The responses obtained from response surface designs that are run sequentially often exhibit serial correlation or time trends. The order in which the runs of the design are performed then has an impact on the precision of the parameter estimators. This article proposes the use of a variable-neighbourhood search algorithm to compute run orders that guarantee a precise estimation of the effects of the experimental factors. The importance of using good run orders is demonstrated by seeking D-optimal run orders for a central composite design in the presence of an AR(1) autocorrelation pattern.

AR(1), D-optimality criterion, autocorrelation, central composite design, local search, time trend
ERIM Article Series (EAS)
Journal of Statistical Planning and Inference
Erasmus Research Institute of Management

Garroi, J-J, Goos, P.P, & Sörensen, K. (2009). A variable-neighbourhood search algorithm for finding optimal run orders in the presence of serial correlation. Journal of Statistical Planning and Inference, 139(1), 30–44. Retrieved from