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Modeling optimization problems in the unstructured world of spreadsheets

Author

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  • Conway, D. G.
  • Ragsdale, C. T.

Abstract

Electronic spreadsheets are the most common software tool managers use to analyze data and model quantitative problems. Increasingly, these software packages are being used in introductory OR/MS courses to introduce students to a variety of quantitative modeling tools. Because spreadsheets are inherently free-form, they impose no particular guidelines or structure on the way problems may be modeled. Thus, academics and practitioners accustomed to solving problems using very structured, dedicated OR/MS software packages are facing the challenge of dealing with these problems in the unstructured spreadsheet environment where there is often a variety of ways to implement and solve the same problem. This challenge is particularly acute in the case of optimization problems. Some are responding to this challenge by devising rules for implementing models that impose an artificial structure on spreadsheets, sometimes resembling the operation of dedicated OR/MS optimization packages. This paper offers a critique of this approach and provides some guidelines we believe to be more helpful in creating effective spreadsheet models for optimization problems.

Suggested Citation

  • Conway, D. G. & Ragsdale, C. T., 1997. "Modeling optimization problems in the unstructured world of spreadsheets," Omega, Elsevier, vol. 25(3), pages 313-322, June.
    • Handle: RePEc:eee:jomega:v:25:y:1997:i:3:p:313-322
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    Citations

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    Cited by:

    1. LeBlanc, Larry J. & Grossman, Thomas A. & Bartolacci, Michael R., 2019. "Ensuring scalability and reusability of spreadsheet linear programming models," Omega, Elsevier, vol. 84(C), pages 55-69.
    2. Cunha, Claudio Barbieri & Mutarelli, Fernando, 2007. "A spreadsheet-based optimization model for the integrated problem of producing and distributing a major weekly newsmagazine," European Journal of Operational Research, Elsevier, vol. 176(2), pages 925-940, January.
    3. John F. Raffensperger, 2003. "New Guidelines for Spreadsheets," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 2(2), pages 141-154, August.
    4. Larry J. LeBlanc & Dale Randels & T. K. Swann, 2000. "Heery International's Spreadsheet Optimization Model for Assigning Managers to Construction Projects," Interfaces, INFORMS, vol. 30(6), pages 95-106, December.
    5. Larry J. LeBlanc & Michael R. Bartolacci & Thomas A. Grossman, 2017. "ASP, The Art and Science of Practice: Increasing Productivity and Minimizing Errors in Operations Research Spreadsheet Models," Interfaces, INFORMS, vol. 47(3), pages 260-269, June.
    6. John Miltenburg, 2019. "Online Teaching in a Large, Required, Undergraduate Management Science Course," INFORMS Transactions on Education, INFORMS, vol. 19(2), pages 89-104, January.
    7. King, Malcolm, 1997. "Some comments on "modeling optimization problems in the unstructured world of spreadsheets"," Omega, Elsevier, vol. 25(5), pages 595-598, October.
    8. Lawson, Barry R. & Baker, Kenneth R. & Powell, Stephen G. & Foster-Johnson, Lynn, 2009. "A comparison of spreadsheet users with different levels of experience," Omega, Elsevier, vol. 37(3), pages 579-590, June.
    9. Larry J. LeBlanc & Michael R. Galbreth, 2007. "Implementing Large-Scale Optimization Models in Excel Using VBA," Interfaces, INFORMS, vol. 37(4), pages 370-382, August.

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