A Robust, Resilience Machine Learning With a Risk Approach for Project Scheduling
ABSTRACT This study proposes a novel Robust, Resilient, and Risk‐Based approach in Machine Learning (3RML) that emphasizes the application of project scheduling for the first time. A robust stochastic LASSO regression model is proposed to predict project duration. This model seeks to enhance a tradi...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Engineering Reports |
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| Online Access: | https://doi.org/10.1002/eng2.70161 |
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| author | Reza Lotfi Soheila Sadeghi Sadia Samar Ali Fatemeh Ramyar Ehsan Ghafourian Ebrahim Farbod |
| author_facet | Reza Lotfi Soheila Sadeghi Sadia Samar Ali Fatemeh Ramyar Ehsan Ghafourian Ebrahim Farbod |
| author_sort | Reza Lotfi |
| collection | DOAJ |
| description | ABSTRACT This study proposes a novel Robust, Resilient, and Risk‐Based approach in Machine Learning (3RML) that emphasizes the application of project scheduling for the first time. A robust stochastic LASSO regression model is proposed to predict project duration. This model seeks to enhance a traditional LASSO regression by minimizing the expected value and the Weighted Value at Risk (WVaR) of the Mean Absolute Deviation (MAD) while penalizing the regression coefficients. The 3R requirements, which prioritize robustness, resilience, and risk aversion, are integrated into the mathematical model to ensure flexibility and disaster consideration. A comparative analysis was carried out between the square root, logarithm, and mixed linear/square root models and the baseline model. The Robust, Resilience MAD with Risk‐Averse (RRMADR) and R‐squared values were computed. The square root regression model demonstrated a 36% enhancement compared with the primary model. The conservatism coefficient affects risk levels, where a 5% increase results in a 2% decrease in the RRMADR. Varying confidence levels influence the model. The penalty coefficient in the lasso regression affects RRMADR and R‐squared. The resiliency coefficient impacts both the RRMADR and R‐squared. Probability scenarios influence RRMADR but do not affect R‐squared. The type of probability density influences the RRMADR but does not impact R‐squared. |
| format | Article |
| id | doaj-art-cf67da3326af4e8c87e66be7a5b5c9a0 |
| institution | Matheson Library |
| issn | 2577-8196 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Engineering Reports |
| spelling | doaj-art-cf67da3326af4e8c87e66be7a5b5c9a02025-06-27T00:38:48ZengWileyEngineering Reports2577-81962025-06-0176n/an/a10.1002/eng2.70161A Robust, Resilience Machine Learning With a Risk Approach for Project SchedulingReza Lotfi0Soheila Sadeghi1Sadia Samar Ali2Fatemeh Ramyar3Ehsan Ghafourian4Ebrahim Farbod5Department of Industrial Engineering Yazd University Yazd IranDreeben School of Education University of the Incarnate Word San Antonio Texas USADepartment of Industrial Engineering, Faculty of Engineering King Abdulaziz University Jeddah Saudi ArabiaDepartment of Industrial and Systems Engineering Auburn University Auburn Alabama USADepartment of Computer Science Iowa State University Ames Iowa USADepartment of Industrial Engineering Payame Noor University Tehran IranABSTRACT This study proposes a novel Robust, Resilient, and Risk‐Based approach in Machine Learning (3RML) that emphasizes the application of project scheduling for the first time. A robust stochastic LASSO regression model is proposed to predict project duration. This model seeks to enhance a traditional LASSO regression by minimizing the expected value and the Weighted Value at Risk (WVaR) of the Mean Absolute Deviation (MAD) while penalizing the regression coefficients. The 3R requirements, which prioritize robustness, resilience, and risk aversion, are integrated into the mathematical model to ensure flexibility and disaster consideration. A comparative analysis was carried out between the square root, logarithm, and mixed linear/square root models and the baseline model. The Robust, Resilience MAD with Risk‐Averse (RRMADR) and R‐squared values were computed. The square root regression model demonstrated a 36% enhancement compared with the primary model. The conservatism coefficient affects risk levels, where a 5% increase results in a 2% decrease in the RRMADR. Varying confidence levels influence the model. The penalty coefficient in the lasso regression affects RRMADR and R‐squared. The resiliency coefficient impacts both the RRMADR and R‐squared. Probability scenarios influence RRMADR but do not affect R‐squared. The type of probability density influences the RRMADR but does not impact R‐squared.https://doi.org/10.1002/eng2.70161machine learningproject schedulingresiliencyriskrobust optimization |
| spellingShingle | Reza Lotfi Soheila Sadeghi Sadia Samar Ali Fatemeh Ramyar Ehsan Ghafourian Ebrahim Farbod A Robust, Resilience Machine Learning With a Risk Approach for Project Scheduling Engineering Reports machine learning project scheduling resiliency risk robust optimization |
| title | A Robust, Resilience Machine Learning With a Risk Approach for Project Scheduling |
| title_full | A Robust, Resilience Machine Learning With a Risk Approach for Project Scheduling |
| title_fullStr | A Robust, Resilience Machine Learning With a Risk Approach for Project Scheduling |
| title_full_unstemmed | A Robust, Resilience Machine Learning With a Risk Approach for Project Scheduling |
| title_short | A Robust, Resilience Machine Learning With a Risk Approach for Project Scheduling |
| title_sort | robust resilience machine learning with a risk approach for project scheduling |
| topic | machine learning project scheduling resiliency risk robust optimization |
| url | https://doi.org/10.1002/eng2.70161 |
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