The Effects of Learning on Optimal Lot Sizes: Further Developments on the Single Product Case

This paper reviews the previous research on the problem of determining optimal lot sizes when production costs decrease on a log-linear function. Problems due to simplifying assumptions of the previous work are discussed and modifications of previous models are presented to address more realistic conditions. Using a dynamic programming approach, the new results for two cases of learning, total transmission of learning (from lot to lot) and partial transmission, indicate that optimal lot sizes are increasing in the long-run and long transient states exist, contrary to conclusions of prior papers. The effect of forgetting indicates that operations managers should consider longer production runs for many processes. The revised model formulation presented in this study is also capable of handling demand characteristics of variance, growth and seasonality, significantly extending the application possibilities of the previous models which were limited to a continuous, deterministic demand pattern.