An improved algorithm for the dynamic lot-sizing problem with learning effect in setups

The dynamic lot-sizing problem with learning in setups is a variation of the Wagner-Whitin lot-sizing problem where the setup costs are a concave, nondecreasing function of the cumulative number of setups. This problem has been a subject of some recent research. The authors extend the previously studied model to include nonstationary production costs and present an O(T²) algorithm to solve this problem. The worst-case complexity of the present algorithm improves the worst-case behavior of the algorithms presently known in the literature.