Modeling learning effects via successive linear programming

A learning effect occurs when the amount of labor required per unit of production decreases as cumulative production increases. Learning effects occur in many situations, and this effect can be especially significant in the startup of a new process. However, from a modeling viewpoint, the inclusion of learning effects can result in a problem that is considerably more difficult to solve than when these effects are ignored. A number of mathematical programming solution methods have been proposed for modeling a learning effect. Unfortunately, these methods frequently require the implementation of sophisticated algorithms. In this paper the authors develop an alternative solution strategy for modeling a learning effect based upon the use of Successive Linear Programming (SLP). This approach is particularly attractive in that it can be easily implemented, and only requires access to a linear programming package.