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Representation Schemes for Linear Programming Models

Author

Listed:
  • Frederic H. Murphy

    (School of Business, Temple University, Philadelphia, Pennsylvania 19103-6417)

  • Edward A. Stohr

    (Stern School of Business, New York University, New York, New York 10006)

  • Ajay Asthana

    (Andersen Consulting, Chicago, Illinois)

Abstract

Because of the difficulties often experienced in formulating and understanding large-scale models, much current research is directed towards developing systems to support the construction and understanding of management science models. This paper discusses eight different methods for representing linear programming models during the formulation phase. The approaches discussed are matrix generators, block-schematic and algebraic languages, three different kinds of graphical schemes, a database-oriented approach and Structured Modeling. While these eight approaches do not cover the entire spectrum of possible representation schemes, they are representative of past and current approaches to developing interfaces for large-scale linear programming systems. The different model representation schemes are compared using a common example and the transformations that allow one to change from one representation to another are discussed.

Suggested Citation

  • Frederic H. Murphy & Edward A. Stohr & Ajay Asthana, 1992. "Representation Schemes for Linear Programming Models," Management Science, INFORMS, vol. 38(7), pages 964-991, July.
    • Handle: RePEc:inm:ormnsc:v:38:y:1992:i:7:p:964-991
    DOI: 10.1287/mnsc.38.7.964
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    Citations

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    Cited by:

    1. Binbasioglu, Meral, 1995. "Key features for model building decision support systems," European Journal of Operational Research, Elsevier, vol. 82(3), pages 422-437, May.
    2. repec:dau:papers:123456789/4171 is not listed on IDEAS
    3. Lin, Suh-Yun Elva & Schuff, David & St. Louis, Robert D., 2000. "Subscript-free modeling languages: A tool for facilitating the formulation and use of models," European Journal of Operational Research, Elsevier, vol. 123(3), pages 614-627, June.
    4. Draman, Murat & Kuban Altinel, I & Bajgoric, Nijaz & Tamer Unal, Ali & Birgoren, Burak, 2002. "A clone-based graphical modeler and mathematical model generator for optimal production planning in process industries," European Journal of Operational Research, Elsevier, vol. 137(3), pages 483-496, March.
    5. David Schuff & Karen Corral & Robert D. St. Louis & Greg Schymik, 0. "Enabling self-service BI: A methodology and a case study for a model management warehouse," Information Systems Frontiers, Springer, vol. 0, pages 1-14.
    6. Santhanam, Radhika & Kyparisis, George J., 1996. "A decision model for interdependent information system project selection," European Journal of Operational Research, Elsevier, vol. 89(2), pages 380-399, March.
    7. David Schuff & Karen Corral & Robert D. St. Louis & Greg Schymik, 2018. "Enabling self-service BI: A methodology and a case study for a model management warehouse," Information Systems Frontiers, Springer, vol. 20(2), pages 275-288, April.

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