Petroleum Refinery Planning Under Uncertainty: A Multiobjective Optimization Approach with Economic and Operational Risk Management

record high of USD 147 per barrel according to the NYMEX exchange on June
2008. It is forecast to spiral upwards (withthe current graph trend) to a muchhigher
price level. The current situation of fluctuating high petroleum crude oil prices is
affecting the markets and industries worldwide by the uncertainty and volatility of
the petroleum industry. As oil refining is the downstream of the petroleum industry,
it is increasingly important for refineries to operate at an optimal level in the
presence of volatility of crude oil prices. Downstream refineries must assess the
potential impact that may affect its optimal profit margin byconsidering the costs of
purchasing the raw material of crude oils and prices of saleable intermediates and
products as well as production yields. With optimization, refinery will be able to
operate at optimal condition.
In this work, we have attempted to solve model formulation concerning the
petroleum refinery planning under uncertainty. We use stochastic programming
optimization incorporating the weighted sum method as well as the epsilon
constraint method to solve the model formulation ofthe petroleum refinery planning
under uncertainty.
The objective of this research project is to formulate a deterministic model
followed by a two stage stochastic programming model with recourse problem for a
petroleum refinery planning. The two stage stochastic risk model is then
reformulated using MeanAbsolute Deviation as the risk measure. After formulating
the stochastic model using Mean Absolute Deviation, the problem is then
investigated using the Pareto front solution of efficient frontier of the resulting
multiobjective optimization problem by using the Weighted SumMethod as well as
the e-constraint method in order to obtain the Pareto Optimal Curve which generates
a wide selection of optimization solutions for our problem. The implementation of
the multiobjective optimization problem is then automated to report the model
solution by capturing the solution values using theGAMS looping system. Notethat
some of the major parameters used throughout the formulated stochastic programming model include prices of the raw material crude oil and saleable
products, market demands for products, andproduction yields.
The main contribution on this work in die first part is to conduct a further
study/research onthe implementation of the model formulation in Khor et al. (2008)
where the model formulated by Khoret al. (2008) uses variance as the risk measure.
The results obtain in the previous paper will be compared with the method in this
paper that incorporates Mean Absolute Deviation as the risk measure. To further
study the model formulated, the solution obtain is further enhanced using the
Weighted Sum Method as well as the Epsilon constraint method to obtain the Pareto
Optimal Curve generation. Hence, most of the exposition on the model formulation
and solution algorithms are taken directly from the original paper so as to provide
the readers with the most accurate information possible.
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