Planning logistics operations in the oil industry

M A H Dempster, N Hicks Pedrón, E A Medova, J E Scott and A Sembos ()
Additional contact information
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

Journal of the Operational Research Society, 2000, vol. 51, issue 11, 1271-1288

Abstract: 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.

Keywords: energy; logistics; planning; supply chain management; stochastic linear programming (search for similar items in EconPapers)
Date: 2000
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Citations: View citations in EconPapers (17)

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DOI: 10.1057/palgrave.jors.2601043

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