An important production programming problem arises in paper industries coupling multiple machine scheduling with cutting stocks. Concerning machine scheduling: how can the production of the quantity of large rolls of paper of different types be determined. These rolls are cut to meet demand of items. Scheduling that minimizes setups and production costs may produce rolls which may increase waste in the cutting process. On the other hand, the best number of rolls in the point of view of minimizing waste may lead to high setup costs. In this paper, coupled modeling and heuristic methods are proposed. Computational experiments are presented.
The multiperiod cutting stock problem arises in the production planning and programming of many industries that have the cutting process as an important stage. Ordered items are required in different periods of a finite planning horizon. It is possible to bring forward or not the production of items. Unused inventory in a certain period becomes available for the next period, all together with new inventory which may come to be acquired in the market. Based on mixed integer optimization models from the literature, extensions are proposed to deal with the multiperiod case and a residual heuristic is used. Computational experiments showed that effective gains can be obtained when comparing multiperiod models with the lot for lot solution, which is typically used in practice. Most of the instances are solved satisfactorily with a high performance optimization package and the heuristic method is used for solving the hard instances.
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