How to cite this paper
APA: Soltan, S & Ashrafi, M. (2025). Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projects. Journal of Project Management, 10(1), 71-86.
Chicago/Turabian: Soltan, S & Ashrafi, M. 2025. "Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projects." Journal of Project Management 10, no. 1 (2025): 71-86.
AMA: Soltan, S & Ashrafi, M. Application of reinforcement learning for integrating project risk analysis and risk response planning: A case study on construction projects. Journal of Project Management. 2025;10(1):71-86.
References
Acebes, F., Pajares, J., Galán, J. M., & López-Paredes, A. (2013). Beyond earned value management: A graphical framework for integrated cost, schedule and risk monitoring. Procedia-Social and Behavioral Sciences, 74, 181-189.
Acebes, F., Pajares, J., Galán, J. M., & López-Paredes, A. (2014). A new approach for project control under uncertainty. Going back to the basics. International Journal of Project Management, 32(3), 423-434.
Alhawari, S., Karadsheh, L., Talet, A. N., & Mansour, E. (2012). Knowledge-based risk management framework for in-formation technology project. International Journal of Information Management, 32(1), 50-65.
Aljohani, A., Ahiaga-Dagbui, D., & Moore, D. (2017). Construction projects cost overrun: What does the literature tell us?. International Journal of Innovation, Management and Technology, 8(2), 137.
Arena, M., Azzone, G., Cagno, E., Ferretti, G., Prunotto, E., Silvestri, A., & Trucco, P. (2013). Integrated risk manage-ment through dynamic capabilities within project-based organizations: the company dynamic response map. Risk Management, 15(1), 50-77.
Bartlett, J. (2004). Project risk analysis and management guide. APM publishing limited.
Barghi, B. (2020). Qualitative and quantitative project risk assessment using a hybrid PMBOK model developed under uncertainty conditions. Heliyon, 6(1).
Batselier, J., & Vanhoucke, M. (2015). Construction and evaluation framework for a real-life project data-base. International Journal of Project Management, 33(3), 697-710.
Batselier, J., & Vanhoucke, M. (2015). Evaluation of deterministic state-of-the-art forecasting approaches for project duration based on earned value management. International Journal of Project Management, 33(7), 1588-1596.
Batselier, J., & Vanhoucke, M. (2017). Improving project forecast accuracy by integrating earned value management with exponential smoothing and reference class forecasting. International journal of project management, 35(1), 28-43.
Bonilla, C. A., Vergara, M., & Watt, R. (2022). Changes in risk and entrepreneurship. Risk Management, 24(4), 367-385.
Brookfield, D., Fischbacher-Smith, D., Mohd-Rahim, F., & Boussabaine, H. (2014). Conceptualising and responding to risk in IT projects. Risk Management, 16, 195-230.
Cagno, E., Caron, F., & Mancini, M. (2007). A multi-dimensional analysis of major risks in complex projects. Risk Management, 9, 1-18.
Chen, Y. S., Chuang, H. M., Sangaiah, A. K., Lin, C. K., & Huang, W. B. (2019). A study for project risk management using an advanced MCDM-based DEMATEL-ANP approach. Journal of Ambient Intelligence and Humanized Com-puting, 10, 2669-2681.
Colin, J., & Vanhoucke, M. (2016). Empirical perspective on activity durations for project-management simulation studies. Journal of Construction Engineering and Management, 142(1), 04015047.
Fan, Z. P., Li, Y. H., & Zhang, Y. (2015). Generating project risk response strategies based on CBR: A case study. Expert Systems with Applications, 42(6), 2870-2883.
Fang, C., & Marle, F. (2012). A simulation-based risk network model for decision support in project risk manage-ment. Decision Support Systems, 52(3), 635-644.
Ford, D. N., & Lander, D. M. (2011). Real option perceptions among project managers. Risk management, 13, 122-146.
Gładysz, B., Skorupka, D., Kuchta, D., & Duchaczek, A. (2015). Project risk time management–a proposed model and a case study in the construction industry. Procedia Computer Science, 64, 24-31.
Gupta, V. K., & Thakkar, J. J. (2018). A quantitative risk assessment methodology for construction pro-ject. Sādhanā, 43(7), 116.
