Optimization-Assisted Fractional Factorial Design

Optimization-assisted fractional factorial design (OA-FFD) combines classical fractional factorial screening with algorithmic optimality criteria — such as D-, I-, or A-optimality — to construct experiment matrices that maximize statistical efficiency. Instead of relying solely on standard orthogonal-array tables, a computer algorithm selects the best subset of runs from a candidate set, enabling experimenters to handle irregular factor constraints, mixed factor types, and custom run sizes that standard tables cannot accommodate.