28.1 IntroductionIn this chapter we attempt to link the results of our research into logistics planning for a consortium of oil companies facing uncertain demands and prices [2, 3] with our current work on risk management and real options evaluation. The logistics work has been carried out in the European Community ESPRIT project Hydrocarbon and Chemical Logistics under Uncertainty via Stochastic Optimization (HChLOUSO). Logistics planning deals with supply, transformation, storage, and transportation activities in a complex network structure over various planning horizons (strategic decisions for the long term and tactical decisions for the medium term). As a feasible solution to such problems is seldom initially achieved, it is common practice in industry to search for a solution by minimizing the cost of infeasibilities and correspondingly adjusting constraints [6]. When internal resources of companies are exhausted (or in surplus), excess demand (or supply) may be handled externally by buying (or selling) the required products in the spot commodity markets.In our ESPRIT project we observed the importance of trading activities and proposed elimination of infeasibilities through trading. This problem has been formulated as a dynamic stochastic program with additional variables representing the trading activities and leads to robust first-stage solutions in the presence of future price and demand uncertainties [2].The volatility of crude oil prices has a significant impact on the planning decisions and budgets of oil companies. It is common for producing companies to develop a hedging program to insure that the company is protected against a collapse in crude oil prices.

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.