Abstract
This paper proposes a hybrid process based on the Analytical Hierarchy Process (AHP), risk matrix, and Monte Carlo Simulation for prioritizing delay risks in construction projects and estimating the probability of timely project completion. The main contribution of this study is the development of a novel framework for risk assessment that quantifies the impact of more critical risks to each activity according to its nature and benefits from the advantages of the AHP for constructing an enhanced risk matrix. In addition to the above, the proposed framework exploits the computational potential of Monte Carlo Simulation to obtain more accurate predictions of a project’s completion time. The proposed approach was used for analyzing the duration, expected delay, and the probability of completion within the deadline, of a vital seawater desalination plant construction project, on the island of Alonissos, Greece. The proposed framework predicted the project duration more accurately than the classic PERT method did, and the results of the analysis documented that it could be a simple tool providing risk managers with helpful information while making decisions under uncertainty.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data Availability
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
References
Afzal F, Yunfei S, Junaid D, Hanif MS (2020) Cost-risk contingency framework for managing cost overrun in metropolitan projects: using fuzzy-AHP and simulation. Int J Manag Proj Bus 13:1121–1139. https://doi.org/10.1108/IJMPB-07-2019-0175
Albogamy A, Dawood N (2015) Development of a client-based risk management methodology for the early design stage of construction processes. Eng Constr Archit Manag 22:493–515. https://doi.org/10.1108/ECAM-07-2014-0096
Attarzadeh M, Kim Huat Chua D, Beer M (2011) Risk Management of Asalouye Desalination Project. In: First International Symposium on Uncertainty Modeling and Analysis and Management (ICVRAM 2011); and Fifth International Symposium on Uncertainty Modeling and Analysis (ISUMA). Hyattsville, Maryland, United States, pp 805–812
Aven T (2016) Risk assessment and risk management: Review of recent advances on their foundation. Eur J Oper Res 253:1–13. https://doi.org/10.1016/j.ejor.2015.12.023
Baghapour MA, Shooshtarian MR, Zarghami M (2020) Process Mining Approach of a New Water Quality Index for Long-Term Assessment under Uncertainty Using Consensus-Based Fuzzy Decision Support System. Water Resour Manag 34:1155–1172. https://doi.org/10.1007/s11269-020-02489-5
Bamakan SMH, Dehghanimohammadabadi M (2015) A Weighted monte carlo simulation approach to risk assessment of information security management system. Int J Enterp Inf Syst 11:63–78. https://doi.org/10.4018/IJEIS.2015100103
Barraza GA (2011) Probabilistic Estimation and Allocation of Project Time Contingency. J Constr Eng Manag 137:259–265. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000280
Bianco L, Caramia M, Giordani S (2019) A chance constrained optimization approach for resource unconstrained project scheduling with uncertainty in activity execution intensity. Comput Ind Eng 128:831–836. https://doi.org/10.1016/j.cie.2018.11.053
Bouamri M, Bouabdesselam H (2018) Risk analysis in seawater desalination sector: a case study of Beni Saf Water Company “BWC. Adv Syst Sci Appl 18:93–106. https://doi.org/10.25728/assa.2018.18.2.531
Dong D, Sun W, Zhu Z et al (2013) Groundwater Risk Assessment of the Third Aquifer in Tianjin City, China. Water Resour Manag 27:3179–3190. https://doi.org/10.1007/s11269-013-0342-z
Dweiri F, Khan SA, Almulla A (2018) A multi-criteria decision support system to rank sustainable desalination plant location criteria. Desalination 444:26–34. https://doi.org/10.1016/j.desal.2018.07.007
Elshaer R (2013) Impact of sensitivity information on the prediction of project’s duration using earned schedule method. Int J Proj Manag 31:579–588. https://doi.org/10.1016/j.ijproman.2012.10.006
Fathy Awwad A (2018) Risk Assessment and Control for Main Hazards in Reverse Osmosis Desalination Plants. Ind Eng 2:1. https://doi.org/10.11648/j.ie.20180201.11
Fu S, Zhang D, Zhang M, Yan X (2017) Identification of environmental risk influencing factors for ship operations in Arctic waters. Harbin Gongcheng Daxue Xuebao/Journal Harbin Eng Univ 38:1682–1688. https://doi.org/10.11990/jheu.201606050
Helbig C, Bradshaw AM, Kolotzek C et al (2016) Supply risks associated with CdTe and CIGS thin-film photovoltaics. Appl Energy 178:422–433. https://doi.org/10.1016/j.apenergy.2016.06.102
