Deterministic time-varying demand lot-sizing models with learning and forgetting in setups and production

We study the deterministic time-varying demand lot-sizing problem in which learning and forgetting in setups and production are considered simultaneously. It is an extension of Chiu's work. We propose a near-optimal forward dynamic programming algorithm and suggest the use of a good heuristic method in a situation in which the computational effort is extremely intolerable. Several important observations obtained from a two-phase experiment verify the goodness of the proposed algorithm and the chosen heuristic method.