Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projects
Project Risk Management contains processes ranging from planning to control. It is applied to identify risks, analyze them, and design responses to change the occurrence rate and/or the effect of project risks. It is important for project managers to analyze the effects of the risks in projects...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Growing Science
2025-01-01
|
| Series: | Journal of Project Management |
| Online Access: | http://www.growingscience.com/jpm/Vol10/jpm_2024_38.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846122945375633408 |
|---|---|
| author | Sajad Soltan Maryam Ashrafi |
| author_facet | Sajad Soltan Maryam Ashrafi |
| author_sort | Sajad Soltan |
| collection | DOAJ |
| description |
Project Risk Management contains processes ranging from planning to control. It is applied to identify risks, analyze them, and design responses to change the occurrence rate and/or the effect of project risks. It is important for project managers to analyze the effects of the risks in projects and also consider project risks in their decisions. If project risks are not addressed during the risk management process, issues such as schedule delays, cost overruns, and even project failure may occur. This paper aims to introduce a Markov method to integrate project risk analysis and risk response planning. This method is applied to forecast the following status of the project when limited information about the project is available. Moreover, earned value management (EVM) methods were used to include various types of project risks through the project lifecycle. The model also offers the capability to choose the most effective risk response for managing project risks through the application of the Markov decision process (MDP). Eventually, we introduce a case study to demonstrate functionality and effectiveness of the presented approach. Solving the model allows for identifying the best set of risk response strategies tailored to each specific project state. The computational results illustrate that the current state of the project has a significant impact in the process of risk response planning. Since uncertainty is the inherent characteristic of projects, the use of the project’s current state is more reliable than the previous status of projects, and the Markov method is applied in this research because it uses the current state for its modelling. Using this method, managers can predict the future state of projects and find the best response in each status of projects.
|
| format | Article |
| id | doaj-art-fe8f12e858854c89a5f09699f23d2b69 |
| institution | Kabale University |
| issn | 2371-8366 2371-8374 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Growing Science |
| record_format | Article |
| series | Journal of Project Management |
| spelling | doaj-art-fe8f12e858854c89a5f09699f23d2b692024-12-14T11:04:56ZengGrowing ScienceJournal of Project Management2371-83662371-83742025-01-01101718610.5267/j.jpm.2024.11.001Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projectsSajad Soltan Maryam Ashrafi Project Risk Management contains processes ranging from planning to control. It is applied to identify risks, analyze them, and design responses to change the occurrence rate and/or the effect of project risks. It is important for project managers to analyze the effects of the risks in projects and also consider project risks in their decisions. If project risks are not addressed during the risk management process, issues such as schedule delays, cost overruns, and even project failure may occur. This paper aims to introduce a Markov method to integrate project risk analysis and risk response planning. This method is applied to forecast the following status of the project when limited information about the project is available. Moreover, earned value management (EVM) methods were used to include various types of project risks through the project lifecycle. The model also offers the capability to choose the most effective risk response for managing project risks through the application of the Markov decision process (MDP). Eventually, we introduce a case study to demonstrate functionality and effectiveness of the presented approach. Solving the model allows for identifying the best set of risk response strategies tailored to each specific project state. The computational results illustrate that the current state of the project has a significant impact in the process of risk response planning. Since uncertainty is the inherent characteristic of projects, the use of the project’s current state is more reliable than the previous status of projects, and the Markov method is applied in this research because it uses the current state for its modelling. Using this method, managers can predict the future state of projects and find the best response in each status of projects. http://www.growingscience.com/jpm/Vol10/jpm_2024_38.pdf |
| spellingShingle | Sajad Soltan Maryam Ashrafi Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projects Journal of Project Management |
| title | Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projects |
| title_full | Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projects |
| title_fullStr | Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projects |
| title_full_unstemmed | Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projects |
| title_short | Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projects |
| title_sort | application of reinforcement learning for integrating project risk analysis and risk response planning a case study on construction projects |
| url | http://www.growingscience.com/jpm/Vol10/jpm_2024_38.pdf |
| work_keys_str_mv | AT sajadsoltan applicationofreinforcementlearningforintegratingprojectriskanalysisandriskresponseplanningacasestudyonconstructionprojects AT maryamashrafi applicationofreinforcementlearningforintegratingprojectriskanalysisandriskresponseplanningacasestudyonconstructionprojects |