AI 中文总结
针对聚合物工厂耦合特性破坏传统S&OP指标的问题，提出AGPPC框架及相关模型，经实际数据验证可显著提升营业利润。
AI 中文摘要
销售与运营规划（S&OP）中，单产品毛利率等盈利指标是产品优先级排序的标准工具，这类指标基于生产线可独立分配的前提，赋予规划人员易于计算和使用的单位吨排名。但耦合工厂（尤其是并行生产线共用单一大宗原料的聚合物工厂）会从根本上破坏该前提，导致制定出损害价值的生产计划。本文提出一种基于产品集群增广毛利率（AGPPC）的框架，将单位吨排名指标转换为单位耦合小时排名，该排名带有实例级最优性证明。我们阐述AGPPC的理论，其将联产品列概念与规划问题的流体松弛相结合，同时构建工厂的集成双线性混合整数规划模型及经McCormick线性化的基线模型，为其提供有保证的界。通过印度尼西亚某聚合物生产商的实际运营数据验证，耦合感知指标单独可使营业利润提升7.6%，完整优化方案较当前实践提升28%。
英文摘要
Profitability metrics, such as the single-product gross profit margin, constitute the standard toolbox to prioritize products in sales and operations planning (S&OP). These metrics leverage the premise that production lines can be committed independently, which endows them with a simple per-ton ranking that a planner can compute and use easily in practice. However, coupled plants, especially polymer plants whose parallel lines draw simultaneously on a single bulk feed, can fundamentally undermine this premise and mislead to value-destroying production plans. We propose a framework built on what we call the Augmented Gross Profit for Product Clusters (AGPPC), which converts the per-ton ranking metric into a per-coupled-hour ranking that carries a per-instance optimality certificate. We present the theory of AGPPC, which combines the co-production column concept with a fluid relaxation of the planning problem, alongside an integrated bilinear mixed integer program of the plant and a McCormick-linearized baseline that provides certified bounds for it, and we demonstrate its effectiveness on real operational data from an Indonesian polymer producer, where the coupling-aware metric alone raises operating profit by 7.6% and the full optimization by 28% over current practice.
