A novel fuzzy Bayesian network-based approach for solving the project time-cost-quality trade-off problem

Document Type : Research Article


1 Amirkabir University of Technology, Department of Industrial Engineering and Management Systems, Tehran, Iran.

2 Amirkabir University of Technology, Department of Computer Engineering, Tehran, Iran.


To successfully complete projects, it is essential to meet all the goals of the criteria that affect the project, such as time, cost, and quality. The time-cost-quality trade-off (TCQT) approach is considered a practical technique when project managers or customers tend to crash the total time of a project and create a balance within these criteria. On the other hand, due to the unique inherent of projects and various risks in the real world, using a certain framework for project management problems does not seem efficient. This paper presents a novel fuzzy Bayesian network-based approach to schedule a project and control real-world uncertainties. This novel approach applies the fuzzy opinions of several experts with regard to their weight. The presented fuzzy Bayesian model can calculate a project’s total cost and duration in various uncertain situations. Consequently, this profound knowledge about the project’s various conditions helps managers be aware of the different probable scenarios. To demonstrate the efficiency and application of the proposed model, a modified project example from the literature review is adopted and solved. A common technique in project management called PERT is applied to verify the proposed approach, and the results are compared. Finally, a comparative analysis with a recent related paper is presented.


Main Subjects

[1]  Bettemir, Ö. H., & Birgönül, M. T. (2017). Network analysis algorithm for the solution of discrete time-cost trade-off problem. KSCE Journal of Civil Engineering, 21(4), 1047-1058.
[2] Ghosh, M., Kabir, G., & Hasin, M. A. A. (2017). Project time–cost trade-off: a Bayesian approach to update project time and cost estimates. International Journal of Management Science and Engineering Management, 12(3), 206-215.
[3] Jeunet, J., & Orm, M. B. (2020). Optimizing temporary work and overtime in the Time Cost Quality Trade-off Problem. European Journal of Operational Research284(2), 743-761.
[4]  Ballesteros-Perez, P., Elamrousy, K. M., & González-Cruz, M. C. (2019). Non-linear time-cost trade-off models of activity crashing: Application to construction scheduling and project compression with fast-tracking. Automation in Construction97, 229-240.
[5] El-Sayegh, S. M., & Al-Haj, R. (2017). A new framework for time-cost trade-off considering float loss impact. Journal of Financial Management of Property and Construction, 22(1), 20-36.
[6] Leyman, P., Van Driessche, N., Vanhoucke, M., & De Causmaecker, P. (2019). The impact of solution representations on heuristic net present value optimization in discrete time/cost trade-off project scheduling with multiple cash flow and payment models. Computers & Operations Research103, 184-197.
[7] Abdel-Basset, M., Ali, M., & Atef, A. (2020). Uncertainty assessments of linear time-cost tradeoffs using neutrosophic set. Computers & Industrial Engineering141, 106286.
[8]  Panwar, A., & Jha, K. N. (2021). Integrating Quality and Safety in Construction Scheduling Time-Cost Trade-Off Model. Journal of Construction Engineering and Management, 147(2), 04020160.
[9] Orm, M. B., & Jeunet, J. (2018). Time cost quality trade-off problems: A survey exploring the assessment of quality. Computers & Industrial Engineering, 118, 319-328.
[10]  Mohammadipour, F., & Sadjadi, S. J. (2016). Project cost-quality-risk trade-off analysis in a time-constrained problem. Computers & Industrial Engineering, 95, 111-121.
[11] Haghighi, M. H., Mousavi, S. M., Antuchevičienė, J., & Mohagheghi, V. (2019). A new analytical methodology to handle time-cost trade-off problem with considering quality loss cost under interval-valued fuzzy uncertainty. Technological and economic development of economy, 25(2), 277-299.
