Risk quantification using fuzzy-based Monte Carlo simulation

Moselhi, Osama; Roghabadi, Mohammadjavad Arabpour

doi:10.36680/j.itcon.2020.005

Cited by 6 publications

(3 citation statements)

References 24 publications

(38 reference statements)

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“…The fuzzy set theory is used to represent the subjectivity of the input data provided by members of project team (Moselhi and Roghabadi, 2020). In this study, the model uses fuzzy sets to represent the uncertainty related to crew productivity rates and amounts of work in repetitive units.…”

Section: Model Developmentmentioning

confidence: 99%

Optimized crew selection for scheduling of repetitive projects

Roghabadi

Moselhi

2020

ECAM

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PurposeThe purpose of this paper is to identify optimum crew formations at unit execution level of repetitive projects that minimize project duration, project cost, crew work interruptions and interruption costs, simultaneously.Design/methodology/approachThe model consists of four modules. The first module quantifies uncertainties associated with the crew productivity rate and quantity of work using the fuzzy set theory. The second module identifies feasible boundaries for activity relaxation. The third module computes direct cost, indirect cost and interruption costs, including idle crew cost as well as mobilization and demobilization costs. The fourth module identifies near-optimum crew formation using a newly developed multi-objective optimization model.FindingsThe developed model was able to provide improvements of 0.2, 16.86 and 12.98% for minimization of project cost, crew work interruptions and interruption costs from US$1,505,960, 8.3 days and US$8,300, as recently reported in the literature, to US$1,502,979, 6.9 days and US$7,222, respectively, without impacting the optimized project duration.Originality/valueThe novelty of this paper lies in its activity-relaxation free float that considers the effect of postponing early finish dates of repetitive activities on crew work interruptions. The introduced new float allows for calculating the required crew productivity rate that minimizes crew work interruptions without delaying successor activities and without impacting the optimized project duration. It safeguards against assignment of unnecessary costly resources.

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Section: Model Developmentmentioning

confidence: 99%

Optimized crew selection for scheduling of repetitive projects

Roghabadi

Moselhi

2020

ECAM

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“…The fuzzy set theory is a frequently used method for managing construction risks [20][21][22]. The fuzzy representation of linguistic terms can capture the vagueness and imprecision associated with the input data provided by the participants [23].…”

Section: Fuzzy Set Theorymentioning

confidence: 99%

A Fuzzy-Based Decision Support Model for Risk Maturity Evaluation of Construction Organizations

Roghabadi

Moselhi

2020

Algorithms

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Risk maturity evaluation is an efficient tool which can assist construction organizations in the identification of their strengths and weaknesses in risk management processes and in taking necessary actions for the improvement of these processes. The accuracy of its results relies heavily on the quality of responses provided by participants specialized in these processes across the organization. Risk maturity models reported in the literature gave equal importance to participants’ responses during the model development, neglecting their level of authority in the organization as well as their level of expertise in risk management processes. Unlike the existing models, this paper presents a new risk maturity model that considers the relative importance of the responses provided by the participants in the model development. It considered their authority in the organization and their level of involvement in the risk management processes for calculating the relative weights associated with the risk maturity attributes. It employed an analytic network process (ANP) to model the interdependencies among the risk maturity attributes and utilizes the fuzzy set theory to incorporate the uncertainty associated with the ambiguity of the responses used in the model development. The developed model allows the construction organizations to have a more accurate and realistic view of their current performance in risk management processes. The application of the developed model was investigated by measuring the risk maturity level of an industrial partner working on civil infrastructure projects in Canada.

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“…If the model is complex with many stochastic variables, theoretical analysis will be relatively difficult. In this case, the MC simulation [21,22] can be introduced to easily compute the probability of each drilling risk. Hence, this paper selects MC simulation to determine the drilling risk probabilities.…”

Section: Calculation Of Drilling Risk Probabilitiesmentioning

confidence: 99%

Formation Matrix and Its Application in Drilling Risk Evaluation

Wei¹,

Li²,

Luan³

et al. 2020

IJSSE

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Drilling safety is the foundation for the safe and efficient development of oil and gas resources. However, drilling accidents (e.g. blowout, lost circulation and borehole instability) often occur due to the complex geological conditions, especially in deep wells, ultra-deep wells and offshore oil and gas wells. To prepare a targeted drilling plan and minimize drilling risks, the key is to grasp the accurate geological parameters of the target formation. However, drilling safety is seriously threatened by the numerous complex, stochastic, and uncertain factors in the target formation, as well as the errors in the formation parameters captured by the current techniques. This paper attempts to quantify drilling risk accurately, despite the uncertainty of drilling information, and make efficient use of geological parameters. For this purpose, the concept of formation matrix was put forward, and coupled with credibility to handle the inevitable errors in formation parameters. According to the mechanical mechanism of common drilling risks, a Monte-Carlo (MC) simulation method was established to calculate the probabilities of drilling risks. The proposed method was applied to evaluate the well kick risk of a well in Kekeya area of Tuha Basin. The results show that our method can accurately predict the drilling risks that conforms to the mechanical hazard mechanism, effectively integrate various drilling information, making them complement each other, and improve the characterization accuracy of formation matrix, thereby enhancing the reliability of drilling risk evaluation. The proposed method provides theoretical support for geological modeling and drilling risk evaluation.

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Risk quantification using fuzzy-based Monte Carlo simulation

Cited by 6 publications

References 24 publications

Optimized crew selection for scheduling of repetitive projects

Optimized crew selection for scheduling of repetitive projects

A Fuzzy-Based Decision Support Model for Risk Maturity Evaluation of Construction Organizations

Formation Matrix and Its Application in Drilling Risk Evaluation

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