Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm
Machine learning technologies have recently emerged as transformative tools for enhancing project management accuracy and efficiency. This study introduces a data-driven model that leverages the hybrid eXtreme Gradient Boosting-Simulated Annealing (XGBoost-SA) algorithm to predict the time and cost...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-11-01
|
| Series: | Machines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-1702/12/12/867 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846103901669949440 |
|---|---|
| author | Ali Akbar ForouzeshNejad Farzad Arabikhan Shohin Aheleroff |
| author_facet | Ali Akbar ForouzeshNejad Farzad Arabikhan Shohin Aheleroff |
| author_sort | Ali Akbar ForouzeshNejad |
| collection | DOAJ |
| description | Machine learning technologies have recently emerged as transformative tools for enhancing project management accuracy and efficiency. This study introduces a data-driven model that leverages the hybrid eXtreme Gradient Boosting-Simulated Annealing (XGBoost-SA) algorithm to predict the time and cost of construction projects. By accounting for the complexity of activity networks and uncertainties within project environments, the model aims to address key challenges in project forecasting. Unlike traditional methods such as Earned Value Management (EVM) and Earned Schedule Method (ESM), which rely on static metrics, the XGBoost-SA model adapts dynamically to project data, achieving 92% prediction accuracy. This advanced model offers a more precise forecasting approach by incorporating and optimizing features from historical data. Results reveal that XGBoost-SA reduces cost prediction error by nearly 50% and time prediction error by approximately 80% compared to EVM and ESM, underscoring its effectiveness in complex scenarios. Furthermore, the model’s ability to manage limited and evolving data offers a practical solution for real-time adjustments in project planning. With these capabilities, XGBoost-SA provides project managers with a powerful tool for informed decision-making, efficient resource allocation, and proactive risk management, making it highly applicable to complex construction projects where precision and adaptability are essential. The main limitation of the developed model in this study is the reliance on data from similar projects, which necessitates additional data for application to other industries. |
| format | Article |
| id | doaj-art-fb6d9bb5c6c0495a90622f11cd3e1e51 |
| institution | Kabale University |
| issn | 2075-1702 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Machines |
| spelling | doaj-art-fb6d9bb5c6c0495a90622f11cd3e1e512024-12-27T14:37:01ZengMDPI AGMachines2075-17022024-11-01121286710.3390/machines12120867Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing AlgorithmAli Akbar ForouzeshNejad0Farzad Arabikhan1Shohin Aheleroff2School of computing, Faculty of Technology, University of Portsmouth, Portsmouth PO1 2UP, UKSchool of computing, Faculty of Technology, University of Portsmouth, Portsmouth PO1 2UP, UKDepartment of Mechanical and Mechatronics Engineering, The University of Auckland, Auckland 1142, New ZealandMachine learning technologies have recently emerged as transformative tools for enhancing project management accuracy and efficiency. This study introduces a data-driven model that leverages the hybrid eXtreme Gradient Boosting-Simulated Annealing (XGBoost-SA) algorithm to predict the time and cost of construction projects. By accounting for the complexity of activity networks and uncertainties within project environments, the model aims to address key challenges in project forecasting. Unlike traditional methods such as Earned Value Management (EVM) and Earned Schedule Method (ESM), which rely on static metrics, the XGBoost-SA model adapts dynamically to project data, achieving 92% prediction accuracy. This advanced model offers a more precise forecasting approach by incorporating and optimizing features from historical data. Results reveal that XGBoost-SA reduces cost prediction error by nearly 50% and time prediction error by approximately 80% compared to EVM and ESM, underscoring its effectiveness in complex scenarios. Furthermore, the model’s ability to manage limited and evolving data offers a practical solution for real-time adjustments in project planning. With these capabilities, XGBoost-SA provides project managers with a powerful tool for informed decision-making, efficient resource allocation, and proactive risk management, making it highly applicable to complex construction projects where precision and adaptability are essential. The main limitation of the developed model in this study is the reliance on data from similar projects, which necessitates additional data for application to other industries.https://www.mdpi.com/2075-1702/12/12/867time forecastingcost forecastingextreme gradient boostingsimulated annealingnetwork complexity |
| spellingShingle | Ali Akbar ForouzeshNejad Farzad Arabikhan Shohin Aheleroff Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm Machines time forecasting cost forecasting extreme gradient boosting simulated annealing network complexity |
| title | Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm |
| title_full | Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm |
| title_fullStr | Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm |
| title_full_unstemmed | Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm |
| title_short | Optimizing Project Time and Cost Prediction Using a Hybrid XGBoost and Simulated Annealing Algorithm |
| title_sort | optimizing project time and cost prediction using a hybrid xgboost and simulated annealing algorithm |
| topic | time forecasting cost forecasting extreme gradient boosting simulated annealing network complexity |
| url | https://www.mdpi.com/2075-1702/12/12/867 |
| work_keys_str_mv | AT aliakbarforouzeshnejad optimizingprojecttimeandcostpredictionusingahybridxgboostandsimulatedannealingalgorithm AT farzadarabikhan optimizingprojecttimeandcostpredictionusingahybridxgboostandsimulatedannealingalgorithm AT shohinaheleroff optimizingprojecttimeandcostpredictionusingahybridxgboostandsimulatedannealingalgorithm |