[12]  Hosseini-Nasab, H., Pourkheradmand, M., & Shahsavaripour, N. (2017). Solving Multi-Mode Time-Cost-Quality Trade-off Problem in Uncertainty Condition Using a Novel Genetic Algorithm. International Journal of Management and Fuzzy Systems, 3(3), 32.
[13] Chapman, C., & Ward, S. (2011). How to manage project opportunity and risk: Why uncertainty management can be a much better approach than risk management. New York, NY: Wiley.
[14] Salman, Baris. (2011). Infrastructure management and deterioration risk assessment of wastewater collection systems (PhD Dissertation). University of Cincinnati, p. 210.
[15] Naik, M., & Kumar, D. (2015). Construction project cost and duration optimization using artificial neural network. American Society of Civil Engineers, 2015, 433–444.
[16]  Ashrafi, M., & Anzabi Zadeh, S. (2017). Lifecycle risk assessment of a technological system using dynamic Bayesian networks. Quality and Reliability Engineering International, 33(8), 2497-2520.
[17] Sanchez, F., Steria, S., Bonjour, E., Micaelli, J. P., & Monticolo, D. (2020). An approach based on bayesian network for improving project management maturity: An application to reduce cost overrun risks in engineering projects. Computers in Industry, 119, 103227.
[18] Covaliu, Z., & Soyer, R. (1996). Bayesian project management. In: Procreation Conference ASA Section on Bayesian Statistical Science, pp. 208–213.
[19] Cho, S. (2009). A linear Bayesian stochastic approximation to update project duration estimates. European Journal of Operational Research, 196, 585–593.
[20] Chou, J. S., Yang, I. T., & Chong, W. K. (2009). Probabilistic simulation for developing likelihood distribution of engineering project cost. Automation in Construction, 18, 570–577.
[21] Zarei, E., Khakzad, N., Cozzani, V., & Reniers, G. (2019). Safety analysis of process systems using Fuzzy Bayesian Network (FBN). Journal of loss prevention in the process industries, 57, 7-16.
[22]  Ferreira, L., & Borenstein, D. (2012). A fuzzy-Bayesian model for supplier selection. Expert Systems with Applications, 39(9), 7834-7844.
[23]   Wan, C., Yan, X., Zhang, D., Qu, Z., & Yang, Z. (2019). An advanced fuzzy Bayesian-based FMEA approach for assessing maritime supply chain risks. Transportation Research Part E: Logistics and Transportation Review, 125, 222-240.
[24] Aliabadi, M. M., Pourhasan, A., & Mohammadfam, I. (2020). Risk modelling of a hydrogen gasholder using Fuzzy Bayesian Network (FBN). International Journal of Hydrogen Energy, 45(1), 1177-1186.
[25]  He, Z., He, H., Liu, R., & Wang, N. (2017). Variable neighbourhood search and tabu search for a discrete time-cost trade-off problem to minimize the maximal cash flow gap. Computers & Operations Research, 78(1), 564-577.
[26]  Liu, D., Li, H., Wang, H., Qi, C., & Rose, T. (2020). Discrete symbiotic organisms search method for solving large-scale time-cost trade-off problem in construction scheduling. Expert Systems with Applications, 148, 113230.
[27] Bossaghzadeh, I., Hejazi, S. R., & Pirmoradi, Z. (2015). Developing robust project scheduling methods for uncertain parameters. AUT Journal of Modeling and Simulation47(1), 21-32.
[28] Nabipoor Afruzi, E., & Aghaie, A. (2019). A Hybridized Metaheuristic Algorithm to Solve the Robust Resource Constrained Multi-Project Scheduling Problem. AUT Journal of Modeling and Simulation51(1), 15-32.
[29] Kim, J., Kang, C., & Hwang, I. (2012). A practical approach to project scheduling: considering the potential quality loss cost in the time-cost trade-off problem. International Journal of Project Management, 30(2), 264-272.
[30]  Monghasemi, S., Nikoo, M. R., Fasaee, M. A. K., & Adamowski, J. (2015). A novel multi-criteria decision- making model for optimizing time-cost-quality trade-off problems in construction projects. Expert Systems with Applications, 42(6), 3089-3104.
