Optimization of a multiperiod refinery planning problem under uncertainty

Abstract Uncertainty in refinery planning presents a significant challenge in determining the day‐to‐day operations of an oil refinery. Deterministic modeling techniques often fail to account for this uncertainty, potentially resulting in reduced profit. The stochastic programming framework explicitly incorporates parameter uncertainty in the problem formulation, thus giving preference to robust solutions. In this work, a nonlinear, multiperiod, industrial refinery problem is extended to a two‐stage stochastic problem, formulated as a mixed‐integer nonlinear program. A crude‐oil sequencing case study is developed with binary scheduling decisions in both stages of the stochastic programming problem. Solution via a decomposition strategy based on the generalized Benders decomposition (GBD) algorithm is proposed. The binary decisions are designated as complicating variables that, when fixed, reduce the full‐space problem to a series of independent scenario subproblems. Through the application of the GBD algorithm, a feasible mixed‐integer solution is obtained that is more robust to uncertainty than its deterministic counterpart.