Multi-Objective Optimization of Oil and Gas Project Portfolios Using Metaheuristic Algorithms and Pareto Front
Keywords:
Project Portfolio Management, Metaheuristic Algorithms, Multi-Objective Optimization, Pareto Front, Risk Management, Oil and Gas ProjectsAbstract
Portfolio management in the oil and gas industry requires multi-objective optimization to balance economic, social, environmental, and risk criteria while respecting resource constraints. This study aims to optimize a portfolio of 18 oil and gas construction projects using metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Simulated Annealing (SA). Data were collected through a standardized questionnaire encompassing eight dimensions (technical, spatial, economic, temporal, environmental, risk and safety, social, and operational) and weighted using the Analytic Hierarchy Process (AHP). The Genetic Algorithm generated a Pareto front and identified an optimal portfolio with an overall score of 2.9, economic index of 0.85, social index of 0.75, environmental index of 0.80, and risk index of 0.40. Comparison with a multi-criteria weighting method revealed that metaheuristic algorithms excel in providing non-dominated solutions, whereas the multi-criteria method suggested a portfolio with a higher economic index (0.90) due to its focus on profitability. The Pareto front, visualized using Plotly, illustrates the trade-offs among objectives and supports strategic decision-making. This approach enhances resource allocation and strengthens sustainability. Future research can explore hybrid algorithms and incorporate more real-world data.
References
[1] E. E. Cihan, C. A. Uslu, and Ö. Kabak, "Hyper-project portfolio management: Post-merger project portfolios," Management Decision, vol. 61, no. 8, pp. 2280-2301, 2023, doi: 10.1108/MD-06-2022-0853.
[2] E. E. Cihan, Ç. Alabaş-Uslu, and Ö. Kabak, "Initial preference algorithm of industrial project portfolio," Kybernetes, vol. 52, no. 12, pp. 6025-6048, 2023, doi: 10.1108/K-04-2022-0644.
[3] L. Bai, Y. An, and Y. Sun, "Measurement of project portfolio benefits with a GA-BP neural network group," IEEE Transactions on Engineering Management, vol. 71, pp. 4737-4749, 2023, doi: 10.1109/TEM.2023.3236956.
[4] Y. Tian, L. Bai, L. Wei, K. Zheng, and X. Zhou, "Modeling for project portfolio benefit prediction via a GA-BP neural network," Technological Forecasting and Social Change, vol. 183, p. 121939, 2022, doi: 10.1016/j.techfore.2022.121939.
[5] A. A. Ramedani, H. Didehkhani, and A. Mehrabian, "Scenario-based optimization robust model project portfolio selection under risk considerations," Neural Computing and Applications, vol. 34, no. 23, pp. 20589-20609, 2022, doi: 10.1007/s00521-022-07434-8.
[6] A. Panahi, A. Habibirad, and S. Safari, "Developing a mathematical programming model to determine the optimal portfolio of capital projects in oil and gas companies to achieve the strategic goals," Petroleum Business Review, vol. 7, no. 2, pp. 15-30, 2023.
[7] R. Rajabi and S. Haj Yakhchali, "Optimization of cash flow in project portfolio considering market indicators using genetic algorithms and particle swarm optimization," Journal of Structural and Construction Engineering, vol. 9, no. 9, pp. 103-120, 2022, doi: 10.22065/jsce.2022.310778.2609.
[8] M. K. Nielsen, A. M. Jacobsen, J. L. Carstensen, M. T. Nielsen, and T. Tambo, "Industrial R&D project portfolio selection method using a multi-objective optimization program: A conceptual quantitative framework," Journal of Industrial Engineering and Management, vol. 17, no. 1, pp. 217-234, 2024, doi: 10.3926/jiem.6552.
[9] M. Mombini, M. Babakhani, and M. Sabzehparvar, "Evaluation and prioritization of project portfolios during execution for resource allocation: Approach of earned value management and analytic hierarchy process; case study," in 12th International Conference on Industrial Engineering, 2015. [Online]. Available: https://civilica.com/doc/516244.
[10] N. Raad, M. Shirazi, and S. Ghodsypour, "Selecting a portfolio of projects considering both optimization and balance of sub-portfolios," Journal of Project Management, vol. 5, no. 1, pp. 1-16, 2020, doi: 10.5267/j.jpm.2019.8.003.
[11] M. Poursekazemi, M. Fattahi, S. Mazhari, and B. Asadi, "Optimization of project portfolios with interdependent effects using the imperialist competitive algorithm (ICA)," Industrial Management Journal, vol. 5, no. 1, pp. 1-20, 2013, doi: 10.22059/imj.2013.35680.
[12] C. Micán, G. Fernandes, and M. Araújo, "Project portfolio risk management: A structured literature review with future directions for research," International Journal of Information Systems and Project Management, vol. 8, no. 3, pp. 67-84, 2020, doi: 10.12821/ijispm080304.