Han, Q., Zhu, Y., Ke, G. Y., & Hipel, K. W. (2019). Public private partnership in brownfield remediation projects in China: Identification and structure analysis of risks. Land Use Policy, 84, 87-104.
Hu, Q., & Yue, W. (2007). Markov decision processes with their applications (Vol. 14). Springer Science & Business Media.
IEEE Standards Association. (2001). IEEE 1540-2001. IEEE standard for software life cycle processes-Risk manage-ment. The Institute of Electrical and Electronics Engineers.
International Organization for Standardization, (2002). Risk management-vocabulary-guidelines for use in standards. ISO.
International Organization for Standardization. (2017). Quality management — Guidelines for quality management in projects. ISO.
Jensen, J. L., Ponsaing, C. D., & Thrane, S. (2012). Risk, resources and structures: Experimental evidence of a new cost of risk component–The structural risk component and implications for enterprise risk management. Risk manage-ment, 14, 152-175.
Khodeir, L. M., & Nabawy, M. (2019). Identifying key risks in infrastructure projects–Case study of Cairo Festival City project in Egypt. Ain Shams Engineering Journal, 10(3), 613-621.
Kumar, C., & Yadav, D. K. (2015). A probabilistic software risk assessment and estimation model for software pro-jects. Procedia Computer Science, 54, 353-361.
Kwak, Y. H., & Ingall, L. (2007). Exploring Monte Carlo simulation applications for project management. Risk man-agement, 9, 44-57.
Leu, S. S., & Lin, Y. C. (2008). Project performance evaluation based on statistical process control techniques. Journal of Construction Engineering and Management, 134(10), 813-819.
Lipke, W. (2002). A study of the normality of earned value management indicators. The Measurable News, 4(1), 6.
Lipke, W., Zwikael, O., Henderson, K., & Anbari, F. (2009). Prediction of project outcome: The application of statistical methods to earned value management and earned schedule performance indexes. International journal of project management, 27(4), 400-407.
Liu, L., Wei, F., & Zhou, S. (2019). Major project risk assessment method based on BP neural network. Discrete and Continuous Dynamical Systems-S, 12(4&5), 1053-1064.
Luko, S. N. (2013). Risk management principles and guidelines. Quality Engineering, 25(4), 451-454.
Magni, P., Quaglini, S., Marchetti, M., & Barosi, G. (2000). Deciding when to intervene: a Markov decision process ap-proach. International Journal of Medical Informatics, 60(3), 237-253.
Marc, M., Arena, M., & Peljhan, D. (2023). The role of interactive style of use in improving risk management effective-ness. Risk Management, 25(2), 9.
Martens, A., & Vanhoucke, M. (2018). An empirical validation of the performance of project control tolerance lim-its. Automation in Construction, 89, 71-85.
Mojtahedi, S. M. H., Mousavi, S. M., & Makui, A. (2010). Project risk identification and assessment simultaneously us-ing multi-attribute group decision making technique. Safety science, 48(4), 499-507.
Mousavi, S. (2015). An application of stochastic processes for analysing risks in highway projects. Advanced Computa-tional Techniques in Electromagnetics, 15(1), 17-25.
Muriana, C., & Vizzini, G. (2017). Project risk management: A deterministic quantitative technique for assessment and mitigation. International Journal of Project Management, 35(3), 320-340.
Obare, D. M., & Muraya, M. M. (2019). Analyzing the Risks in Highway Projects Using the Markov Chain Ap-proach. American Journal of Applied Mathematics and Statistics, 7(2), 59-64.
OR-AS. (2020, November 13). Retrieved from Online consultation of the real-life project database Website, URL: http://www.projectmanagement.ugent.be/research/data/realdata
Project Management Institute. (2017). A guide to the project management body of knowledge (PMBOK guide). Newtown Square, Pa: Project Management Institute.
Puterman, M. L. (2014). Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons.
Rolik, Y. (2017). Risk management in implementing wind energy project. Procedia Engineering, 178, 278-288.
Ross, S. M. (2014). Introduction to probability models. Academic press.
