  The main goal of this paper was to analyze the importance that the effects of
learning and forgetting might have in a lot-sizing problem. It assumes that the
learning curve and the economies of scale are present in several industries yet
are, in most cases, not considered when dealing with a lot-sizing problem. The
importance of the effects was demonstrated and quantified, showing that there
is still space for developments in this field. However, as the problem becomes
quadratic, there is a possibility that the current algorithms are not able to
solve the problem to optimality. Thus, future improvements in the algorithms
may further improve the results. However, the overall results found with
current algorithms show that the contribution of a discount from a learning
curve can be very considerable, even if it is a minimal amount.
