方法对比

	优化辅助全因子设计	多响应全因子设计
领域	实验设计	实验设计
方法族	Process / pipeline	Process / pipeline
起源年份	1980s–1990s (formalized with desirability functions by Derringer & Suich, 1980)	1950s–1980s
提出者	Integrated from D. C. Montgomery (DoE) and classical optimization literature	Douglas C. Montgomery (factorial framework); Derringer & Suich (multi-response desirability optimization)
类型	Hybrid experimental-optimization workflow	Experimental design with multi-objective optimization
开创性文献	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119492443
别名	OA-FFD, full factorial with optimization, full factorial design with response optimization, DoE-optimization hybrid	MRFFD, multi-response FFD, multiple-response full factorial, multi-objective full factorial design
相关	3	3
摘要	Optimization-assisted full factorial design is a structured engineering workflow that runs a complete full factorial experiment — covering every combination of factor levels — and then applies a formal optimization method to identify the factor settings that best satisfy one or more performance targets. It combines the exhaustive data coverage of full factorial design with numerical or analytical optimization to turn experimental results into actionable optimal configurations.	Multi-response full factorial design extends the classic full factorial experiment by measuring and jointly optimizing two or more response variables at the same time. Every combination of all factor levels is tested, providing complete main-effect and interaction information for each response. A desirability function or Pareto-front approach then reconciles competing responses into a single optimal factor setting, making this the method of choice when engineering or process goals involve trade-offs among several quality characteristics simultaneously.