Uncertainty in component quality in gasoline blending due to measurement errors and variation in operation leads to planned blends which may not meet quality specifications and re-blending is required. Formulating gasoline blending as chance constrained programming enables a decision maker to decide what percentage of blends will be guaranteed to meet the specifications and balance the increased cost of blends vs. the cost of having to re-blend the off-spec blends. Chance constrained formulation makes the gasoline blend problem nonlinear and nonconvex. In this work, we employ a supply-demand pinch based algorithm to optimize gasoline blend planning with uncertainty in components qualities and examine its performance vs. full-space model. The supply-demand pinch algorithm decomposes the problem into two sub-problems, top-level (NLP) computes optimal blend recipes and the bottom-level (MILP) computes an optimal production plan using the recipes computed at the top-level. Computational efficiency of the algorithm is verified by case studies.

由于测量误差和操作变化导致的汽油混合中组分质量的不确定性导致计划的混合可能不符合质量规格，因此需要重新混合。将汽油调和公式化为机会受限的编程，可使决策者决定可以保证满足标准要求的混合比例，并权衡增加的混合燃料成本与必须重新混合不合格的混合燃料的成本。机会约束公式使汽油混合问题非线性和不凸。在这项工作中，我们采用基于供需夹点的算法来优化具有混合成分质量不确定性的汽油混合计划，并检查其性能与全空间模型之间的关系。供需夹点算法将问题分解为两个子问题，顶级（NLP）使用最佳计算配方来计算最佳混合配方，而底层（MILP）使用在顶层计算出的配方来计算最佳生产计划。通过案例研究验证了该算法的计算效率。