Designing a closed loop supply chain network for engine oil in an uncertain environment: A case study in Behran Oil Company

Document Type : Research Paper

Authors

School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

The increase in demand for petroleum derivatives such as engine oil has contributed to not only the rapid and adequate response to them, but also attracting many managers and researchers to control their adverse environmental impacts. In this regard, this study unveils an optimization model to develop a closed loop supply chain network for engine oil in an uncertain environment. In the concerned model, in addition to addressing supply chain costs, adverse environmental effects are also minimized. To solve the proposed multi-objective model and acquire Pareto optimal solutions, the goal programming approach is deployed. The demand and amount of recyclable materials are considered to be imbued with uncertainty, which a robust optimization approach is devised to capture it. Likewise, new methods are taken into account to recycle engine oil, being capable of supporting both economic and environmental benefits. Lastly, a case study is utilized to evaluate and validate the presented model, through which outstanding management results are derived.

Keywords

Main Subjects

References

Amin, S. H., & Zhang, G. (2013). A multi-objective facility location model for closed-loop supply chain network under uncertain demand and return. Applied Mathematical Modelling, 37(6), 4165–4176. https://doi.org/10.1016/j.apm.2012.09.039
Ben-Tal, A., El Ghaoui, L., & Nemirovski, A. (2004). Adjustable robust solutions of uncertain linear programs. Mathematical Programming, 99(2), 351–376. https://doi.org/10.1007/s10107-003-0454-y
Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.
Botas, J. A., Moreno, J., Espada, J. J., Serrano, D. P., & Dufour, J. (2017). Recycling of used lubricating oil: Evaluation of environmental and energy performance by LCA. Resources, Conservation and Recycling, 125(July), 315–323. https://doi.org/10.1016/j.resconrec.2017.07.010
Cohon, J. L. (2004). Multiobjective programming and planning (Vol. 140). Courier Corporation.
Dauer, J. P., & Krueger, R. J. (1977). An iterative approach to goal programming. Journal of the Operational Research Society, 28(3), 671–681.
Dehghani, E., Jabalameli, M. S., Jabbarzadeh, A., & Pishvaee, M. S. (2018). Resilient solar photovoltaic supply chain network design under business-as-usual and hazard uncertainties. Computers and Chemical Engineering, 111, 288–310. https://doi.org/10.1016/j.compchemeng.2018.01.013
Ebrahimi, S. B. (2018). A stochastic multi-objective location-allocation-routing problem for tire supply chain considering sustainability aspects and quantity discounts. Journal of Cleaner Production, 198, 704–720. https://doi.org/10.1016/j.jclepro.2018.07.059
Gregory, C., Darby-Dowman, K., & Mitra, G. (2011). Robust optimization and portfolio selection: The cost of robustness. European Journal of Operational Research, 212(2), 417–428.
Hamawand, I., Yusaf, T., & Rafat, S. (2013). Recycling of waste engine oils using a new washing agent. Energies, 6(2), 1023–1049. https://doi.org/10.3390/en6021023
Husain, R., Assavapokee, T., & Khumawala, B. (2006). Supply Chain Management in the Petroleum Industry : Challenges and Opportunities Supply Chain Management in the Petroleum Industry : International Journal of Global Logistics & Supply Chain Management, 1(2), 90–97. Retrieved from https://www.researchgate.net/profile/Raed_Al-Husain/publication/261912419_Supply_Chain_Management_in_the_Petroleum_Industry_Challenges_and_Opportunities/links/00b7d535e79badf958000000.pdf
Li, Z., Ding, R., & Floudas, C. A. (2011). A comparative theoretical and computational study on robust counterpart optimization: I. Robust linear optimization and robust mixed integer linear optimization. Industrial & Engineering Chemistry Research, 50(18), 10567–10603.
Paydar, M. M., Babaveisi, V., & Safaei, A. S. (2017). An engine oil closed-loop supply chain design considering collection risk. Computers and Chemical Engineering, 104, 38–55. https://doi.org/10.1016/j.compchemeng.2017.04.005
Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637–649. https://doi.org/10.1016/j.apm.2010.07.013
Rincón, J., Cañizares, P., & García, M. T. (2005). Waste oil recycling using mixtures of polar solvents. Industrial and Engineering Chemistry Research, 44(20), 7854–7859. https://doi.org/10.1021/ie0580452
Rincón, J., Cañizares, P., & García, M. T. (2007). Regeneration of used lubricant oil by ethane extraction. Journal of Supercritical Fluids, 39(3), 315–322. https://doi.org/10.1016/j.supflu.2006.03.007
Salem, S., Salem, A., & Babaei, A. A. (2015). Application of Iranian nano-porous Ca-bentonite for recovery of waste lubricant oil by distillation and adsorption techniques. Journal of Industrial and Engineering Chemistry, 23, 154–162. https://doi.org/10.1016/j.jiec.2014.08.009
Soyster, A. L. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research, 21(5), 1154–1157.
Yu, C.-S., & Li, H.-L. (2000). A robust optimization model for stochastic logistic problems. International Journal of Production Economics, 64(1–3), 385–397.
Yuan, Y., Li, Z., & Huang, B. (2016). Robust optimization under correlated uncertainty: Formulations and computational study. Computers and Chemical Engineering, 85, 58–71. https://doi.org/10.1016/j.compchemeng.2015.10.017
Zografos, K. G., & Davis, C. F. (1989). Multi-objective programming approach for routing hazardous materials. Journal of Transportation Engineering, 115(6), 661–673.

Files

History

  • Receive Date: 08 July 2020
  • Revise Date: 23 August 2020
  • Accept Date: 01 September 2020

Share

How to cite

Statistics