Journal of Quality Engineering and Production Optimization

Journal of Quality Engineering and Production Optimization

A hybrid decision analysis-based soft computing approach to solve ppp project selection problem with incomplete information

Document Type : Research Paper

Authors
1 Department of Civil Engineering, Qeshm Branch, Islamic Azad University, Qeshm, Iran
2 Associate professor, Technical and Engineering Faculty, University of Qom, Qom, Iran
3 Assistant professor, Department of Architecture, Science and research Branch, Islamic Azad University, Tehran, Iran
Abstract
Nowadays, governments are cooperating with the private sector under the name of public-private partnership (3P) projects in order to reduce the risks of projects and to achieve the ultimate goal, which is the construction and operation of the project. In order to help decision makers and managers to make decisions in 3P projects, this article examines the financial challenges of the project in uncertain conditions with an intuitive fuzzy approach. The intuitionistic fuzzy multi-criteria weighting and ranking model (IF-MCWR) proposed method based on the intuitionistic fuzzy values to compute the weighting and ranking of the criteria. In process of the proposed IF-MCWR model, criteria's weights are generated to reduce the errors. Also, criteria with optimal weights are obtained based on IF Hamming distance measure by utilizing an extended maximizing deviation approach. Furthermore, judgments of decision makers (DMs) and experts are taken into account for calculating the criteria’ weights. Nevertheless, weights of experts are shown based on proposed new IF technique for order performance by similarity to ideal solution method. Then, based on hamming distance, a new IF index measure is proposed to obtain the relative closeness coefficient for ranking the candidates or alternatives. By doing this, project managers can make decisions with a more confident view. Finally, a case study is presented that examines the issue of urban development and presents the option of constructing a highway project as the most important and highest ranking among the projects that brings the most financial challenge.
Keywords

