Compara mètodes

	Disseny de Box-Behnken assistit per optimització	Disseny Central Composite Assistit per Optimització
Camp	Disseny experimental	Disseny experimental
Família	Process / pipeline	Process / pipeline
Any d'origen	1960 (BBD); optimization integration established 1980s–1990s	1951 (CCD); optimization coupling formalized 1970s–1990s
Autor original	Box & Behnken (design); Derringer & Suich (desirability optimization)	Box & Wilson (CCD, 1951); optimization integration by Myers, Montgomery & colleagues
Tipus	Experimental design with post-modeling optimization	Experimental design with mathematical optimization
Font seminal	Box, G. E. P., & Behnken, D. W. (1960). Some new three level designs for the study of quantitative variables. Technometrics, 2(4), 455–475.	Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2009). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (3rd ed.). Wiley. ISBN: 978-0470174463
Àlies	BBD with optimization, Box-Behnken design optimization, RSM-BBD optimization, Box-Behnken response optimization	CCD with optimization, optimized CCD, RSM-CCD optimization, central composite design with response optimization
Relacionats	5	3
Resum	Optimization-assisted Box-Behnken design (BBD) combines the Box-Behnken three-level experimental design with a formal optimization step to locate factor settings that maximize, minimize, or hit a target for one or more responses. BBD fits a second-order response surface model using fewer runs than a full factorial, and the optimization stage — typically via desirability functions or numerical search — then exploits that fitted model to identify the true optimum within the experimental region.	Optimization-assisted central composite design (CCD) combines the rotatable, second-order experimental layout of central composite design with mathematical optimization algorithms — typically desirability functions, response surface optimization, or metaheuristics — to find the factor settings that simultaneously maximize, minimize, or hit target values for one or more response variables. It is the most widely applied response-surface optimization workflow in chemical, pharmaceutical, food science, and manufacturing engineering.