Advances in Industrial Engineering

Advances in Industrial Engineering

Designing a Multi-Objective Programming Model for Hydrocarbon Products Using a Fuzzy Credibility-Constrained Programming-Benders Decomposition Algorithm

Document Type : Research Paper

Authors
Alireza Hamidieh 1
Abdollah Divanipour 2
bahareh akhgari 2
1 Assistant Professor, Department of Industrial Engineering, Payame Noor University (PNU), Tehran, Iran.
2 Ph.D. Candidate, Department of Industrial Engineering, Payame Noor University (PNU), Tehran, Iran.
10.22059/aie.2024.374281.1894
Abstract
The hydrocarbon supply chain (HCSC) is integral to the world economy. This chain includes petroleum products extraction, refinement, distribution, and consumption. Considering the importance of planning HCSC for the chain’s activities, it is necessary to simultaneously review and optimize these activities by incorporating important and influential factors. The problem considered in this research has three objectives: 1) maximizing profits, 2) minimizing withdrawal from reservoirs, and 3) minimizing greenhouse gas emissions. The results demonstrated that the profit level in a specific time (10 periods) improved by 18% compared to the current point. In addition, a numerical example was used to simulate distribution, refinement, and extraction locations as a supply chain for petroleum products. Finally, the sensitivity analysis revealed that the optimization results are robust to parameter changes and can further improve the optimization of the oil and gas supply chain by maintaining different balances (e.g., natural resources) and reducing environmental effects. Interactive fuzzy programming based on credibility criteria was applied to address the parameter uncertainty. Further, to reduce the problem’s computational complexity and produce valid and reliable optimal Pareto cuts, the Benders decomposition method has been employed, which has led to the production of efficient solutions.
Keywords
Fuzzy Programming
Uncertainty
Benders Decomposition
Subjects
  1. Jiao, J. L., Zhang, J. L., & Tang, Y. S. (2010, May). A model for the optimization of the petroleum supply chain in China and its empirical analysis. In 2010 International conference on e-business and e-government (pp. 3327-3330). IEEE.
  2. Chen, J., Lu, J., & Qi, S. (2010, August). Transportation network optimization of import crude oil in China based on minimum logistics cost. In 2010 IEEE International Conference on Emergency Management and Management Sciences (pp. 335-338). IEEE.
  3. Lu, M. (2010). Rock engineering problems related to underground hydrocarbon storage. Journal of Rock Mechanics and Geotechnical Engineering, 2(4), 289-297.
  4. Susarla, N., & Karimi, I. A. (2012). Intelligent Decision-Support Tools for Effective and Integrated Operational Planning in Pharmaceutical Plants. In Computer Aided Chemical Engineering (Vol. 31, pp. 1165-1169). Elsevier.
  5. Gupta, V., & Grossmann, I. E. (2012). An efficient multiperiod MINLP model for optimal planning of offshore oil and gas field infrastructure. Industrial & Engineering Chemistry Research, 51(19), 6823-6840.
  6. Aizemberg, L., Kramer, H. H., Pessoa, A. A., & Uchoa, E. (2014). Formulations for a problem of petroleum transportation. European Journal of Operational Research, 237(1), 82-90.
  7. Nasab, N. M., & Amin-Naseri, M. R. (2016). Designing an integrated model for a multi-period, multi-echelon and multi-product petroleum supply chain. Energy, 114, 708-733.
  8. Liang, C., Li, M., Lu, B., Gu, T., Jo, J., & Ding, Y. (2017). Dynamic configuration of QC allocating problem based on multi-objective genetic algorithm. Journal of Intelligent Manufacturing, 28, 847-855.
  9. Rocha, R., Grossmann, I. E., & de Aragão, M. V. P. (2017). Petroleum supply planning: reformulations and a novel decomposition algorithm. Optimization and Engineering, 18, 215-240.
  10. Ghaithan, A. M., Attia, A., & Duffuaa, S. O. (2017). Multi-objective optimization model for a downstream oil and gas supply chain. Applied Mathematical Modelling, 52, 689-708.
  11. Rahimi, M., Shahhosseini, S., Sobati, M. A., Movahedirad, S., Khodaei, B., & Hassanzadeh, H. (2019). A novel multi-probe continuous flow ultrasound assisted oxidative desulfurization reactor; experimental investigation and simulation. Ultrasonics Sonochemistry, 56, 264-273.
  12. Attia, A. M., Ghaithan, A. M., & Duffuaa, S. O. (2019). A multi-objective optimization model for tactical planning of upstream oil & gas supply chains. Computers & chemical engineering, 128, 216-227.
  13. Kumar, S., & Mahapatra, R. P. (2021). Design of multi-warehouse inventory model for an optimal replenishment policy using a rain optimization algorithm. Knowledge-Based Systems, 231, 107406.
