Multimodal Transportation Optimization of Refined Oil Logistics Considering Daily Scheduling: Case from China
Publication: Journal of Pipeline Systems Engineering and Practice
Volume 15, Issue 2
Abstract
With the rapid development of global industrialization, the consumption of refined oil is at a high level. The scale of refined oil transportation is huge. For refined oil sales enterprises, determining a method for making reasonable transportation plans and reducing transportation cost has become an important means to improve competitiveness. The current method of developing a primary logistics plan does not consider the characteristics of various transportation modes, which can lead to an increase in overall costs. The logistics system in this case has multiple transportation modes, including road, rail, and pipeline. In addition, a multiproduct pipeline has multiple injection points. For such a complex system, this work unifies the time dimension of continuous transportation by pipeline and discrete transportation by rail and road based on the idea of multimodal transportation. This paper couples a pipeline scheduling submodel and a logistics optimization submodel to construct a daily scheduling model for refined oil which is close to the actual operation in the field. This model was validated using a refined oil logistics system in China, and the results show that the optimized logistics solution decreased total transportation costs by 14.5% compared with the actual cost. This model can serve as a guide for actual operations.
Get full access to this article
View all available purchase options and get full access to this article.
Data Availability Statement
Some or all data, models, or codes that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (52202405) and the Science Foundation of China University of Petroleum, Beijing (2462023XKBH013). The authors are grateful to all study participants.
References
Bamoumen, M., S. Elfirdoussi, L. Ren, and N. Tchernev. 2023. “An efficient GRASP-like algorithm for the multi-product straight pipeline scheduling problem.” Comput. Oper. Res. 150 (Feb): 106082. https://doi.org/10.1016/j.cor.2022.106082.
Cafaro, D. C., and J. Cerdá. 2004. “Optimal scheduling of multiproduct pipeline systems using a non-discrete MILP formulation.” Comput. Chem. Eng. 28 (10): 2053–2068. https://doi.org/10.1016/j.compchemeng.2004.03.010.
Cafaro, V. G., D. C. Cafaro, C. A. Méndez, and J. Cerdá. 2011. “Detailed scheduling of operations in single-source refined products pipelines.” Ind. Eng. Chem. Res. 50 (10): 6240–6259. https://doi.org/10.1021/ie200007a.
Dong, K., R. Sun, J. Wu, and G. Hochman. 2018. “The growth and development of natural gas supply chains: The case of China and the US.” Energy Policy 123 (Dec): 64–71. https://doi.org/10.1016/j.enpol.2018.08.034.
Escudero, L. F., F. J. Quintana, and J. Salmerón. 1999. “CORO, a modeling and an algorithmic framework for oil supply, transformation and distribution optimization under uncertainty.” Eur. J. Oper. Res. 114 (3): 638–656. https://doi.org/10.1016/S0377-2217(98)00261-6.
Jiao, Y., R. Qiu, Y. Liang, Q. Liao, R. Tu, X. Wei, and H. Zhang. 2022. “Integration optimization of production and transportation of refined oil: A case study from China.” Chem. Eng. Res. Des. 188 (Dec): 39–49. https://doi.org/10.1016/j.cherd.2022.09.037.
Li, Z., Y. Liang, Q. Liao, N. Xu, J. Zheng, and H. Zhang. 2021. “Scheduling of a branched multiproduct pipeline system with robust inventory management.” Comput. Ind. Eng. 162 (Dec): 107760. https://doi.org/10.1016/j.cie.2021.107760.
Liao, Q., R. Tu, W. Zhang, B. Wang, Y. Liang, and H. Zhang. 2022. “Auction design for capacity allocation in the petroleum pipeline under fair opening.” Energy 264 (Feb): 126079. https://doi.org/10.1016/J.ENERGY.2022.126079.
Lima, C., S. Relvas, and A. Barbosa-Póvoa. 2021. “Designing and planning the downstream oil supply chain under uncertainty using a fuzzy programming approach.” Comput. Chem. Eng. 151 (Aug): 107373. https://doi.org/10.1016/j.compchemeng.2021.107373.
Lima, C., S. Relvas, and A. P. F. D. Barbosa-Póvoa. 2016. “Downstream oil supply chain management: A critical review and future directions.” Comput. Chem. Eng. 92 (Sep): 78–92. https://doi.org/10.1016/j.compchemeng.2016.05.002.
