time history process is seldom considered. To effectively evaluate earthquake-induced geological disasters, a probabilistic simulation method of slope stochastic dynamic reliability considering the randomness of seismic ground motions and the failure time is proposed. Clough and Penzien power spectrum model is used to simulate the non-stationary and random physical properties of seismic ground motions. Stochastic dynamic reliability analysis based on a coupling method of finite element method and limit equilibrium method (FEM-LEM) is performed considering the triggering mechanism of slope failure, integrated with Monte Carlo Simulation (MCS) and first-passage theory to obtain probability density functions (PDFs) of factor of safety (FOS) and displacement indicators, and their fluctuation characteristics with time and different peak ground acceleration (PGA) levels. The results showed that dynamic reliability of slope in the whole seismic history may be overestimated when first passage event is not considered in the evaluation of slope nonlinear dynamic time-history stability. PDFs of FOS had a significantly non-Gaussian, and even skewed distribution during slope nonlinear failure process. Normal distribution hypothesis may be unreasonable when calculating dynamic reliability of FOS index and should be used carefully for slope system under strong seismic loading. Finally, slope stochastic dynamic reliability time-history curves based on first-passage event was obtained within safety domain to provide new perspective for the performance-based seismic design of slope engineering under different seismic fortification levels and needs.

Probabilistic numerical simulation for predicting spherulitic morphology from calorimetric crystallization conversion curves: An isothermal case

The present work introduces a novel method to estimate the morphology of semicrystalline polymers, namely the average spherulite size, size distribution, and nucleus density based on experimental crystallization conversion curves recorded by differential scanning calorimetry (DSC). A fast and accurate probabilistic numerical simulation method describing random nuclei formation and subsequent growth is introduced to handle the crystallization process of polymers. The developed method was used in a curve-fitting procedure with experimental crystallization curves to obtain structural parameters of neat and nucleated isotactic polypropylene. The simulated morphology was used with success to predict haze of injection-molded samples, proving the reliability and applicability of our new approach. The method presented here can be a useful technique to construct the spherulitic morphology based on crystallization conversion curves and predict properties dependent on the spherulite size, size distribution, and nucleus density, even when microscopic measurement cannot be carried out.

Risk assessment of multireservoir joint flood control system under multiple uncertainties

AbstractMultireservoir joint flood control operation is an important nonstructural measure for flood control in some basins. Owing to the existence of uncertainties in flood control operation, various degrees of risk can be shown in flood control operation systems. Examining these uncertainties and the resulting risk of failure in multireservoir operation systems is extremely beneficial and indispensable for basin flood mitigation. In this study, different stochastic simulation methods were employed to generate streamflow series at multiple stations and other uncertainties such as the flood forecast errors, discharge capacity, and reservoir storage. Then, the Monte Carlo framework was established based on the simulation sequence to estimate the risk of a failure of each flood control unit in the flood control system. Furthermore, a comprehensive risk assessment indicator was proposed to consider the failure probability and failure consequences at the same time. The Xiluodu‐Xiangjiaba‐Three Gorges cascade reservoirs were selected as a case study. Results show that the applied probabilistic simulation methods could simulate various uncertainties in a multireservoir flood control system effectively, and the proposed risk analysis method could comprehensively evaluate the risks of reservoir overtopping, flood flows exceeding a standard, and flood diversion from the perspective of overall watershed security.

Resilient Scheduling as a Response to Uncertainty in Construction Projects

Complex construction projects are developed in a dynamic environment, where uncertainty conditions have a great potential to affect project deliverables. In an attempt to efficiently deal with the negative impacts of uncertainty, resilient baseline schedules are produced to improve the probability of reaching project goals, such as respecting the due date and reaching the expected profit. Prior to introducing the resilient scheduling procedure, a taxonomy model was built to account for uncertainty sources in construction projects. Thence, a multi-objective optimization model is presented to manage the impact of uncertainty. This approach can be described as a complex trade-off analysis between three important features of a construction project: duration, stability, and profit. The result of the suggested procedure is presented in a form of a resilient baseline schedule, so the ability of a schedule to absorb uncertain perturbations is improved. The proposed optimization problem is illustrated on the example project network, along which the probabilistic simulation method was used to validate the results of the scheduling process in uncertain conditions. The proposed resilient scheduling approach leads to more accurate forecasting, so the project planning calculations are accepted with increased confidence levels.

