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A framework for crude oil scheduling in an integrated terminal-refinery system under supply uncertainty

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  • Oliveira, F.
  • Nunes, P.M.
  • Blajberg, R.
  • Hamacher, S.

Abstract

Operational decisions for crude oil scheduling activities are determined on a daily basis and have a strong impact on the overall supply chain cost. The challenge is to develop a feasible schedule at a low cost that has a high level of confidence. This paper presents a framework to support decision making in terminal-refinery systems under supply uncertainty. The proposed framework comprises a stochastic optimization model based on mixed-integer linear programming for scheduling a crude oil pipeline connecting a marine terminal to an oil refinery and a method for representing oil supply uncertainty. The scenario generation method aims at generating a minimal number of scenarios while preserving as much as possible of the uncertainty characteristics. The proposed framework was evaluated considering real-world data. The numerical results suggest the efficiency of the framework in providing resilient solutions in terms of feasibility in the face of the inherent uncertainty.

Suggested Citation

  • Oliveira, F. & Nunes, P.M. & Blajberg, R. & Hamacher, S., 2016. "A framework for crude oil scheduling in an integrated terminal-refinery system under supply uncertainty," European Journal of Operational Research, Elsevier, vol. 252(2), pages 635-645.
    • Handle: RePEc:eee:ejores:v:252:y:2016:i:2:p:635-645
    DOI: 10.1016/j.ejor.2016.01.034
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    References listed on IDEAS

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    1. Gabriel, Steven A. & Zhuang, Jifang & Egging, Ruud, 2009. "Solving stochastic complementarity problems in energy market modeling using scenario reduction," European Journal of Operational Research, Elsevier, vol. 197(3), pages 1028-1040, September.
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    Cited by:

    1. Klepikov, Vladimir Pavlovich & Klepikov, Vladimir Vladimirovich, 2020. "Quantitative approach to estimating crude oil supply in Southern Europe," Resources Policy, Elsevier, vol. 69(C).
    2. Panda, Debashish & Ramteke, Manojkumar, 2019. "Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 68-82.
    3. Aragão, Amanda & Giampietro, Mario, 2016. "An integrated multi-scale approach to assess the performance of energy systems illustrated with data from the Brazilian oil and natural gas sector," Energy, Elsevier, vol. 115(P2), pages 1412-1423.

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