Sigaud, O., & Buffet, O. (Eds.). (2013). Markov decision processes in artificial intelligence. John Wiley & Sons.
Smith, N. J., Merna, T., & Jobling, P. (2014). Managing risk in construction projects. John Wiley & Sons.
Soltan, S., & Ashrafi, M. (2020). Predicting project duration and cost, and selecting the best action plan using statistical methods for earned value management. Journal of Project Management, 5(3), 157-166.
Sujiao, Z. (2009, July). Risk analysis of construction projects based on markov chain. In 2009 Asia-Pacific Conference on Information Processing (Vol. 1, pp. 514-517). IEEE.
Tamak, J., & Bindal, D. (2013). An Empirical Study of Risk Management & Control. International Journal of Advance Research in Computer Science and Software Engineering, 3(12), 4.
Wu, D., Dai, Q., & Zhu, X. (2016). Measuring the effect of project risks based on Shapley value for project risk re-sponse. Procedia computer science, 91, 774-778.
Wu, D., Li, J., Xia, T., Bao, C., Zhao, Y., & Dai, Q. (2018). A multiobjective optimization method considering process risk correlation for project risk response planning. Information Sciences, 467, 282-295.
Yan, H., Gao, C., Elzarka, H., Mostafa, K., & Tang, W. (2019). Risk assessment for construction of urban rail transit projects. Safety science, 118, 583-594.
Zhang, Y. (2016). Selecting risk response strategies considering project risk interdependence. International Journal of Project Management, 34(5), 819-830.
Zhang, Y., & Fan, Z. P. (2014). An optimization method for selecting project risk response strategies. International journal of project management, 32(3), 412-422.
Zhang, Y., Luan, H., Shao, W., & Xu, Y. (2016). Managerial risk preference and its influencing factors: analysis of large state-owned enterprises management personnel in China. Risk Management, 18, 135-158.
Zhang, Y., Zuo, F., & Guan, X. (2020). Integrating case-based analysis and fuzzy optimization for selecting project risk response actions. Physica A: Statistical Mechanics and Its Applications, 545, 123578.
Zhao, X., Hwang, B. G., & Gao, Y. (2016). A fuzzy synthetic evaluation approach for risk assessment: a case of Singa-pore's green projects. Journal of Cleaner Production, 115, 203-213.
Zuo, F., & Zhang, K. (2018). Selection of risk response actions with consideration of secondary risks. International Journal of Project Management, 36(2), 241-254.
Acebes, F., Pajares, J., Galán, J. M., & López-Paredes, A. (2014). A new approach for project control under uncertainty. Going back to the basics. International Journal of Project Management, 32(3), 423-434.
Alhawari, S., Karadsheh, L., Talet, A. N., & Mansour, E. (2012). Knowledge-based risk management framework for in-formation technology project. International Journal of Information Management, 32(1), 50-65.
Aljohani, A., Ahiaga-Dagbui, D., & Moore, D. (2017). Construction projects cost overrun: What does the literature tell us?. International Journal of Innovation, Management and Technology, 8(2), 137.
Arena, M., Azzone, G., Cagno, E., Ferretti, G., Prunotto, E., Silvestri, A., & Trucco, P. (2013). Integrated risk manage-ment through dynamic capabilities within project-based organizations: the company dynamic response map. Risk Management, 15(1), 50-77.
Bartlett, J. (2004). Project risk analysis and management guide. APM publishing limited.
Barghi, B. (2020). Qualitative and quantitative project risk assessment using a hybrid PMBOK model developed under uncertainty conditions. Heliyon, 6(1).
Batselier, J., & Vanhoucke, M. (2015). Construction and evaluation framework for a real-life project data-base. International Journal of Project Management, 33(3), 697-710.
Batselier, J., & Vanhoucke, M. (2015). Evaluation of deterministic state-of-the-art forecasting approaches for project duration based on earned value management. International Journal of Project Management, 33(7), 1588-1596.
Batselier, J., & Vanhoucke, M. (2017). Improving project forecast accuracy by integrating earned value management with exponential smoothing and reference class forecasting. International journal of project management, 35(1), 28-43.