Abbaspour Ghadim Bonab, A., & Yousefi Nejad Attari, M. (2022). A predictive data-driven state-dependent decision approach to determine inventory system states for critical spare parts. Journal of Quality Engineering and Production Optimization, 1(2), pp.1–13.
Adwan, O., Faris, H., Jaradat, K., Harfoushi, O., & Ghatasheh, N. (2014). Predicting customer churn in the telecom industry using multilayer perceptron neural networks: Modeling and analysis. Journal of Computer Science and Technology, 11(3), pp.75–81.
Alsiehemy, A. (2019). An Assessment of Customer Retention with Self-Service Technologies: A Model Development. Business Management and Strategy, 10(1), pp.78–92.
Amin, A., Anwar, S., Adnan, A., Nawaz, M., Alawfi, K., Hussain, A., & Huang, K. (2017). Customer churn prediction in the telecommunications sector using a rough set approach. Neurocomputing, 237, pp.242–254.
Azizi, M. (2020). Atomic orbital search: A novel metaheuristic algorithm. Applied Mathematical Modelling, 89(1), pp.123–134.
Azizi, M., Talatahari, S., Khodadadi, N., & Sareh, P. (2022). Multiobjective Atomic Orbital Search (MOAOS) for Global and Engineering Design Optimization. IEEE Access, 10, pp. 67727–67746.
Behdinian, A., Amani, M. A., Aghsami, A., & Jolai, F. (2022). An Integrating Machine Learning Algorithm and Simulation Method for Improving Software Project Management: A Case Study. Journal of Quality Engineering and Production Optimization, 1(2), pp.1–11.
Bhushan, S. B. (2024). Enhancing Customer Churn Prediction in the Telecom Sector Using Advanced Machine Learning Techniques and Explainable AI [Master's Research Project, National College of Ireland]. National College of Ireland Institutional Repository (NORMA). https://norma.ncirl.ie/8683/
Bi, W., Cai, M., Liu, M., & Li, G. (2016). A big data clustering algorithm for mitigating the risk of customer churn. IEEE Transactions on Industrial Informatics, 12(3), pp.1270–1281.
Boobier, T. (2018). Advanced analytics and AI: Impact, implementation, and the future of work. Hoboken, NJ: John Wiley & Sons.
Chai, E., Khadullo, K., & Tole, K. (2025). Enhancing customer churn prediction: Addressing disparities and imbalances in machine learning models. Journal of Machine Learning Research, 13(3), pp.129–148.
Chugh, A., Sharma, V. K., Bhatia, M. K., & Jain, C. (2022). A big data query optimization framework for telecom customer churn analysis. In A. Khanna, S. Bhattacharyya, S. Anand, D. Gupta, A. E. Hassanien, & A. Jaiswal (Eds.), Advances in Intelligent Systems and Computing (pp. 475–484). Singapore: Springer.
Deng, W., Deng, L., Liu, J., & Qi, J. (2019). Sampling method based on an improved C4.5 decision tree and its application in the prediction of telecom customer churn. International Journal of Information Technology and Management, 18(1), pp.93–109.
Ever, Y. K., Dimililer, K., & Sekeroglu, B. (2019). Comparison of machine learning techniques for prediction problems. In L. Barolli, M. Takizawa, F. Xhafa, & T. Enokido (Eds.), Web, Artificial Intelligence and Network Applications: Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications (WAINA-2019) (pp. 713–723). Matsue, Japan: Springer.
Géron, A. (2022). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (3rd ed.). Sebastopol, CA: O'Reilly Media.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press.
Han, H., Back, K.-J., & Barrett, B. (2009). Influencing factors on restaurant customers’ revisit intention: The roles of emotions and switching barriers. International Journal of Hospitality Management, 28(4), pp.563–572.
Huettmann, F., Craig, E. H., Herrick, K. A., Baltensperger, A. P., Humphries, G. R. et al. (2018). Use of machine learning (ML) for predicting and analyzing ecological and ‘presence only’ data: An overview of applications and a good outlook. In F. Huettmann, D. J. Lieske, S. N. R. Mohseni, E. H. Craig, & S. C. L. Li (Eds.), Machine Learning for Ecology and Sustainable Natural Resource Management (pp. 27–61). Cham: Springer.
Hossein Khani, N., Hosseini Motlagh, S. M., & Khakzar Bafruei, M. (2014). Identification of factors affecting customer churn in the insurance industry. Paper presented at the 21st National and 7th International Insurance and Development Conference, Tehran, Iran. SID. https://sid.ir/paper/833543/fa
Lalwani, P., Mishra, M. K., Chadha, J. S., & Sethi, P. (2021). Customer churn prediction system: a machine learning approach. Computing, 104, pp.271–294.
Lee, J., Lee, J., & Feick, L. (2001). The impact of switching costs on the customer satisfaction‐loyalty link: Mobile phone service in France. Journal of Service Management, 15(1), pp.35–48.
Manzoor, A., Qureshi, M. A., Kidney, E., & Longo, L. (2024). A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners. IEEE Access, 12, pp.70434–70445.
Maskale, V., Vaidya, V., Patil, Y., Bagal, Y., & Dhamdhre, V. (2024, April). To Design and implement an Application for Bank Customer Churning Rate Prediction and Analysis using a Machine Learning Algorithm. In 2024, MIT Art, Design and Technology School of Computing International Conference (MITADTSoCiCon) (pp. 475–484). Pune, India: IEEE.
Movafaghpour, M. A. (2023). Predicting Project Delays Using a New Trended Regression Tree Method. Journal of Quality Engineering and Production Optimization, 2(1), pp.29–41.
Neslin, S. A., Gupta, S., Kamakura, W., Lu, J., & Mason, C. H. (2006). Defection detection: Measuring and understanding the predictive accuracy of customer churn models. Journal of Marketing Research, 43(2), pp.204–211.
Nielsen, M. A. (2015). Neural Networks and Deep Learning. Determination Press.
Noe, R. A., Hollenbeck, J. R., Gerhart, B., & Wright, P. M. (2017). Human resource management: Gaining a competitive advantage. New York, NY: McGraw-Hill Education.
Pustokhina, I. V., Pustokhin, D. A., Aswathy, R., Jayasankar, T., Jeyalakshmi, C., & Díaz, V. G. (2021). Dynamic customer churn prediction strategy for business intelligence using text analytics with evolutionary optimization algorithms. Computers & Industrial Engineering, 162, 107606.
Rai, S., Khandelwal, N., & Boghey, R. (2020). Analysis of customer churn prediction in the telecom sector using the CART algorithm. In A. K. Luhach, J. A. Kosa, R. C. Poonia, X.-Z. Gao, & D. Singh (Eds.), Sustainable Technologies for Computational Intelligence: Proceedings of ICSTCI 2020 (AISC, Vol. 1045, pp. 457–466). Ghaziabad, India: Springer.
Ramesh, P., Jeba Emilyn, J. & Vijayakumar, V. (2022). Hybrid Artificial Neural Networks Using Customer Churn Prediction. Wireless Personal Communications, 124, pp.1695–1709.
Rani, K. S., Thaslima, S., Prasanna, N. G., Vindhya, R., & Srilakshmi, P. (2021). Analysis of Customer Churn Prediction in Telecom Industry Using Logistic Regression. International Journal of Innovative Research in Computer Science & Technology, 9(4), pp.2347–5552.
Ren, C. R., Hu, Y., & Cui, T. (2019). Responses to rival exit: Product variety, market expansion, and preexisting market structure. Strategic Management Journal, 40(2), pp.253–276.
Sagming, M., Heymann, R., & Visaya, M. V. (2025). Using topological data analysis and machine learning to predict customer churn. Journal of Big Data, 12(4), pp.320–338.
Sato, T., Huang, B. Q., Huang, Y., Kechadi, M.-T., & Buckley, B. (2010). Using PCA to predict customer churn in a telecommunication dataset. In L. Cao, Y. Liu, C. Zhang, & H. Zhu (Eds.), Advanced Data Mining and Applications: 6th International Conference, ADMA 2010 (Lecture Notes in Computer Science, Vol. 6460, pp. 198–208). Chongqing, China: Springer.
Zhang, T., Moro, S., & Ramos, R. F. (2025). A data-driven approach to improve customer churn prediction based on telecom customer segmentation. Journal of Machine Learning Research, 13(5), pp.213–229.