Document Type : Research Paper


Department of Industrial Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran.


In this paper, a framework for optimizing the oil condensate supply chain is modeled using mathematical planning to design and make strategic and tactical decisions. According to this framework, investment and operating costs for oil and gas transmission lines can be minimized to meet the pressure requirements and the transmission network. Also, we can minimize the production of pollutants in the chain-related sectors. In the case under study, all possible decisions are considered to consider the environmental aspects of the supply chain. Therefore, the structure and decisions of the supply chain are generally based on two objective functions, including reducing transmission and maintenance costs and pollution in treatment plants and distribution centers. The proposed model is 95% reliable, which is acceptable reliability, and can estimate goals with only 5% error. Using the proposed model will reduce costs by 31% and emissions by 51%. Also, there will be an 8% increase in the capacity of fields and refineries and an increase in exports by 65%. Using the results obtained from solving the model, we can determine the share of each petroleum product in the cost and each part of the chain in the production of greenhouse gases. According to the results, fuel oil has the highest and oils the lowest. In addition, refineries have the greatest impact, and storage tanks have the least impact on environmental pollution.


Main Subjects

  1. Tautenhain, C. P., Barbosa-Povoa, A. P., & Nascimento, M. C. (2019). A multi-objective matheuristic for designing and planning sustainable supply chains. Computers & industrial engineering135, 1203-1223.
  2. Malvestio, A. C., Fischer, T. B., & Montaño, M. (2018). The consideration of environmental and social issues in transport policy, plan and programme making in Brazil: a systems analysis. Journal of cleaner production179, 674-689.
  3. Watts, N., Amann, M., Arnell, N., Ayeb-Karlsson, S., Belesova, K., Berry, H., ... & Costello, A. (2018). The 2018 report of the Lancet Countdown on health and climate change: shaping the health of nations for centuries to come. The lancet392(10163), 2479-2514.
  4. Balcombe, P., Anderson, K., Speirs, J., Brandon, N., & Hawkes, A. (2017). The natural gas supply chain: the importance of methane and carbon dioxide emissions. ACS sustainable chemistry & engineering5(1), 3-20.
  5. Hamedi, M., Farahani, R. Z., Husseini, M. M., & Esmaeilian, G. R. (2009). A distribution planning model for natural gas supply chain: a case study. Energy policy37(3), 799-812.
  6. Borraz-Sánchez, C., & Ríos-Mercado, R. Z. (2009). Improving the operation of pipeline systems on cyclic structures by tabu search. Computers & chemical engineering33(1), 58-64.
  7. Chebouba, A., Yalaoui, F., Smati, A., Amodeo, L., Younsi, K., & Tairi, A. (2009). Optimization of natural gas pipeline transportation using ant colony optimization. Computers & operations research36(6), 1916-1923.
  8. Chung, T. S., Li, K. K., Chen, G. J., Xie, J. D., & Tang, G. Q. (2003). Multi-objective transmission network planning by a hybrid GA approach with fuzzy decision analysis. International journal of electrical power & energy systems25(3), 187-192.
  9. Hamedi, M., Zanjirani Farahani, R., & Esmaeilian, Gh. (2011). Optimization in natural gas network planning. In R. Zanjirani Farahani, Sh. Rezapour & L. Kardar (Eds.) Logistics operations and management: concepts and models (pp. 393-420). Elsevier.
  10. Woldeyohannes, A. D., & Abd Majid, M. A. (2011). Simulation model for natural gas transmission pipeline network system. Simulation modelling practice and theory19(1), 196-212.
  11. Wu, X., Li, C., Jia, W., & He, Y. (2014). Optimal operation of trunk natural gas pipelines via an inertia-adaptive particle swarm optimization algorithm. Journal of natural gas science and engineering21, 10-18.
