An application of Earned Value Management (EVM) with Monte Carlo simulation in engineering project management

Bonato, Felipe Kahan; Albuquerque, Andrei Aparecido de; Paixão, Michel Augusto Santana da

doi:10.1590/0104-530x4641-19

Cited by 6 publications

(1 citation statement)

References 19 publications

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“…Ponz-Tienda et al 35 found that using fuzzy arithmetic on EVM provided more objective results in uncertain environments than the traditional methodology. Bonato et al 37 integrated EVM with Monte Carlo simulation to predict the final cost of three engineering projects. Batselier and Vanhoucke 38 compared the accuracy of the project time and cost forecasting using EVM and simulation.…”

Section: Introductionmentioning

confidence: 99%

Machine learning in project analytics: a data-driven framework and case study

Uddin

Ong

2022

Sci Rep

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The analytic procedures incorporated to facilitate the delivery of projects are often referred to as project analytics. Existing techniques focus on retrospective reporting and understanding the underlying relationships to make informed decisions. Although machine learning algorithms have been widely used in addressing problems within various contexts (e.g., streamlining the design of construction projects), limited studies have evaluated pre-existing machine learning methods within the delivery of construction projects. Due to this, the current research aims to contribute further to this convergence between artificial intelligence and the execution construction project through the evaluation of a specific set of machine learning algorithms. This study proposes a machine learning-based data-driven research framework for addressing problems related to project analytics. It then illustrates an example of the application of this framework. In this illustration, existing data from an open-source data repository on construction projects and cost overrun frequencies was studied in which several machine learning models (Python’s Scikit-learn package) were tested and evaluated. The data consisted of 44 independent variables (from materials to labour and contracting) and one dependent variable (project cost overrun frequency), which has been categorised for processing under several machine learning models. These models include support vector machine, logistic regression, k-nearest neighbour, random forest, stacking (ensemble) model and artificial neural network. Feature selection and evaluation methods, including the Univariate feature selection, Recursive feature elimination, SelectFromModel and confusion matrix, were applied to determine the most accurate prediction model. This study also discusses the generalisability of using the proposed research framework in other research contexts within the field of project management. The proposed framework, its illustration in the context of construction projects and its potential to be adopted in different contexts will significantly contribute to project practitioners, stakeholders and academics in addressing many project-related issues.

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Section: Introductionmentioning

confidence: 99%