Monte Carlo vs. Fuzzy Monte Carlo Simulation for Uncertainty and Global Sensitivity Analysis

Kim, Young‐Jin

doi:10.3390/su9040539

Cited by 19 publications

(10 citation statements)

References 19 publications

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“…The values of trip time vary since clock-based trip time components are random variables subject to probability distributions. The probability distributions of clock-based trip time components are obtained and used in Monte Carlo simulation [37] to generate the passengers' PTT.…”

Section: Probability Distributions Of Clock-based Trip Time Componentsmentioning

confidence: 99%

Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

et al. 2021

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Time reliability in a Rail Transit Network (RTN) is usually measured according to clock-based trip time, while the travel conditions such as travel comfort and convenience cannot be reflected by clock-based trip time. Here, the crowding level of trains, seat availability, and transfer times are considered to compute passengers’ Perceived Trip Time (PTT). Compared with the average PTT, the extra PTT needed for arriving reliably, which equals the 95th percentile PTT minus the average PTT, is converted into the monetary cost for estimating Perceived Time Reliability Cost (PTRC). The ratio of extra PTT needed for arriving reliably to the average PTT referring to the buffer time index is proposed to measure Perceived Time Reliability (PTR). To overcome the difficulty of obtaining passengers’ PTT who travel among rail transit modes, a Monte Carlo simulation is applied to generated passengers’ PTT for computing PTR and PTRC. A case study of Chengdu’s RTN shows that the proposed metrics and method measure the PTR and PTRC in an RTN effectively. PTTR, PTRC, and influential factors have significant linear relations among them, and the obtained linear regression models among them can guide passengers to travel reliably.

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Section: Probability Distributions Of Clock-based Trip Time Componentsmentioning

confidence: 99%

Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

et al. 2021

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“…It has been extensively used for generating many scenarios by considering the random sampling of each probability distribution. In practice, the probability of an event can be estimated according to the frequency of that event occurring in a number of experiments [3]. However, if the number of experiments is not large enough to be significant, and more experiments cannot be performed, it is not possible to accurately estimate the event's probability.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Monte Carlo Simulation to Optimize Resource Planning and Operations

Alzarrad¹

2021

Concepts, Applications and Emerging Opportunities in Industrial Engineering

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Resources planning and operations are essential concerns and specialty areas within industrial engineering and project management. Crew configuration plays a significant role in resource planning and operations. Crew configuration inefficiency is one of the most common reasons for the low productivity of manpower. Resources planning contains some inherent uncertainties and risks because it is an estimate of unknown values. Many factors affect resource planning. Some of these factors are fuzzy variables such as expert's judgment, and some of them are random variables such as direct cost of equipment. The objective of this chapter is to present a method that combines fuzzy logic and Monte Carlo simulation (MCS) for the selection of the best crew configuration to perform a certain task. The model presented in this chapter is a joint propagation method based on both the probability theory of MCS and the possibility theory of fuzzy arithmetic. The research outcomes indicate that the presented model can reduce the duration and cost of a certain task, which will help reduce the cost and duration of the project.

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“…Also, some hybrid models have been proposed that try to combine benefits of the mentioned techniques such as Fuzzy Monte Carlo simulation [6], Fuzzy ANP [7] and Fuzzy AHP [8], etc.…”

Section: Introductionmentioning

confidence: 99%

“…In 2017, Kim proposed Fuzzy MCS and compared the results with Monte Carlo simulation. By comparing the results of Fuzzy MCS and MCS, one can see more reliable and meaningful information has been achieved from the Fuzzy Monte Carlo simulation [6].…”

Section: Introductionmentioning

confidence: 99%

A novel risk assessment approach using Monte Carlo simulation based on co-occurrence of risk factors: A case study of a petrochemical plant construction

Ostadi

Harofteh

2020

Scientia Iranica

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Nowadays, because of the advancement of technology and subsequently unpredictable events, it is important for addressing risk management as an important part of projects and business. In this paper, a novel approach based on Monte Carlo simulation has been proposed for risk assessment, which considers the co-occurrence of risks. In this method, the output of extended and classic Monte Carlo simulation is applied for co-occurrence-based risk assessment (CORA) and prioritization. Also, the magnitude in each source of uncertainty has been determined by a new approach. The proposed model investigates risk's relationship and determines the type of effect as resonance or reduction in addition to identifying and analyzing the risks. Also, a system dynamic model is applied to illustrate the relationships of risks. Finally, this method is applied to a petrochemical project. Five risks as temperature, rain, labor, cost, and inflation are considered in this project. Based on the numerical results, the most important risk is inflation. Also, there is a significant different between the result of the proposed model in comparison with model that ignore the co-occurrence of risks. CORA helps the manager to consider all aspect of risks and help them to have a better decision.

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Monte Carlo vs. Fuzzy Monte Carlo Simulation for Uncertainty and Global Sensitivity Analysis

Cited by 19 publications

References 19 publications

Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

Perceived Trip Time Reliability and Its Cost in a Rail Transit Network

Fuzzy Monte Carlo Simulation to Optimize Resource Planning and Operations

A novel risk assessment approach using Monte Carlo simulation based on co-occurrence of risk factors: A case study of a petrochemical plant construction

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