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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 Markowitzs mean-variance (MV) model to handle randomness in the objective coefficients by minimizing the variance (economic risk) of the expected value of...

An accessible, easy-to-read introduction to the methods of mixed-integer optimization, with practical applications, real-world operational data, and case studies Interest in model-based approaches for optimizing the design of petroleum refineries has increased throughout the industry in recent years. Mathematical optimization based on mixed-integer programming has brought about the superstructure optimization method for synthesizing petroleum refinery configurations from multiple topological alternatives. Model-Based Optimization for Petroleum Refinery Configuration Design presents a...