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Formulations for a problem of petroleum transportation

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  • Aizemberg, Luiz
  • Kramer, Hugo Harry
  • Pessoa, Artur Alves
  • Uchoa, Eduardo

Abstract

Oil tankers play a fundamental role in every offshore petroleum supply chain and due to its high price, it is essential to optimize its use. Since this optimization requires handling detailed operational aspects, complete optimization models are typically intractable. Thus, a usual approach is to solve a tactical level model prior to optimize the operational details. In this case, it is desirable that tactical models are as precise as possible to avoid too severe adjustments in the next optimization level. In this paper, we study tactical models for a crude oil transportation problem by tankers. We did our work on the top of a previous paper found in the literature. The previous model considers inventory capacities and discrete lot sizes to be transported, aiming to meet given demands over a finite time horizon. We compare several formulations for this model using 50 instances from the literature and proposing 25 new harder ones. A column generation-based heuristic is also proposed to find good feasible solutions with less computational burden than the heuristics of the commercial solver used.

Suggested Citation

  • Aizemberg, Luiz & Kramer, Hugo Harry & Pessoa, Artur Alves & Uchoa, Eduardo, 2014. "Formulations for a problem of petroleum transportation," European Journal of Operational Research, Elsevier, vol. 237(1), pages 82-90.
    • Handle: RePEc:eee:ejores:v:237:y:2014:i:1:p:82-90
    DOI: 10.1016/j.ejor.2014.01.036
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    References listed on IDEAS

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    Cited by:

    1. Carmona-Benítez, Rafael Bernardo & Cruz, Héctor, 2023. "A multiproduct gasoline supply chain with product standardization and postponement strategy," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    2. Wang, Shuang & Wallace, Stein W. & Lu, Jing & Gu, Yewen, 2020. "Handling financial risks in crude oil imports: Taking into account crude oil prices as well as country and transportation risks," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 133(C).
    3. de Assis, Leonardo Salsano & Camponogara, Eduardo, 2016. "A MILP model for planning the trips of dynamic positioned tankers with variable travel time," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 93(C), pages 372-388.
    4. Hennig, F. & Nygreen, B. & Furman, K.C. & Song, J., 2015. "Alternative approaches to the crude oil tanker routing and scheduling problem with split pickup and split delivery," European Journal of Operational Research, Elsevier, vol. 243(1), pages 41-51.
    5. Juman, Z.A.M.S. & Hoque, M.A., 2014. "A heuristic solution technique to attain the minimal total cost bounds of transporting a homogeneous product with varying demands and supplies," European Journal of Operational Research, Elsevier, vol. 239(1), pages 146-156.
    6. Kumar, Sourabh & Barua, Mukesh Kumar, 2022. "A modeling framework and analysis of challenges faced by the Indian petroleum supply chain," Energy, Elsevier, vol. 239(PE).
    7. S. K. Bharati & Rita Malhotra, 2017. "Two stage intuitionistic fuzzy time minimizing transportation problem based on generalized Zadeh’s extension principle," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1442-1449, November.

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