Titlebook: Linear Programming and its Applications; H. A. Eiselt,C. -L. Sandblom Book 2007 Springer-Verlag Berlin Heidelberg 2007 Linear Programming.

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Computational Complexity,the effort it will take to perform these calculations. Since the days of the abacus, the slide rule, and the mechanical calculating machine, our ability to handle vast amounts of calculations has developed to an extraordinary extent. However, at the same time, the need for ever more complex and larg

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tolerance

Applications,are designed to give readers a flavor of the application, rather than a model that includes all of the bells and whistles usually found in real applications. We have already discussed an example of the first large class of applications in the Introduction, ., production problems. Readers may recall

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Metastasis

Extensions of the Simplex Method,troduces a technique that allows variables to be constraint by upper bounds without introducing these bounds in the tableau explicitly, thus keeping the tableaus small. The second section describes the column-generation technique, another method that has proven to be an invaluable tool for large-sca

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Non-Simplex Based Solution Methods,ul in practice for the last half century, the simplex method is by no means the only solution method for linear programming. Interestingly enough, the first alternative solution technique to the simplex method, a method by Brown and Koopmans (1951), was published in the same volume in which Dantzig

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Accommodation

Multiobjective Programming,umstances (e.g., when a well-established company attempts to maximize its profit), it will be unsatisfactory in others. In particular, when there is more than a single stakeholder as is the case in virtually all public decision making problems, or more than a single concern exists. Typical concerns