  12. Borraz-Sánchez, C., & Haugland, D. (2011). Minimizing fuel cost in gas transmission networks by dynamic programming and adaptive discretization. Computers & industrial engineering61(2), 364-372.
  13. Misra, S., Fisher, M. W., Backhaus, S., Bent, R., Chertkov, M., & Pan, F. (2014). Optimal compression in natural gas networks: a geometric programming approach. IEEE transactions on control of network systems2(1), 47-56.
  14. Midthun, K. T., Fodstad, M., & Hellemo, L. (2015). Optimization model to analyse optimal development of natural gas fields and infrastructure. Energy procedia64, 111-119.
  15. Farouk, H., Zahraee, S. M., Atabani, A. E., Mohd Jaafar, M. N., & Alhassan, F. H. (2020). Optimization of the esterification process of crude jatropha oil (CJO) containing high levels of free fatty acids: a Malaysian case study. Biofuels11(6), 655-662.
  16. Hamedi, M., Farahani, R. Z., Husseini, M. M., & Esmaeilian, G. R. (2009). A distribution planning model for natural gas supply chain: a case study. Energy policy37(3), 799-812.
  17. Azadeh, A., Raoofi, Z., & Zarrin, M. (2015). A multi-objective fuzzy linear programming model for optimization of natural gas supply chain through a greenhouse gas reduction approach. Journal of natural gas science and engineering26, 702-710.
  18. 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 engineering128, 216-227.
  19. Zarei, J., & Amin-Naseri, M. R. (2019). An integrated optimization model for natural gas supply chain. Energy185, 1114-1130.
  20. Vasconcelos, C. D., Lourenço, S. R., Gracias, A. C., & Cassiano, D. A. (2013). Network flows modeling applied to the natural gas pipeline in Brazil. Journal of natural gas science and engineering14, 211-224.
  21. Wang, B., Yuan, M., Zhang, H., Zhao, W., & Liang, Y. (2018). An MILP model for optimal design of multi-period natural gas transmission network. Chemical engineering research and design129, 122-131.
  22. Kabirian, A., & Hemmati, M. R. (2007). A strategic planning model for natural gas transmission networks. Energy policy35(11), 5656-5670.
  23. Andre, J., Bonnans, F., & Cornibert, L. (2009). Optimization of capacity expansion planning for gas transportation networks. European journal of operational research197(3), 1019-1027.
  24. Behrooz, H. A., & Boozarjomehry, R. B. (2017). Dynamic optimization of natural gas networks under customer demand uncertainties. Energy134, 968-983.
  25. Maadanpour Safari, F., Etebari, F., & Pourghader Chobar, A. (2021). Modelling and optimization of a tri-objective Transportation-Location-Routing Problem considering route reliability: using MOGWO, MOPSO, MOWCA and NSGA-II. Journal of optimization in industrial engineering14(2), 99-114.
  26. Pourghader Chobar, A., Adibi, M. A., & Kazemi, A. (2021). A novel multi-objective model for hub location problem considering dynamic demand and environmental issues. Journal of industrial engineering and management studies8(1), 1-31.
  27. Lotfi, R., Kargar, B., Gharehbaghi, A., & Weber, G. W. (2021). Viable medical waste chain network design by considering risk and robustness. Environmental science and pollution research, 1-16.
  28. Lotfi, R., Safavi, S., Gharehbaghi, A., Ghaboulian Zare, S., Hazrati, R., & Weber, G. W. (2021). Viable supply chain network design by considering blockchain technology and cryptocurrency. Mathematical problems in engineering2021.
  29. Lotfi, R., Kargar, B., Hoseini, S. H., Nazari, S., Safavi, S., & Weber, G. W. (2021). Resilience and sustainable supply chain network design by considering renewable energy. International journal of energy research45(12), 17749-17766.
  30. Abolghasemian, M., Ghane Kanafi, A., & Daneshmandmehr, M. (2020). A two-phase simulation-based optimization of hauling system in open-pit mine. Iranian journal of management studies13(4), 705-732.
  31. Abolghasemian, M., & Darabi, H. (2018). Simulation based optimization of haulage system of an open-pit mine: Meta modeling approach. Organizational resources management researchs8(2), 1-17.