A Dynamic Lot Sizing Model with Learning in Setups

This paper considers the dynamic lot sizing problem of H. M. Wagner and T. M. Whitin with the assumption that the total cost of n setups is a concave nondecreasing function of n. Such setup costs could arise from the worker learning in setups and/or technological improvements in setup methods. An efficient dynamic programming algorithm is developed to solve a finite horizon problem and results are presented to find decision/forecast horizons. Several new results presented in the paper have potential use in solving other related problems.