Two procedures for estimating initial states of a production line that ensure the line has a high probability of meeting the specified production target during a scheduled production shift are presented. The problem of determining desirable initial states is important in low variety, high volume production systems such as those from the automobile industry. One procedure is derived from design of experiments (DOE) theory whereas the other uses a genetic algorithm (GA). In the study it was determined that both procedures are straightforward to implement and produce good solutions to the problem. The results from the procedures are compared and their benefits and disadvantages are discussed.