MPLP-Based Fast Power System Reliability Evaluation Using Transmission Line Status Dictionary

Composite power system reliability evaluation is computationally time-consuming because the optimal power flow (OPF) with the least load curtailment is calculated for a large number of samples. Most current studies of probabilistic simulation methods focus on sampling techniques to improve the sampling efficiency and decrease the calculations of the OPF. This paper proposes a fast reliability evaluation method that accelerates the calculation of the OPF using multi-parametric linear programming (MPLP). In this paper, the sampled transmission line statuses are considered by the transmission line status dictionary (TLSD). We match the line status of each sample with the scenarios in the TLSD. For matching samples, the generation status and the sampled load are treated as MPLP parameters of the DC-OPF evaluation model. A dynamic learning algorithm is applied to solve the MPLP problem. Case studies are conducted on both IEEE Reliability Test Systems and a provincial power system in China. The results show that the proposed method improves the evaluation efficiency by 23–30 times compared with the normal DC-OPF-based model.

Probabilistic production simulation of a wind/photovoltaic/energy storage hybrid power system based on sequence operation theory

Probabilistic production simulation is an important tool used in conventional power systems to calculate generated energy and evaluate reliability. With the continuous expansion of the renewable energy plants, new characteristics such as intermittency and volatility of power are widely discussed, and it is necessary to study the application of probabilistic production simulation for renewable energy power generation. This study proposes a probabilistic production simulation method based on sequence operation theory (SOT) to simulate the operation of a wind/photovoltaic/energy storage power system. Both the uncertainty of renewable resources and the outage of wind turbines are considered in this study. Considering the complementary property of the renewable energy, pattern clustering is used to analyse the meteorological conditions and to assist in the probabilistic production simulation. Moreover, the output model of the energy storage device is developed using the Monte Carlo method and controlled using a smoothing strategy of the energy storage device. Ultimately, the simulation example shows the feasibility and the higher efficiency of the algorithm compared with Monte Carlo method and a production simulation method based on equivalent energy function.

A Non-Sequential Probabilistic Production Simulation Method for Wind Energy Curtailment Evaluation Considering the Seasonal Heat Supply Constraints

High wind energy curtailment during winter and early spring in north China has drawn widespread concern in recent years, which, at source, results from the incongruous planning between wind power development and other generation expansion. However, the scenarios obtained by traditional planning method without considering the seasonal heat supply constraints are far from the actual operation stage. More difficulty lies in the lack of sequential data of wind power and load demand in planning stage. A nonsequential probabilistic production simulation model is proposed to evaluate the wind energy curtailment during the heat supply period, which only needs the input data of the cumulative probability function (CDF) and/or probability density function (PDF) of heat demand, electricity demand, and wind power. Then an algorithm based on the equivalent joint energy function and minimal power-heat ratio method is applied to solve the model. The simulations have been performed based on the actual data of a large wind power zone in north China. Comparing with the previous sequential simulation method, the precision of the proposed method is satisfactory and the calculation time is dramatically reduced.

Cost–benefit analysis of pumped hydro storage using improved probabilistic production simulation method

This study presents an improved probabilistic production simulation method to facilitate the cost–benefit analysis of pumped hydro storage. To capture the coherent feature of power system operation, the traditional form of probabilistic production simulation is strengthened under a three-fold computational framework. First, the chronological sequences of renewable energy generation and imbalanced power are produced according to a set of feature metrics. Then, the imbalanced power series are modified through the long-term scheduling of pumped hydro storage. At last, the regular steps of probabilistic production simulation, including construction of equivalent energy function and convolution operation, are implemented to derive the operational cost and reliability metrics of power system. Case studies on IEEE-RTS79 system demonstrate the effectiveness of the proposed simulation method, which enables the quantitative assessment for cost-benefits of pumped hydro storage towards a high-penetration renewable energy integrated power system.

Cadmium in Chinese Postharvest Peanuts and Dietary Exposure Assessment in Associated Population.

