LOT-SPLITTING DECISIONS AND LEARNING EFFECTS

We consider a lot-splitting model where the unit manufacturing time fo llows a learning curve. Our objective is to maximize the net present v alue of the total revenue collected at the delivery of the sublets. An algorithm is developed to solve the problem. Computational experiment s are conducted to study the performance of two 'convenient' operation al approaches, namely the equal sublet size approach and the equal tim e interval approach. The computational results suggest that these two solution approaches are nearly optimal and that the net present value of the total revenue becomes less sensitive to the sublet sizes as the learning effect increases.