Effect of process learning on manufacturing schedules

The capacity constrained lot sizing problem with learning is modeled as a nonlinear mixed integer program. Three solution techniques for solving the model are investigated. A nonlinear programming package together with the branch and bound technique is used to obtain a solution to the exact problem. The issue of nonconvexity is discussed. In the piecewise linearization of the learning curve, the problem is represented by a mixed integer programming model. Finally, a heuristic that gives near optimal solutions in minimal computer time is developed. Process learning results in a reduction in the number of setups and an increase in inventory level.