Bonilla, C. A., Vergara, M., & Watt, R. (2022). Changes in risk and entrepreneurship. Risk Management, 24(4), 367-385.
Brookfield, D., Fischbacher-Smith, D., Mohd-Rahim, F., & Boussabaine, H. (2014). Conceptualising and responding to risk in IT projects. Risk Management, 16, 195-230.
Cagno, E., Caron, F., & Mancini, M. (2007). A multi-dimensional analysis of major risks in complex projects. Risk Management, 9, 1-18.
Chen, Y. S., Chuang, H. M., Sangaiah, A. K., Lin, C. K., & Huang, W. B. (2019). A study for project risk management using an advanced MCDM-based DEMATEL-ANP approach. Journal of Ambient Intelligence and Humanized Com-puting, 10, 2669-2681.
Colin, J., & Vanhoucke, M. (2016). Empirical perspective on activity durations for project-management simulation studies. Journal of Construction Engineering and Management, 142(1), 04015047.
Fan, Z. P., Li, Y. H., & Zhang, Y. (2015). Generating project risk response strategies based on CBR: A case study. Expert Systems with Applications, 42(6), 2870-2883.
Fang, C., & Marle, F. (2012). A simulation-based risk network model for decision support in project risk manage-ment. Decision Support Systems, 52(3), 635-644.
Ford, D. N., & Lander, D. M. (2011). Real option perceptions among project managers. Risk management, 13, 122-146.
Gładysz, B., Skorupka, D., Kuchta, D., & Duchaczek, A. (2015). Project risk time management–a proposed model and a case study in the construction industry. Procedia Computer Science, 64, 24-31.
Gupta, V. K., & Thakkar, J. J. (2018). A quantitative risk assessment methodology for construction pro-ject. Sādhanā, 43(7), 116.
Han, Q., Zhu, Y., Ke, G. Y., & Hipel, K. W. (2019). Public private partnership in brownfield remediation projects in China: Identification and structure analysis of risks. Land Use Policy, 84, 87-104.
Hu, Q., & Yue, W. (2007). Markov decision processes with their applications (Vol. 14). Springer Science & Business Media.
IEEE Standards Association. (2001). IEEE 1540-2001. IEEE standard for software life cycle processes-Risk manage-ment. The Institute of Electrical and Electronics Engineers.
International Organization for Standardization, (2002). Risk management-vocabulary-guidelines for use in standards. ISO.
International Organization for Standardization. (2017). Quality management — Guidelines for quality management in projects. ISO.
Jensen, J. L., Ponsaing, C. D., & Thrane, S. (2012). Risk, resources and structures: Experimental evidence of a new cost of risk component–The structural risk component and implications for enterprise risk management. Risk manage-ment, 14, 152-175.
Khodeir, L. M., & Nabawy, M. (2019). Identifying key risks in infrastructure projects–Case study of Cairo Festival City project in Egypt. Ain Shams Engineering Journal, 10(3), 613-621.
Kumar, C., & Yadav, D. K. (2015). A probabilistic software risk assessment and estimation model for software pro-jects. Procedia Computer Science, 54, 353-361.
Kwak, Y. H., & Ingall, L. (2007). Exploring Monte Carlo simulation applications for project management. Risk man-agement, 9, 44-57.
Leu, S. S., & Lin, Y. C. (2008). Project performance evaluation based on statistical process control techniques. Journal of Construction Engineering and Management, 134(10), 813-819.
Lipke, W. (2002). A study of the normality of earned value management indicators. The Measurable News, 4(1), 6.
Lipke, W., Zwikael, O., Henderson, K., & Anbari, F. (2009). Prediction of project outcome: The application of statistical methods to earned value management and earned schedule performance indexes. International journal of project management, 27(4), 400-407.
Liu, L., Wei, F., & Zhou, S. (2019). Major project risk assessment method based on BP neural network. Discrete and Continuous Dynamical Systems-S, 12(4&5), 1053-1064.
Luko, S. N. (2013). Risk management principles and guidelines. Quality Engineering, 25(4), 451-454.
Magni, P., Quaglini, S., Marchetti, M., & Barosi, G. (2000). Deciding when to intervene: a Markov decision process ap-proach. International Journal of Medical Informatics, 60(3), 237-253.