Cadmium (Cd) in 8698 peanut samples collected from China in 2009-2014 was studied to evaluate its contamination level, distribution, and health risk. The average Cd concentration was 0.1684 mg kg-1; the range of 2.5-97.5% was 0.0191-0.4762 mg kg-1, indicating the cadmium-contaminated peanut level was even lower. Some peanut strains for which protein contents had a significant correlation (Pearson correlation coefficient r = 0.86**) with the Cd concentration level should be of concern. Under the same soil Cd background, the difference in Cd contents in different peanut varieties is extremely significant. For example, the Cd concentration of Silihong is about 0.4522 mg kg-1, being 7 times higher than that of Zhonghua 6. According to the exposure assessment using the probabilistic simulation method, the target hazard quotients (THQs) of all groups should be below 1. The THQ range in this study was from 0.0035 to 0.0202, suggesting that there were no potential noncinogenic effects in any group.

Probabilistic evaluation of available load supply capability of distribution networks as an index for wind turbines allocation

This study deals with placement of wind turbines in distribution networks for maximising the probabilistic index of load supply capability (LSC). To calculate this index, one of the most accurate and efficient probabilistic simulation methods known as Latin hypercube sampling is used. Variations of load and output power of wind turbines are considered in the probabilistic evaluation of LSC. Moreover, the constraints of maximum voltage drop and current carrying capacity of conductors are considered as the limiting factors of the network LSC. Since there is a correlation between the network load and the output power of wind turbines, considering this correlation in simulations will push the results closer to reality. Therefore, the Cholesky decomposition approach is used to take into account the correlation between the input variables of the problem. Casestudiesare carried out on the 33-bus IEEE distribution network. The results of case studies demonstrate the effectiveness of the proposed methodology.

The impacts of changing flight demands and throughput performance on airport delays through the Great Recession

Several significant events between 2007 and 2009 impacted flight demands and the abilities of the three major New York area airports to handle demand. This paper assesses the results of applying a probabilistic simulation method – which isolates the individual contributions of changes in flight demand and changes in airport throughput performance to changes in flight delays – to diagnose how these different events may have caused operational changes at these airports, and in turn, how the results may be used to inform policies for appropriate countermeasures. The analysis revealed two key observations. Firstly, certain patterns in throughput performance shifts caused the most significant delays, and were more likely to have been caused by controller staffing issues rather than caps. Secondly, relatively constant average delays from one year to the next may result from significant demand drops accompanied by large throughput performance degradations at an airport. This suggests that not only operational limitations on capacity encourage airlines to reduce schedules, but that changed demands can also impact throughput performance. Overall, the analysis indicates that caps may not have provided their fully intended delay benefits. Although they successfully reduced overall flight demands at LGA and JFK, they also directly limited throughput performance at critical times, in turn limiting delay benefits. In addition, demands at the busiest times of the day appear to be relatively inelastic to these operational limitations, insofar as demand profiles at EWR and JFK remained “peaky” in 2008 and 2009. Also, the recession was largely responsible for reducing demands at the airports in 2009, but the delay benefits of this were dampened by a corresponding throughput performance degradation. Based on the above observations, a more direct demand management policy combined with policies that focus on maintaining high staffing capabilities at critical times of the day may be considered, to reduce the likelihood of major queue formation on days that do experience sustained demands. The results also suggest that a more flexible caps system, particularly during times of heavy queues, could be explored. Although airport practitioners have keen understandings of how their airports operate, without the support of quantitative analysis tools, it can be more difficult to argue the need for appropriate countermeasures. An analysis such as the one presented here can provide the detailed quantitative substantiation required to build cases for these targeted policy directives and infrastructure investments.

