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.