[31] Tran, D. H., & Long, L. D. (2018). Project scheduling with time, cost and risk trade-off using adaptive multiple objective differential evolution. Engineering, Construction and Architectural Management, 25(5), 623-638.
[32] Ashrafi, M., Davoudpour, H., & Khodakarami, V. (2015). Risk assessment of wind turbines: Transition from pure mechanistic paradigm to modern complexity paradigm. Renewable and Sustainable Energy Reviews, 51, 347-355.
[33]    Abolbashari, M. H., Chang, E., Hussain, O. K., & Saberi, M. (2018). Smart buyer: a Bayesian network modelling approach for measuring and improving procurement performance in organisations. Knowledge-Based Systems142, 127-148.
[34] Lessan, J., Fu, L., & Wen, C. (2019). A hybrid Bayesian network model for predicting delays in train operations. Computers & Industrial Engineering127, 1214-1222.
[35]  Ashrafi, M., Davoudpour, H., & Khodakarami, V. (2016). A Bayesian network based framework to evaluate reliability in wind turbines. Wind and Structures, 22(5), 543–553.
[36] Ashrafi, M., & Davoudpour, H. (2019). A hierarchical bayesian network to compare maintenance strategies based on cost and reliability: A case of onshore wind turbines. International Journal of Industrial Engineering26(3). 
[37] Malagrino, L. S., Roman, N. T., & Monteiro, A. M. (2018). Forecasting stock market index daily direction: A Bayesian Network approach. Expert Systems with Applications105, 11-22.
[38] Ojha, R., Ghadge, A., Tiwari, M. K., & Bititci, U. S. (2018). Bayesian network modelling for supply chain risk propagation. International Journal of Production Research56(17), 5795-5819.
[39]Kabir, G., Tesfamariam, S., Francisque, A., & Sadiq, R. (2015). Evaluating risk of water mains failure using a Bayesian belief network model. European Journal of Operational Research, 240, 220–234.
[40] Ashrafi, M. (2021). Forward and backward risk assessment throughout a system life cycle using dynamic Bayesian networks: A case in a petroleum refinery. Quality and Reliability Engineering International, 37(1), 309-334.
[41] Pearl, J. (1988). Probabilistic reasoning in Intelligent Systems: Networks of plausible inference. San Francisco: Morgan Kaufmann , Incorporation.
[42]  Zarei, E., Khakzad, N., Cozzani, V., & Reniers, G. (2019). Safety analysis of process systems using Fuzzy Bayesian Network (FBN). Journal of loss prevention in the process industries, 57, 7-16.
[43]  Shan, X., Liu, K., & Sun, P. L. (2017). Risk analysis on leakage failure of natural gas pipelines by fuzzy Bayesian network with a bow-tie model. Scientific programming, 2017.
[44]  Haghighi, M. H., Mousavi, S. M., & Mohagheghi, V. (2019). A new soft computing model based on linear assignment and linear programming technique for multidimensional analysis of preference with interval type-2 fuzzy sets. Applied Soft Computing77, 780-796.
[45] Haghighi, M. H., & Ashrafi, M. (2022). A new qualitative and quantitative analytical approach for risk management in energy project time-cost trade-off problem under interval type-2 fuzzy uncertainty: A case study in the gas industry. Energy Reports8, 12668-12685.
[46]  Rostamabadi, A., Jahangiri, M., Zarei, E., Kamalinia, M., & Alimohammadlou, M. (2020). A novel Fuzzy Bayesian Network approach for safety analysis of process systems; An application of HFACS and SHIPP methodology. Journal of Cleaner Production244, 118761.
[47]  Lavasani, S. M., Ramzali, N., Sabzalipour, F., & Akyuz, E. (2015). Utilisation of Fuzzy Fault Tree Analysis (FFTA) for quantified risk analysis of leakage in abandoned oil and natural-gas wells. Ocean Engineering108, 729-737.
[48] Yucesan, M., Gul, M., & Celik, E. (2021). A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method. Complex & Intelligent Systems, 1-18.
[49]Onisawa, T. (1988). An approach to human reliability in man-machine systems using error possibility. Fuzzy sets and Systems27(2), 87-103.