Industrial experimentation requires both factor screening to identify critical variables and response optimization to find optimal operating conditions. Traditional approaches treat these as separate phases, necessitating costly sequential experimentation and full experimental redesign between phases. This paper introduces HASOD (Hybrid Adaptive Screening-Optimization Design), a novel three-phase sequential framework that simultaneously addresses factor identification and response surface optimization within a unified adaptive structure. Phase 1 employs a modified Definitive Screening Design with an enhanced Cumulative Weighted Effect Screening Statistic (CWESS) incorporating interaction detection via ElasticNet regression. Phase 2 adaptively selects augmentation strategies -- from full factorial to Response Surface Methodology designs -- based on critical factors identified in Phase 1. Phase 3 applies Gaussian process-based global optimization with uncertainty-guided refinement near the predicted optimum. We prove that CWESS asymptotically separates active from inactive factors, providing classification consistency guarantees absent from most screening methodologies. Across six test scenarios, HASOD achieves 97.08% factor detection accuracy -- 13.75 percentage points above traditional sequential methods (83.33%) -- and significantly outperforms all eight competitor methods (p < 0.001). HASOD yields improved prediction performance (mean error: 3.61) while maintaining >=90% detection across all scenarios including interaction-heavy systems. The framework requires an average of 41.5 experimental runs -- a 43% increase over traditional approaches -- yet delivers superior detection accuracy with dramatically reduced prediction error. HASOD offers a theoretically grounded, unified framework that eliminates sequential redesign without sacrificing predictive capability.

翻译：摘要：工业实验需要同时进行因子筛选以识别关键变量，以及响应优化以寻找最优操作条件。传统方法将这两阶段分离处理，导致阶段间需要进行高成本的序贯实验和完整的实验重新设计。本文提出HASOD（混合自适应筛选-优化设计）——一种新颖的三阶段序贯框架，在统一的自适应结构中同时解决因子识别与响应面优化问题。第一阶段采用改进的确定性筛选设计，结合增强的累积加权效应筛选统计量（CWESS），通过弹性网络回归纳入交互效应检测。第二阶段根据第一阶段识别的关键因子自适应选择扩充策略——从全因子设计到响应面方法论设计。第三阶段应用基于高斯过程的全局优化，在预测最优值附近进行不确定性引导的细化。我们证明CWESS能渐近地区分活跃因子与非活跃因子，提供了大多数筛选方法所不具备的分类一致性保证。在六种测试场景下，HASOD实现了97.08%的因子检测准确率——比传统序贯方法（83.33%）高出13.75个百分点——并显著优于全部八种对比方法（p < 0.001）。HASOD在保持所有场景（包括强交互系统）≥90%检测率的同时，获得了更优的预测性能（平均误差：3.61）。该框架平均需要41.5次实验运行——比传统方法增加43%——但能以显著降低的预测误差提供更优的检测准确率。HASOD提供了一个理论严谨的统一框架，在不牺牲预测能力的前提下消除了序贯重新设计的需求。