Kim MH, Lee EB, Choi HS (2019) A forecast and mitigation model of construction performance by assessing detailed engineering maturity at key milestones for offshore EPC mega-projects. Sustain 11. https://doi.org/10.3390/su11051256
Kirytopoulos KA, Leopoulos VN, Diamantas VK (2008) PERT vs. Monte Carlo Simulation along with the suitable distribution effect. Int J Proj Organ Manag 1:24–46. https://doi.org/10.1504/IJPOM.2008.020027
Koulinas GK, Xanthopoulos AS, Tsilipiras TT, Koulouriotis DE (2020) Schedule Delay Risk Analysis in Construction Projects with a Simulation-Based Expert System. Buildings 10. https://doi.org/10.3390/buildings10080134
Li F, Zhao Y, Feng P et al (2015) Risk Assessment of Groundwater and its Application. Part I: Risk Grading Based on the Functional Zoning of Groundwater. Water Resour Manag 29:2697–2714. https://doi.org/10.1007/s11269-015-0964-4
Luthra S, Mangla SK, Venkatesh VG, Jakhar SK (2018) Management of risks in sustainable supply chain using AHP and monte carlo simulation. Global Business Expansion: Concepts, Methodologies, Tools, and Appl. Government Engineering College Nilokheri, India, pp 1633–1652
Negahban A (2018) Optimizing consistency improvement of positive reciprocal matrices with implications for Monte Carlo Analytic Hierarchy Process. Comput Ind Eng 124:113–124. https://doi.org/10.1016/j.cie.2018.07.018
Ntzeremes P, Kirytopoulos K (2018) Applying a stochastic-based approach for developing a quantitative risk assessment method on the fire safety of underground road tunnels. Tunn Undergr Sp Technol 81:619–631. https://doi.org/10.1016/j.tust.2018.08.020
Organization WH (2011) Safe Drinking-water from Desalination. 1–34
PMI (2017) PMBOK Guide – Sixth Edition
Rausch C, Nahangi M, Haas C, Liang W (2019) Monte Carlo simulation for tolerance analysis in prefabrication and offsite construction. Autom Constr 103:300–314. https://doi.org/10.1016/j.autcon.2019.03.026
Rees M (2015) Business Risk and Simulation Modelling in Practice Using Excel, VBA and @RISK. John Wiley & Sons Ltd
Saaty TL (1990) How to make a decision: The analytic hierarchy process. Eur J Oper Res 48:9–26. https://doi.org/10.1016/0377-2217(90)90057-I
Spanidis PM, Roumpos C, Pavloudakis F (2021) A fuzzy-ahp methodology for planning the risk management of natural hazards in surface mining projects. Sustain 13:1–23. https://doi.org/10.3390/su13042369
Tapia JFD, Promentilla MAB, Tseng ML, Tan RR (2017) Screening of carbon dioxide utilization options using hybrid Analytic Hierarchy Process-Data Envelopment Analysis method. J Clean Prod 165:1361–1370. https://doi.org/10.1016/j.jclepro.2017.07.182
Vanhoucke M (2011) On the dynamic use of project performance and schedule risk information during project tracking. Omega 39:416–426. https://doi.org/10.1016/j.omega.2010.09.006
Wang C, Jiao B, Guo L et al (2016) Robust scheduling of building energy system under uncertainty. Appl Energy 167:366–376. https://doi.org/10.1016/j.apenergy.2015.09.070
Yi D, Lee EB, Ahn J (2019) Onshore Oil and Gas Design Schedule Management Process Through Time-Impact Simulations Analyses. Sustain. 11
Yu X, Liang W, Zhang L et al (2018) Risk assessment of the maintenance process for onshore oil and gas transmission pipelines under uncertainty. Reliab Eng Syst Saf 177:50–67. https://doi.org/10.1016/j.ress.2018.05.001
Zhang Y, Wang R, Huang P et al (2020) Risk evaluation of large-scale seawater desalination projects based on an integrated fuzzy comprehensive evaluation and analytic hierarchy process method. Desalination 478:114286. https://doi.org/10.1016/j.desal.2019.114286
Zhong M, Wang J, Gao L et al (2019) Fuzzy Risk Assessment of Flash Floods Using a Cloud-Based Information Diffusion Approach. Water Resour Manag 33:2537–2553. https://doi.org/10.1007/s11269-019-02266-z
Funding
This research received no external funding.
Author information
Authors and Affiliations
Contributions
Conceptualization, G.Koulinas, D.Koulouriotis; data curation, G.Koulinas, A.Xanthopoulos, K.Sidas; formal analysis, G.Koulinas, K.Sidas, D.Koulouriotis; investigation, G.Koulinas, K.Sidas; methodology, G.Koulinas, A.Xanthopoulos, D.Koulouriotis; writing—original draft, G.Koulinas; writing—review and editing, G.Koulinas, A.Xanthopoulos, D.Koulouriotis.
All authors have read and agreed to the published version of the manuscript.
Corresponding author
Ethics declarations
Conflicts of Interest
We declare no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Koulinas, G.K., Xanthopoulos, A.S., Sidas, K. et al. Risks Ranking in a Desalination Plant Construction Project with a Hybrid AHP, Risk Matrix, and Simulation-Based Approach. Water Resour Manage 35, 3221–3233 (2021). https://doi.org/10.1007/s11269-021-02886-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11269-021-02886-4