Hybrid: Multicriteria Linear Programming System for Computers under DOS and Unix

  • Marek Makowski
  • Janusz S. Sosnowski
Conference paper
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 397)

Abstract

Hybrid is a mathematical programming package which includes all the functions necessary for the solution of single and multicriteria LP problems. Hybrid uses a non-simplex method which combines the proximal multiplier method and an active set algorithm and has been prepared in two versions: one for UNIX (currently implemented for Sun Sparc running under SunOS 4.1) and one for a PC compatible with the IBM PC.

Keywords

Ditioned Univer 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bertsekas, D.P. (1982). Constrained Optimization and Lagrange Multiplier Methods. Academic Press, New York.Google Scholar
  2. Flecher, R. (1981). Practical Methods of Optimization. Vol, II: Constrained optimization. John Wiley, New York.Google Scholar
  3. Gill, P.E., W. Murray, and M.H. Wright (1981) Practical Optimization. Academic Press. London New York.Google Scholar
  4. Golub, G.H., and C.F. Van Loan (1983). Matrix Computations. Johns Hopkins University Press, Baltimore, Maryland.Google Scholar
  5. Lewandowski, A., and A.P. Wierzbicki (eds.) (1989). Aspiration Based Decision. Lecture Notes in Economics and Mathematical System, Vol. 331. Springer-Verlag, Berlin.Google Scholar
  6. Makowski, M., and J.S. Sosnowski (1989). Mathematical Programming Package HYBRID. In: A. Lewandowski, and A.P. Wierzbicki, (eds.) Aspiration Based Decision. Lecture Notes in Economics and Mathematical System, Vol. 331. Springer-Verlag, Berlin.Google Scholar
  7. Makowski, M., and J.S. Sosnowski (1991). HYBRID-FMS: An Element of DSS for Designing Flexible Manufacturing Systems, In: P. Korhonen, A. Lewandowski, and J. Wallenius (eds.) Multiple Criteria Decision Support. Lecture Notes in Economics and Mathematical Systems, Vol. 356. Springer-Verlag, Berlin.Google Scholar
  8. Rockafellar, R.T. (1976). Augmented Lagrangians and Applications of the Proximal Point Algorithm in Convex Programming. Mathematics of Operations Research, No 1, pp. 97–116.Google Scholar
  9. Sosnowski, J.S. (1990a). A stable method for solving multicriteria linear programming problems in a decision support system. In: M. Fedrizzi and J. Kacprzyk (eds.) Systems Analysis and Decision Support in Economics and Technology. Proceedings of 8th Italian-Polish Symposium. Omnitech Press, Warsaw.Google Scholar
  10. Sosnowski, J.S (1990b). A method for minimization of piecewise quadratic functions with lower and upper bounds. CP-90–003. HAS A, Laxenburg.Google Scholar
  11. Steuer, R.E. (1982). On sampling the efficient set using weighted Tchebycheff metrics. In: M. Grauer, A. Lewandowski, and A.P. Wierzbicki (eds.) Multiobjective and Stochastic Optimization. Collaborative Proceedings CP-82-S12. IIASA, Laxenburg.Google Scholar
  12. Wierzbicki, A. (1980). A mathematical basis for satisficing decision making. WP-80–90. IIASA, Laxenburg.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1993

Authors and Affiliations

  • Marek Makowski
    • 1
  • Janusz S. Sosnowski
    • 2
  1. 1.IIASALaxenburgAustria
  2. 2.Systems Research InstitutePolish Academy of SciencesWarsawPoland

Personalised recommendations