A method for selecting one or more crude oils from a plurality of crude oils. In some embodiments, a plurality of scenarios may be generated, each scenario comprising a plurality of values corresponding, respectively, to a plurality of uncertain parameters, the plurality of uncertain parameters comprising at least one uncertain parameter relating to a quality of a crude oil of the plurality of crude oils. In some embodiments, a stochastic programming model may be solved to obtain a solution that optimizes an objective function, and one or more crude oils may be procured based on respective procurement amounts in the solution of the stochastic programming model. In some embodiments, a chance-constrained programming model may be solved to obtain a solution that optimizes an objective function, and a plurality of feedstocks may be blended into a final product based on the solution of the chance-constrained programming model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for selecting one or more crude oils from a plurality of crude oils, the method comprising acts of: generating a plurality of scenarios, each scenario comprising a plurality of values corresponding, respectively, to a plurality of uncertain parameters, the plurality of uncertain parameters comprising at least one uncertain parameter relating to a quality of a crude oil of the plurality of crude oils; using at least one processor programmed by executable instructions to solve a stochastic programming model to obtain a solution that optimizes an objective function, wherein: the stochastic programming model represents one or more refinery operations performed on one or more crude oils to produce one or more final products; the solution includes one or more crude oils to be procured and, for each crude oil to be procured, a procurement amount; and the solution satisfies at least one constraint in each scenario of the plurality of scenarios, the constraint representing a quality specification for a final product; and procuring the one or more crude oils based on respective procurement amounts in the solution of the stochastic programming model. 2. The method of claim 1, wherein the plurality of scenarios are generated based at least in part on historical data relating to the quality of the crude oil. 3. The method of claim 2, further comprising: using the historical data relating to the quality of the crude oil to identify a probability distribution for the at least one uncertain parameter, wherein the plurality of scenarios are generated at least in part by sampling the probability distribution. 4. The method of claim 1, further comprising: identifying a realized scenario based at least in part on at least one physical measurement taken from at least one procured crude oil, the realized scenario comprising a plurality of realized values corresponding, respectively, to the plurality of uncertain parameters; and controlling, based at least in part on the realized scenario, performance of the one or more refinery operations on the one or more procured crude oils. 5. The method of claim 4, wherein controlling the performance of the one or more refinery operations comprises: using the realized scenario to determine one or more values for one or more flow rates, the one or more values optimizing the objective function given the realized scenario; and controlling the one or more flow rates using the one or more values determined using the realized scenario. 6. The method of claim 1, wherein the stochastic programming model comprises one or more nonconvex functions, and wherein solving the stochastic programming model comprises using a nonconvex generalized Benders decomposition (NGBD) method to solve the stochastic programming model. 7. A system for selecting one or more crude oils from a plurality of crude oils, the system comprising: at least one processor; and at least one computer-readable medium having encoded thereon executable instructions, wherein the at least one processor is programmed by the executable instructions to: generate a plurality of scenarios, each scenario comprising a plurality of values corresponding, respectively, to a plurality of uncertain parameters, the plurality of uncertain parameters comprising at least one uncertain parameter relating to a quality of a crude oil of the plurality of crude oils; solve a stochastic programming model to obtain a solution that optimizes an objective function, wherein: the stochastic programming model represents one or more refinery operations performed on one or more crude oils to produce one or more final products; the solution includes one or more crude oils to be procured and, for each crude oil to be procured, a procurement amount; and the solution satisfies at least one constraint in each scenario of the plurality of scenarios, the constraint representing a quality specification for a final product; and cause the one or more crude oils to be procured based on respective procurement amounts in the solution of the stochastic programming model. 8. The system of claim 7, wherein the at least one processor is programmed to generate the plurality of scenarios based at least in part on historical data relating to the quality of the crude oil. 9. The system of claim 8, wherein the at least one processor is further programmed to: use the historical data relating to the quality of the crude oil to identify a probability distribution for the at least one uncertain parameter, wherein the at least one processor is programmed to generate the plurality of scenarios at least in part by sampling the probability distribution. 