| Abstract: | 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. We 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 our algorithm improves the worst-case behavior of the algorithms presently known in the literature. © 1993 John Wiley & Sons, Inc. |
