This paper describes a combined linear and dynamic programming model which can be used to determine the real-time optimal chopping of boards into dimension parts in a production optimization system. An adaptive optimization method was used to modify the cutting strategy as the system acquires knowledge of the input raw material quality and inventory levels for parts that are being produced in real time. A simulation was conducted to compare the performance of this model with previously published models. The results demonstrate that the adaptive method has a more consistent performance against cutting lists of different levels of difficulty than previous published models. The study also found that solution updating frequency plays an important role in the performance of the adaptive model. Higher solution updating frequency achieves a higher yield, a lower overproduction and ultimately a lower overall cost.
['Thomas C. Maness', 'Lin Ye', 'Catalin Ristea']
An Adaptive Optimization Method for Chop Saw Systems