[13] L. Bai, X. Han, H. Wang, K. Zhang, and Y. Sun, "A method of network robustness under strategic goals for project portfolio selection," Computers & Industrial Engineering, vol. 161, p. 107658, 2021, doi: 10.1016/j.cie.2021.107658.
[14] A. RezaHoseini, S. F. Ghannadpour, and M. Hemmati, "A comprehensive mathematical model for resource-constrained multi-objective project portfolio selection and scheduling considering sustainability and projects splitting," Journal of Cleaner Production, vol. 269, p. 122073, 2020, doi: 10.1016/j.jclepro.2020.122073.
[15] M. Saiz, M. A. Lostumbo, A. A. Juan, and D. Lopez-Lopez, "On the use of simulation-optimization in sustainability aware project portfolio management," in 2020 Winter Simulation Conference (WSC), 2020, doi: 10.1109/WSC48552.2020.9383941.
[16] M. Z. Wu, "Portfolio Optimization Under ESG Constraints: Markowitz Model vs. Index Model," SHS Web of Conferences, vol. 218, p. 02002, 2025, doi: 10.1051/shsconf/202521802002.
[17] C. C. Wittebrood, "Digitizing project portfolio management in the architecture, engineering & construction industry," TU Delft, 2020.
[18] F. Nsubuga, "Towards digital project portfolio management in construction: Challenges and opportunities for public clients," ed, 2024.
[19] A. Zaidouni, M. A. J. Idrissi, and A. Bellabdaoui, "A PRISMA systematic review for intelligent IS/IT project portfolio dashboard from the value, prioritization and risk perspectives," in 2024 4th International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET), 2024, doi: 10.1109/IRASET60544.2024.10548997.
[20] R. A. Pinto, F. Bernardini, and R. M. Marcacini, "aePPM-HC: A method for helping project portfolio management based on topic hierarchy learnings," International Journal of Business Intelligence and Data Mining, vol. 18, no. 3, pp. 364-382, 2021, doi: 10.1504/IJBIDM.2021.114469.
[21] L. Bai, S. Liu, Y. An, and Q. Xie, "A pruned GA-BPNN model for project portfolio benefit evaluation considering ambidexterity," Kybernetes, 2024, doi: 10.1108/K-04-2024-0982.
[22] Z. Zhu, "Selection of projects for project portfolio using fuzzy TOPSIS and machine learning," ed, 2021.
[23] M. T. Elnabwy, D. Khalaf, E. A. Mlybari, and E. Elbeltagi, "An integrated machine learning approach for evaluating critical success factors influencing project portfolio management adoption in the construction industry," Engineering, Construction and Architectural Management, 2024, doi: 10.1108/ECAM-05-2024-0537.
[24] S. H. Hosseini, H. Shakouri G, A. Kazemi, R. Zareayan, and M. Mousavian H, "A system dynamics investigation of project portfolio management evolution in the energy sector: Case study: An Iranian independent power producer," Kybernetes, vol. 49, no. 2, pp. 505-525, 2020, doi: 10.1108/K-12-2018-0688.
[25] P. Lönnrot, "Portfolio optimization with AI: Evaluating Performance Beyond Traditional Techniques," ed, 2025.
[26] L. A. Mantshimuli and J. W. Muteba Mwamba, "Few-Shot Portfolio Optimization: Can Large Language Models Outperform Quantitative Portfolio Optimization? A Comparative Study of LLMs and Optimized Portfolio Allocators," (in English), Journal of Risk and Financial Management, vol. 19, no. 5, p. 320, 2026, doi: 10.3390/jrfm19050320.
[27] S. Mohd, M. Dasgupta, F. Akter, M. P. Arunachalam, and S. Sharma, "Quantum computing applications in financial modeling and portfolio optimization," 2025: IEEE, pp. 868-872, doi: 10.1109/CE2CT64011.2025.10939675.
[28] L. M. Moreira, I. L. d. Santos, and P. H. González, "Portfolio Optimization for Pension Purposes: Literature Review," Journal of Economic Surveys, vol. 40, no. 1, pp. 45-72, 2025, doi: 10.1111/joes.12702.
[29] D. Song, Machine Learning for Price Prediction and Risk -Adjusted Portfolio Optimization in Cryptocurrencies (In Machine Learning and Modeling Techniques in Financial Data Science). 2025, pp. 321-356.
[30] N. Shahbazi and S. Barkhordari, "Optimizing a portfolio comprising selected stocks and cryptocurrencies," Budget and Finance Strategic Research, vol. 6, no. 2, pp. 11-35, 2025.
[31] M. Eskandari, A. Mohseni, and M. Ghasemi, "The Effect of Behavioral Biases on Portfolio Optimization of Investors in the Tehran Stock Exchange: An Approach Based on Modern Behavioral Finance," Accounting, Finance, and Computational Intelligence, pp. 1-17, 2026.
Downloads
Published
Submitted
Revised
Accepted
Issue
Section
License
Copyright (c) 2025 Mahdi Arefnazari (Author); Aliasghar Amirkardoust (Corresponding author); Babak Aminnejad, Alireza Lork, Mohammad Taghi Banki (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
