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Planning logistics operations in the oil industry

Author

Listed:
  • M A H Dempster

    (University of Cambridge)

  • N Hicks Pedrón

    (University of Cambridge)

  • E A Medova

    (University of Cambridge)

  • J E Scott

    (University of Cambridge)

  • A Sembos

    (University of Cambridge)

Abstract

In this paper we apply stochastic programming modelling and solution techniques to planning problems for a consortium of oil companies. A multiperiod supply, transformation and distribution scheduling problem—the Depot and Refinery Optimization Problem (DROP)—is formulated for strategic or tactical level planning of the consortium's activities. This deterministic model is used as a basis for implementing a stochastic programming formulation with uncertainty in the product demands and spot supply costs (DROPS), whose solution process utilizes the deterministic equivalent linear programming problem. We employ our STOCHGEN general purpose stochastic problem generator to ‘recreate’ the decision (scenario) tree for the unfolding future as this deterministic equivalent. To project random demands for oil products at different spatial locations into the future and to generate random fluctuations in their future prices/costs a stochastic input data simulator is developed and calibrated to historical industry data. The models are written in the modelling language XPRESS-MP and solved by the XPRESS suite of linear programming solvers. From the viewpoint of implementation of large-scale stochastic programming models this study involves decisions in both space and time and careful revision of the original deterministic formulation. The first part of the paper treats the specification, generation and solution of the deterministic DROP model. The stochastic version of the model (DROPS) and its implementation are studied in detail in the second part and a number of related research questions and implications discussed.

Suggested Citation

  • M A H Dempster & N Hicks Pedrón & E A Medova & J E Scott & A Sembos, 2000. "Planning logistics operations in the oil industry," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 51(11), pages 1271-1288, November.
    • Handle: RePEc:pal:jorsoc:v:51:y:2000:i:11:d:10.1057_palgrave.jors.2601043
    DOI: 10.1057/palgrave.jors.2601043
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    Citations

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    Cited by:

    1. Pudasaini, Pramesh, 2021. "Integrated planning of downstream petroleum supply chain: a multi-objective stochastic approach," Operations Research Perspectives, Elsevier, vol. 8(C).
    2. S Karabuk, 2008. "Production planning under uncertainty in textile manufacturing," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(4), pages 510-520, April.
    3. Guajardo, Mario & Kylinger, Martin & Rönnqvist, Mikael, 2013. "Speciality oils supply chain optimization: From a decoupled to an integrated planning approach," European Journal of Operational Research, Elsevier, vol. 229(2), pages 540-551.
    4. Jeremy Bulow & Paul Klemperer, 1998. "The Tobacco Deal," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 29(1998 Micr), pages 323-394.
    5. Hezarkhani, Behzad & Kubiak, Wieslaw, 2010. "A coordinating contract for transshipment in a two-company supply chain," European Journal of Operational Research, Elsevier, vol. 207(1), pages 232-237, November.
    6. Chen, Ruoran & Deng, Tianhu & Huang, Simin & Qin, Ruwen, 2015. "Optimal crude oil procurement under fluctuating price in an oil refinery," European Journal of Operational Research, Elsevier, vol. 245(2), pages 438-445.
    7. Al-Othman, Wafa B.E. & Lababidi, Haitham M.S. & Alatiqi, Imad M. & Al-Shayji, Khawla, 2008. "Supply chain optimization of petroleum organization under uncertainty in market demands and prices," European Journal of Operational Research, Elsevier, vol. 189(3), pages 822-840, September.
    8. Yan, Min & Filieri, Raffaele & Raguseo, Elisabetta & Gorton, Matthew, 2021. "Mobile apps for healthy living: Factors influencing continuance intention for health apps," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    9. Apoorva Ghosh & Pranabesh Ray, 2012. "A Contemporary Model for Industrial Relations," Management and Labour Studies, XLRI Jamshedpur, School of Business Management & Human Resources, vol. 37(1), pages 17-30, February.
    10. Jikai Zou & Shabbir Ahmed & Xu Andy Sun, 2018. "Partially Adaptive Stochastic Optimization for Electric Power Generation Expansion Planning," INFORMS Journal on Computing, INFORMS, vol. 30(2), pages 388-401, May.
    11. Moradi Nasab, N. & Amin-Naseri, M.R., 2016. "Designing an integrated model for a multi-period, multi-echelon and multi-product petroleum supply chain," Energy, Elsevier, vol. 114(C), pages 708-733.
    12. Moshe Kress & Michal Penn & Maria Polukarov, 2007. "The minmax multidimensional knapsack problem with application to a chance‐constrained problem," Naval Research Logistics (NRL), John Wiley & Sons, vol. 54(6), pages 656-666, September.
    13. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    14. Tan, Shin Bin & Ti, Edward S.W., 2020. "What is the value of built heritage conservation? Assessing spillover effects of conserving historic sites in Singapore," Land Use Policy, Elsevier, vol. 91(C).
    15. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).
    16. Nguyet Thi Tran & Dirk Weichgrebe, 2020. "Regional material flow behaviors of agro‐food processing craft villages in Red River Delta, Vietnam," Journal of Industrial Ecology, Yale University, vol. 24(3), pages 707-725, June.

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