Σύγκριση μεθόδων

	Σχεδιασμός Πλήρους Παραγοντικής Ανάλυσης με Υποβοήθηση Προσομοίωσης	Μεθοδολογία Επιφανειών Απόκρισης με Υποβοήθηση Προσομοίωσης
Πεδίο	Πειραματικός Σχεδιασμός	Πειραματικός Σχεδιασμός
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	1990s–2000s (simulation-DOE integration formalized)	1951 (RSM); simulation integration widely adopted from 1980s onward
Δημιουργός	Montgomery (DOE foundations); Kleijnen (simulation DOE formalization)	Box & Wilson (RSM foundation); Kleijnen and others for simulation-based extensions
Τύπος	Experimental design with computer simulation	Experimental optimization method
Θεμελιώδης πηγή	Montgomery, D. C. (2017). Design and Analysis of Experiments (9th ed.). Wiley. ISBN: 978-1119113478	Myers, R. H., Montgomery, D. C., & Anderson-Cook, C. M. (2016). Response Surface Methodology: Process and Product Optimization Using Designed Experiments (4th ed.). Wiley. ISBN: 978-1118916025
Εναλλακτικές ονομασίες	SA-FFD, computer simulation full factorial, virtual full factorial design, simulation-based full factorial DOE	SA-RSM, simulation-based RSM, computer simulation RSM, metamodel-assisted RSM
Συναφείς	4	6
Σύνοψη	Simulation-assisted full factorial design integrates full factorial design of experiments (DOE) with computer simulation models — such as discrete-event simulation, finite element analysis, or Monte Carlo methods — to systematically explore every combination of factor levels and quantify their effects on system responses. It enables comprehensive experimentation in contexts where physical trials would be costly, dangerous, or infeasible.	Simulation-assisted response surface methodology (SA-RSM) combines computer simulation models — such as finite element analysis, computational fluid dynamics, or discrete-event simulation — with the statistical framework of response surface methodology to efficiently map, model, and optimize system responses. Instead of running physical experiments, the researcher executes simulation runs at design points prescribed by an RSM design, fits a polynomial metamodel (surrogate) to the simulation outputs, and uses that metamodel to locate optimal factor settings.