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A Capacity allocation problem with integer side constraints

Author: Toktay, L. Beril ; Uzsoy, RehaINSEAD Area: Technology and Operations ManagementIn: European Journal of Operational Research, vol. 109, no. 1, aug. 1998 Language: EnglishDescription: p. 170-182.Type of document: INSEAD ArticleNote: Please ask us for this itemAbstract: The authors address a capacity allocation problem arising as a subproblem of an artificial intelligence based scheduling system for a semiconductor wafer fabrication facility. Tooling constraints, setup considerations and differences in machine capabilities are taken into account. Focusing on the objectives of maximizing throughput and minimizing deviation from predetermined production goals, the authors formulate the problem as a mawimum flow problem on a bipartite network with integer side constraints and develop efficient heuristics which obtain near-optimal solutions in negligible computation time. The type of network flow problem they study has not been addressed in the literature to date and is of considerable theoretical interest. From a practical point of view, the network flow model of the problem and the algorithms developed for its solution are applicable to a wide range of production settings
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The authors address a capacity allocation problem arising as a subproblem of an artificial intelligence based scheduling system for a semiconductor wafer fabrication facility. Tooling constraints, setup considerations and differences in machine capabilities are taken into account. Focusing on the objectives of maximizing throughput and minimizing deviation from predetermined production goals, the authors formulate the problem as a mawimum flow problem on a bipartite network with integer side constraints and develop efficient heuristics which obtain near-optimal solutions in negligible computation time. The type of network flow problem they study has not been addressed in the literature to date and is of considerable theoretical interest. From a practical point of view, the network flow model of the problem and the algorithms developed for its solution are applicable to a wide range of production settings

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