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This work proposes a hybrid of stochastic programming
(SP) approaches for an optimal midterm refinery
planning that addresses three forms of uncertainties:
prices of crude oil and products, demands, and
yields. An SP technique that utilizes compensating
slack variables is employed to explicitly account for
constraint violations to increase model tractability.
Four approaches are considered to achieve solution
and model robustness: (1) the Markowitz´´s
mean-variance (MV) model to handle randomness in the
objective coefficients by minimizing the variance
(economic risk) of the expected value of the random
coefficients; (2) the two-stage SP with fixed
recourse to deal with randomness in the RHS and LHS
coefficients of the constraints by minimizing the
expected recourse costs; (3) incorporation of the MV
model within the framework in (2) to formulate a
mean?risk model that minimizes the expectation and
the operational risk measure of variance of the
recourse costs; and (4) reformulation of the model in
(3) by adopting mean-absolute deviation (MAD) as the
operational risk measure imposed by the recourse
costs for a novel refinery planning application. A
numerical example is illustrated.

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Cheng Seong Khor is a lecturer-cum-researcher at the PETRONASUniversity of Technology. His research interests involves theapplication of systemsengineering and optimization to chemical process systemsengineering (PSE) problems and other operations-research-relatedproblems with particular focus on petroleum refinery systems.

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