A hybrid heuristic to solve the two dimensional cutting stock problem with consideration of forecasts

This paper considers a two dimensional guillotine cutting stock problem as a bin packing problem. Many pieces with different dimensions have to be cut with different quantities in order to satisfy customers' orders. In addition to the firm orders, forecast plans are considered as new constraints to be taken into account. In this paper, a hybrid heuristic is developed, based on the combination of the bottom left and the shelf algorithms. Several experimental tests are reported to demonstrate the validity and the performance of the heuristic. In fact, the proposed heuristic reduces the waste rate for all the considered tests in very short computational time. Results show that integrating the forecast constraints is actually more an additional way to improve the trim loss than a real constraint.