Planning and optimising of petroleum industry supply chain and logistics under uncertainty

Petroleum industry has a major share in the world energy and industrial markets. In the recent years, petroleum industry has grown increasingly complex as a result of tighter competition, stricter environmental regulations and lower-margin profits. It is facing a challenging task to remain competitive in a globalised market, the fluctuating demand for petroleum products and the current situation of fluctuating high petroleum crude oil prices is a demonstration that markets and industries throughout the world are impacted by the uncertainty and volatility of the petroleum industry. These factors and others forced petroleum companies for a greater need in the strategic planning and optimisation in order to make decisions that satisfy conflicting multi-objective goals of maximising expected profit while simultaneously minimising risk. These decisions have to take into account uncertainties and constraints in factors such as the source and availability of raw material, production and distribution costs and expected market demand. The main aim of this research is the development of a strategic planning and optimising model suitable for use within the petroleum industry supply chain under different types of uncertainty. The petroleum supply chain consists of all those activities related to the petroleum industry, from the recovery of raw materials to the distribution of the finished product. This network of activities forms the basis of the proposed mathematical and simulation models. Mathematical model of two-stage stochastic linear programming taking into consideration the effect of uncertainty in market demand is developed to address the strategic planning and optimisation of petroleum supply chain. GAMS software is used to solve the proposed mathematical models for this research. Arena simulation Software is utilised to develop a model for the proposed petroleum supply chain starting from crude oil supply to the system, going through three stages of separation processes and finally reaching the distillation stage. The model took into account the following factors: Input Rate, Oil Quality, Distillation Capacity and Number of Failed Separators which are analysed against the performance measures: Total Products and Equipment Utilisation. The results obtained from the experiment are analysed using SPSS Programme.