Magatão, L., L. V. R. Arruda, and F. Neves Jr. 2004. “A mixed integer programming approach for scheduling commodities in a pipeline.” Comput. Chem. Eng. 28 (1–2): 171–185. https://doi.org/10.1016/S0098-1354(03)00165-0.
MirHassani, S. A. 2008. “An operational planning model for petroleum products logistics under uncertainty.” Appl. Math. Comput. 196 (2): 744–751. https://doi.org/10.1016/j.amc.2007.07.006.
Paltsev, S., and D. Zhang. 2015. “Natural gas pricing reform in China: Getting closer to a market system?” Energy Policy 86 (Nov): 43–56. https://doi.org/10.1016/j.enpol.2015.06.027.
Qiu, R., Y. Liang, Q. Liao, X. Wei, H. Zhang, Y. Jiao, and H. Zhang. 2022. “A model-experience-driven method for the planning of refined product primary logistics.” Chem. Eng. Sci. 254 (Jun): 117607. https://doi.org/10.1016/j.ces.2022.117607.
Qiu, R., H. Zhang, X. Gao, X. Zhou, Z. Guo, Q. Liao, and Y. Liang. 2019. “A multi-scenario and multi-objective scheduling optimization model for liquefied light hydrocarbon pipeline system.” Chem. Eng. Res. Des. 141 (Jan): 566–579. https://doi.org/10.1016/j.cherd.2018.11.018.
Sear, T. N. 1993. “Logistics planning in the downstream oil industry.” J. Oper. Res. Soc. 44 (1): 9–17. https://doi.org/10.1057/jors.1993.2.
Tu, R., Y. Jiao, R. Qiu, Q. Liao, N. Xu, J. Du, and Y. Liang. 2023a. “Energy saving and consumption reduction in the transportation of petroleum products: A pipeline pricing optimization perspective.” Appl. Energy 342 (Mar): 121135. https://doi.org/10.1016/j.apenergy.2023.121135.
Tu, R., Q. Liao, L. Huang, Y. Jiao, X. Wei, and Y. Liang. 2023b. “Pipeline sharing: Remaining capacity estimation of multiproduct pipelines.” Chem. Eng. Res. Des. 191 (May): 338–352. https://doi.org/10.1016/j.cherd.2023.01.028.
Tu, R., Q. Liao, N. Xu, X. Wei, Y. Wang, Y. Liang, and H. Zhang. 2023c. “Pipeline sharing: Potential capacity analysis of biofuel transportation through existing pipelines.” J. Cleaner Prod. 398 (Apr): 136507. https://doi.org/10.1016/j.jclepro.2023.136507.
Wang, B., J. J. Klemeš, X. Yu, R. Qiu, J. Zheng, Y. Lin, and B. Zhu. 2021a. “Planning of a flexible refined products transportation network in response to emergencies.” J. Pipeline Sci. Eng. 1 (4): 468–475. https://doi.org/10.1016/j.jpse.2021.12.004.
Wang, B., J. J. Klemeš, T. Zheng, and Y. Liang. 2021b. “A fair profit allocation model for the distribution plan optimisation of refined products supply chains.” Comput. Aided Chem. Eng. 50 (Jan): 1847–1852. https://doi.org/10.1016/B978-0-323-88506-5.50286-2.
Wang, B., Y. Liang, M. Yuan, H. Zhang, and Q. Liao. 2019. “A metaheuristic method for the multireturn-to-depot petrol truck routing problem with time windows.” Pet. Sci. 16 (3): 701–712. https://doi.org/10.1007/s12182-019-0316-8.
Yamashita, D., B. J. V. da Silva, R. Morabito, and P. C. Ribas. 2019. “A multi-start heuristic for the ship routing and scheduling of an oil company.” Comput. Ind. Eng. 136 (Oct): 464–476. https://doi.org/10.1016/j.cie.2019.07.039.
Information & Authors
Information
Published In
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Sep 27, 2023
Accepted: Nov 28, 2023
Published online: Feb 22, 2024
Published in print: May 1, 2024
Discussion open until: Jul 22, 2024
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.