Author:

Keywords:

A-Optimality, D-Optimality, Easy-to-Change Factors, Hard-to-Change Factors, Split-Split-Plot Design, Strip-Plot Design, Three-Stage Processes, Update Formulas, Science & Technology, Technology, Physical Sciences, Engineering, Industrial, Operations Research & Management Science, Statistics & Probability, Engineering, Mathematics, RESPONSE-SURFACE DESIGNS, MULTISTAGE PROCESSES, FACTORIAL-DESIGNS, SPLIT-PLOT, 0104 Statistics, 0913 Mechanical Engineering, 1503 Business and Management, 3507 Strategy, management and organisational behaviour, 4905 Statistics

Abstract:

Strip-plot designs are commonly used in situations where the production process consists of two process stages involving hard-to-change factors and where it is possible to apply the second stage to semifinished products from the first stage. In this paper, we focus on three-stage processes. As opposed to the threestage strip-plot designs in the literature, the third stage does not involve hard-to-change factors but easyto- change factors that are reset independently for each run. For this scenario, the split-split-plot design is a well-known alternative design option. However, we prefer the more statistically efficient strip-plot designs and, therefore, we construct D-optimal strip-plot designs for three-stage processes with no randomization restriction in the third stage. The coordinate-exchange algorithm we use to construct our designs can handle any type of factor and any number of factor levels, runs, rows, and columns.