  14. Li, F., Qian, F., Du, W., Yang, M., Long, J., & Mahalec, V. (2021). Refinery production planning optimization under crude oil quality uncertainty. Computers & Chemical Engineering, 151, 107361.
  15. Ge, C., & Yuan, Z. (2021). Production scheduling for the reconfigurable modular pharmaceutical manufacturing processes. Computers & Chemical Engineering, 151, 107346.
  16. Sahoo, D., Tripathy, A. K., & Pati, J. K. (2022). Study on multi-objective linear fractional programming problem involving pentagonal intuitionistic fuzzy number. Results in Control and Optimization, 6, 100091.
  17. Zhao, F., Liu, Y., Lu, N., Xu, T., Zhu, G., & Wang, K. (2021). A review on upgrading and viscosity reduction of heavy oil and bitumen by underground catalytic cracking. Energy Reports, 7, 4249-4272.
  18. Buslaev, G., Morenov, V., Konyaev, Y., & Kraslawski, A. (2021). Reduction of carbon footprint of the production and field transport of high-viscosity oils in the Arctic region. Chemical Engineering and Processing-Process Intensification, 159, 108189.
  19. Pettersson, M., Olofsson, J., Börjesson, P., & Björnsson, L. (2022). Reductions in greenhouse gas emissions through innovative co-production of bio-oil in combined heat and power plants. Applied Energy, 324, 119637.
  20. Scrimieri, D., Adalat, O., Afazov, S., & Ratchev, S. (2022). Modular reconfiguration of flexible production systems using machine learning and performance estimates. IFAC-PapersOnLine, 55(10), 353-358.
  21. Komesker, S., Motsch, W., Popper, J., Sidorenko, A., Wagner, A., & Ruskowski, M. (2022). Enabling a multi-agent system for resilient production flow in modular production systems. Procedia CIRP, 107, 991-998.
  22. Alnaqbi, A., Dweiri, F., & Chaabane, A. (2022). Impact of horizontal mergers on supply chain performance: The case of the upstream oil and gas industry. Computers & Chemical Engineering, 159, 107659.
  23. Motahari, R., Alavifar, Z., Andaryan, A. Z., Chipulu, M., & Saberi, M. (2023). A multi-objective linear programming model for scheduling part families and designing a group layout in cellular manufacturing systems. Computers & Operations Research, 151, 106090.
  24. Sang, M., Ding, Y., Bao, M., Song, Y., & Wang, P. (2022). Enhancing resilience of integrated electricity-gas systems: A skeleton-network based strategy. Advances in Applied Energy, 7, 100101.
  25. Vafadarnikjoo, A., Moktadir, M. A., Paul, S. K., & Ali, S. M. (2023). A novel grey multi-objective binary linear programming model for risk assessment in supply chain management. Supply Chain Analytics, 2, 100012.
  26. AlEdan, A. B., & Erfani, T. (2023). Sustainable produced water supply chain design and optimisation: Trading-off the economic cost and environmental impact in Kuwait oil company. Journal of Cleaner Production, 136185.
  27. Kumar, N., Tyagi, M., Sachdeva, A., & Walia, R. S. (2023). Analyzing the thermal, economic, and environmental dynamics of phase change materials used in cold chain applications. Materials Today: Proceedings.
  28. Wang, J., Swartz, C. L., & Huang, K. (2023). Deep learning-based model predictive control for real-time supply chain optimization. Journal of Process Control, 129, 103049.
  29. Ratner, S., Balashova, S., Revinova, S. (2024). Assessing the sustainability of hydrogen supply chains using network Data Envelopment Analysis. Procedia Computer Science, Volume 232, 2024, Pages 1626-1635.
  30. Avellaneda, J.A.C., Rodriguez, A.U., Yanez, E., Rey, R.M. (2024). Assessment of the Colombian long-term energy planning scenarios for the national hydrocarbon value chain: Insights from the TIMES-O&G model. Energy Conversion and Management, Volume 306, 15 April 2024, 118317.
  31. Najafi, M., Zolfagharinia, H., Rostami, S., Rafiee, M.(2024). Enhancing supply chain resilience facing partial and complete disruptions: The application in the cooking oil industry. Applied Mathematical Modelling Volume 131, July 2024, Pages 253-287.
  32. Attia, M.A.(2021). A multi-objective robust optimization model for upstream hydrocarbon supply chain. Alexandria Engineering Journal Volume 60, Issue 6, December 2021, Pages 5115-5127.
  33. Alnaqbi, A., Trochu, J., Dweiri, F., Chaabane, A.(2023). Tactical supply chain planning after mergers under uncertainty with an application in oil and gas. Computers & Industrial Engineering Volume 179, May 2023, 109176.
  34. Pishvaee, M.S., Torabi, S.A., Razmi, J. (2012). Credibility-based fuzzy mathematical programming model for green logistics design under uncertainty. Computers & Industrial Engineering Volume 62, Issue 2, March 2012, Pages 624-632.
Advances in Industrial Engineering
Volume 58, Issue 2
December 2024
Pages 413-437