Article ID: | iaor20051675 |
Country: | Netherlands |
Volume: | 155 |
Issue: | 1 |
Start Page Number: | 96 |
End Page Number: | 111 |
Publication Date: | May 2004 |
Journal: | European Journal of Operational Research |
Authors: | Sierksma Gerard, Gaalman Gerard J.C., Wanders Henrico L.T. |
Keywords: | manufacturing industries, graphs, heuristics |
A solid board factory produces rectangular sheets of cardboard in two different formats, namely large formats and small formats. The production process consists of two stages separated by an inventory point. In the first stage, a cardboard machine produces the large formats. In the second stage a part of the large formats is cut into small formats by a separate rotary cut machine. Due to very large setup times, technical restrictions, and trim losses, the cardboard machine is not able to produce these small formats. The company follows two policies to satisfy customer demands for rotary cut format orders. When the company applies the first policy, then for each customer order an ‘optimal’ large format (with respect to trim loss) is determined and produced on the cardboard machine. In case of the second policy, a stock of a restricted number of large formats is determined in such a way that the expected trim loss is minimal. The rotary cut format order then uses the most sitable standard large format from the stock. Currently, the dimensions of the standard large formats in the semi-finished inventory are based on intuitive motives, with an accent on minimizing trim losses. On average, the first policy results in a lower trim loss. In order to make efficient use of the two machines and to meet customer's due times the company applies both policies. In this paper, we concentrate on the second policy, taking into account the various objectives and restrictions of the company. The problem is formulated as a minimum clique covering problem with alternatives (MCCA), which is presumed to be NP-hard. We solve the problem by using an appropriate heuristic, which is built into a decision support system. Based on a set of real data, the actual composition of semi-finished inventories is determined. The paper concludes with computational experiments.