Efficient and consistent reliability analysis of soil slope stability using both limit equilibrium analysis and finite element analysis

Limit equilibrium methods (LEMs) and finite element methods (FEMs) of slope stability analysis can be used in computer-based probabilistic simulation approaches (e.g., direct Monte Carlo Simulation (MCS) and Subset Simulation (SS)) to evaluate the slope failure probability (Pf). For a given slope problem, the computational effort for the LEM is generally much less than that required for the FEM, but the FEM tends to give a more realistic prediction of slope failure mechanism and its associated factor of safety. To make use of the advantages of both the LEM (e.g., computationally more efficient) and FEM (e.g., theoretically more realistic and rigorous in terms of slope failure mechanisms), a new probabilistic simulation method is developed in the paper. The proposed approach combines both a simple LEM (i.e., Ordinary Method of Slices considering a limited number of potential slip surfaces) and FEM with the response conditioning method to efficiently calculate Pf of slope stability and to give an estimate of Pf consistent with that obtained from directly performing MCS and SS based on the FEM. It is illustrated through two soil slope examples. Results show that the proposed approach calculates the Pf properly at small probability levels (e.g., Pf < 0.001). More importantly, it significantly reduces the number of finite element analyses needed in the calculation, and therefore improves the computational efficiency at small probability levels that are of great interest in slope design practice. In addition, the proposed approach opens up the possibility that makes use of the information obtained using a simple model (e.g., LEM) to guide the reliability analysis based on a relatively sophisticated model (e.g., FEM).

Investigation of the effect of reservoir bottom absorption on seismic performance of concrete gravity dams using sensitivity analysis

In this research, probabilistic analysis of the seismic performance of gravity dams is presented considering interaction effects to quantify the sensitivity of seismic response to reservoir bottom absorption. The maximum value of horizontal displacement of dam crest, hydrodynamic pressure, 1st principle stress at heel and 3rd principle stress at toe of dam, during earthquake excitations, are considered random variables in the probabilistic analysis of base-case model. In order to propagate the parametric sensitivity to the seismic performance of the system, Monte Carlo simulation with Latin hypercube sampling is used as a probabilistic simulation method. Sensitivity of responses under seismic loading is reliably examined utilizing different values of bottom absorption as random inputs. Computational procedure for two-dimensional finite-element analysis of the base-case model is carried out in time domain considering the dynamic excitation and dam-reservoir interaction. Considering the obtained results, that the manner through which the bottom absorption can affect the seismic behavior of concrete gravity dams due to earthquake is determined. To estimate the structural performance under seismic loads, a suite of ground motions is selected and applied to the base-case model and the model sensitivity to reservoir bottom absorption is investigated. Finally, to show the effectiveness of the sensitivity and probabilistic analysis procedure presented in this paper the response of Koyna and Pine Flat dams with respect to the horizontal component of El Centro earthquake is investigated as real models. The obtained results show that the sensitivity curves of output parameters are linear or bilinear relative to the models and ground motion records.

Reliability assessment of a standalone wind-conventional/energy storage system using probabilistic production simulation method

This paper presents a reliability evaluation technique for a small standalone system including wind and conventional resources integrated with an energy storage system. A battery bank is considered as the energy storage system. The focus in this analysis is on the effect of battery modeling and its performance on system reliability. In this way, this paper presents an accurate model for the electrochemical batteries, which takes into account all the influential characteristics of the battery related to reliability studies, such as depth of charge, efficiency of charge, and rate of discharge. The proposed reliability evaluation method is based on the combination of a probabilistic production simulation technique as an analytical method and time series technique, in the domain of adequacy studies. By using the probabilistic production simulation technique, not only can the reliability of wind-conventional resources be assessed, but the fuel consumption can also be analyzed. On the other hand, by using the time series approach, the time variation of wind speed and operating constraints of the battery bank can be considered in evaluating the reliability indices. In this paper, a typical lead--acid battery bank is used. It is shown how the accurate modeling of the battery bank can affect the reliability indices. Beneficial results are obtained for different operating strategies and wind penetrations and are then compared.

Scalable quantum simulation of pulsed entanglement and Einstein-Podolsky-Rosen steering in optomechanics

We demonstrate a complete, probabilistic quantum dynamical simulation of the standard nonlinear Hamiltonian of optomechanics, including decoherence at finite temperatures. Robust entanglement of an optical pulse with the oscillator is predicted, as well as strong quantum steering between the optical and mechanical systems. Importantly, our probabilistic quantum simulation method uses the positive-P technique, which is scalable to large Hilbert spaces.