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Linear and Mixed Integer Programming

  • Hartmut Stadtler

Abstract

Linear Programming (LP) is one of the most famous optimization techniques introduced independently by Kantarowitsch in 1939 and by Dantzig in 1949 (Krekó, 1973). LP is applicable in decision situations where quantities (variables) can take any real values only restricted by linear (in-) equalities, e. g. for representing capacity constraints. Still, LP has turned out to be very useful for many companies so far. LP is used in APS e. g. in Master Planning as well as in Distribution and Transport Planning. Very powerful solution algorithms have been developed (named solvers), solving LP models with thousands of variables and constraints within a few minutes on a personal computer.

Keywords

Feasible Solution Search Tree Mixed Integer Programming Integer Solution Linear Program Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. DASH Associates (2000) Homepage, URL: http://www.dash.co.uk, State: May 12, 2000Google Scholar
  2. ILOG CPLEX Division (2000) Homepage, URL: http://www.ilog.com/products/cplex, State: May 12, 2000Google Scholar
  3. Krekó, B. (1973) Lehrbuch der Linearen Optimierung, BerlinGoogle Scholar
  4. Martin, R. K. (1999) Large scale linear and integer optimization: A unified approach, Boston et al.CrossRefGoogle Scholar
  5. Winston, W. L. (1994) Operations Research: Applications and algorithms, 3rd ed., Belmont, CaliforniaGoogle Scholar
  6. Wolsey, L. A. (1998) Integer Programming, New York et al.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Hartmut Stadtler
    • 1
  1. 1.Institute of Business Administration, Department of Operations and Materials ManagementDarmstadt University of TechnologyDarmstadtGermany

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