10. The system of claim 7, wherein the at least one processor is further programmed to: identify a realized scenario based at least in part on at least one physical measurement taken from at least one procured crude oil, the realized scenario comprising a plurality of realized values corresponding, respectively, to the plurality of uncertain parameters; and control, based at least in part on the realized scenario, performance of the one or more refinery operations on the one or more procured crude oils. 11. The system of claim 10, wherein the at least one processor is programmed to control the performance of the one or more refinery operations at least in part by: using the realized scenario to determine one or more values for one or more flow rates, the one or more values optimizing the objective function given the realized scenario; and controlling the one or more flow rates using the one or more values determined using the realized scenario. 12. The system of claim 7, wherein the stochastic programming model comprises one or more nonconvex functions, and wherein the at least one processor is programmed to solve the stochastic programming model at least in part by using a nonconvex generalized Benders decomposition (NGBD) method to solve the stochastic programming model. 13. A method comprising acts of: identifying, for each uncertain parameter of a plurality of uncertain parameters, a probability distribution for the uncertain parameter, wherein a first uncertain parameter of the plurality of uncertain parameters relates to a quality of a first feedstock; using at least one processor programmed by executable instructions to solve a chance-constrained programming model to obtain a solution that optimizes an objective function, wherein: the chance-constrained programming model represents a blending operation that blends a plurality of feedstocks into a final product, the plurality of feedstocks comprising the first feedstock; the chance-constrained programming model is solved based on the probability distributions for the plurality of uncertain parameters; the solution includes one or more feedstocks to be blended and, for each feedstock to be blended, a percentage of the feedstock in the final product; the solution satisfies at least one constraint with at least a selected probability, the constraint representing a quality specification for a final product; and blending the one or more feedstocks based on respective percentages in the solution of the chance-constrained programming model. 14. The method of claim 13, wherein the quality of the first feedstock comprises a quality selected from a group consisting of: Vapor Pressure (RVP), Research Octane Number (RON), Motor Octane Number (MON), sulfur, and benzene. 15. The method of claim 13, wherein the quality specification for the final product comprises multiple quality requirements, and wherein solving the chance-constrained programming model comprises: obtaining a conservative approximation by converting a joint chance constraint relating to the quality requirements into individual chance constraints; and solving the conservative approximation. 16. A system comprising: at least one processor; and at least one computer-readable medium having encoded thereon executable instructions, wherein the at least one processor is programmed by the executable instructions to: identify, for each uncertain parameter of a plurality of uncertain parameters, a probability distribution for the uncertain parameter, wherein a first uncertain parameter of the plurality of uncertain parameters relates to a quality of a first feedstock; use at least one processor programmed by executable instructions to solve a chance-constrained programming model to obtain a solution that optimizes an objective function, wherein: the chance-constrained programming model represents a blending operation that blends a plurality of feedstocks into a final product, the plurality of feedstocks comprising the first feedstock; the chance-constrained programming model is solved based on the probability distributions for the plurality of uncertain parameters; the solution includes one or more feedstocks to be blended and, for each feedstock to be blended, a percentage of the feedstock in the final product; the solution satisfies at least one constraint with at least a selected probability, the constraint representing a quality specification for a final product; and cause the one or more feedstocks to be blended based on respective percentages in the solution of the chance-constrained programming model. 17. The system of claim 16, wherein the quality of the first feedstock comprises a quality selected from a group consisting of: Vapor Pressure (RVP), Research Octane Number (RON), Motor Octane Number (MON), sulfur, and benzene. 18. The system of claim 16, wherein the quality specification for the final product comprises multiple quality requirements, and wherein the at least one processor is programmed to solve the chance-constrained programming model at least in part by: obtaining a conservative approximation by converting a joint chance constraint relating to the quality requirements into individual chance constraints; and solving the conservative approximation.