Marc, M., Arena, M., & Peljhan, D. (2023). The role of interactive style of use in improving risk management effective-ness. Risk Management, 25(2), 9.
Martens, A., & Vanhoucke, M. (2018). An empirical validation of the performance of project control tolerance lim-its. Automation in Construction, 89, 71-85.
Mojtahedi, S. M. H., Mousavi, S. M., & Makui, A. (2010). Project risk identification and assessment simultaneously us-ing multi-attribute group decision making technique. Safety science, 48(4), 499-507.
Mousavi, S. (2015). An application of stochastic processes for analysing risks in highway projects. Advanced Computa-tional Techniques in Electromagnetics, 15(1), 17-25.
Muriana, C., & Vizzini, G. (2017). Project risk management: A deterministic quantitative technique for assessment and mitigation. International Journal of Project Management, 35(3), 320-340.
Obare, D. M., & Muraya, M. M. (2019). Analyzing the Risks in Highway Projects Using the Markov Chain Ap-proach. American Journal of Applied Mathematics and Statistics, 7(2), 59-64.
OR-AS. (2020, November 13). Retrieved from Online consultation of the real-life project database Website, URL: http://www.projectmanagement.ugent.be/research/data/realdata
Project Management Institute. (2017). A guide to the project management body of knowledge (PMBOK guide). Newtown Square, Pa: Project Management Institute.
Puterman, M. L. (2014). Markov decision processes: discrete stochastic dynamic programming. John Wiley & Sons.
Rolik, Y. (2017). Risk management in implementing wind energy project. Procedia Engineering, 178, 278-288.
Ross, S. M. (2014). Introduction to probability models. Academic press.
Sigaud, O., & Buffet, O. (Eds.). (2013). Markov decision processes in artificial intelligence. John Wiley & Sons.
Smith, N. J., Merna, T., & Jobling, P. (2014). Managing risk in construction projects. John Wiley & Sons.
Soltan, S., & Ashrafi, M. (2020). Predicting project duration and cost, and selecting the best action plan using statistical methods for earned value management. Journal of Project Management, 5(3), 157-166.
Sujiao, Z. (2009, July). Risk analysis of construction projects based on markov chain. In 2009 Asia-Pacific Conference on Information Processing (Vol. 1, pp. 514-517). IEEE.
Tamak, J., & Bindal, D. (2013). An Empirical Study of Risk Management & Control. International Journal of Advance Research in Computer Science and Software Engineering, 3(12), 4.
Wu, D., Dai, Q., & Zhu, X. (2016). Measuring the effect of project risks based on Shapley value for project risk re-sponse. Procedia computer science, 91, 774-778.
Wu, D., Li, J., Xia, T., Bao, C., Zhao, Y., & Dai, Q. (2018). A multiobjective optimization method considering process risk correlation for project risk response planning. Information Sciences, 467, 282-295.
Yan, H., Gao, C., Elzarka, H., Mostafa, K., & Tang, W. (2019). Risk assessment for construction of urban rail transit projects. Safety science, 118, 583-594.
Zhang, Y. (2016). Selecting risk response strategies considering project risk interdependence. International Journal of Project Management, 34(5), 819-830.
Zhang, Y., & Fan, Z. P. (2014). An optimization method for selecting project risk response strategies. International journal of project management, 32(3), 412-422.
Zhang, Y., Luan, H., Shao, W., & Xu, Y. (2016). Managerial risk preference and its influencing factors: analysis of large state-owned enterprises management personnel in China. Risk Management, 18, 135-158.
Zhang, Y., Zuo, F., & Guan, X. (2020). Integrating case-based analysis and fuzzy optimization for selecting project risk response actions. Physica A: Statistical Mechanics and Its Applications, 545, 123578.
Zhao, X., Hwang, B. G., & Gao, Y. (2016). A fuzzy synthetic evaluation approach for risk assessment: a case of Singa-pore's green projects. Journal of Cleaner Production, 115, 203-213.
Zuo, F., & Zhang, K. (2018). Selection of risk response actions with consideration of secondary risks. International Journal of Project Management, 36(2), 241-254.