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Model Development and Solver Demonstrations Using Randomized Test Problems

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Abstract

Scientific-technical computing (STC) systems have integrated capabilities to support optimization model development and solution, report generation, and visualization. STC systems can be put to good use also in education since students can readily see and discuss the solution of non-trivial, practically motivated models. Based also on our related research and teaching experience, we think that such an approach can be very useful in topical Business Analytics/Industrial Engineering/Management Science/Operations Research (BA/IE/MS/OR) courses. Classroom discussions and hands-on explorations can directly lead to proposing and testing model variants, devising problem-generation rules, and using solver options. To illustrate the proposed approach, we introduce a general framework to randomly generate, solve, and visualize scalable optimization problems arising in facility dispersion applications. Our model development study has been implemented in Mathematica. The proposed model class is transferable also to other model development environments such as AMPL, GAMS, Julia, LINGO, Maple, MATLAB, Python, and others (calling external visualization tools when required). The proposed model generation framework can be adapted to handling other classes of randomized optimization problems.

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Data Availability

The Mathematica notebook corresponding to the model is available from the authors.

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Author information

Authors and Affiliations

  1. Physicist at Large Consulting LLC, Bryn Mawr, PA, USA

    Frank J. Kampas

  2. Department of Management Science and Information Systems, Rutgers Business School, Rutgers University, NJ, New Brunswick, USA

    János D. Pintér

  3. Lazaridis School of Business and Economics, Wilfrid Laurier University, Waterloo, ON, Canada

    Ignacio Castillo

Authors
  1. Frank J. Kampas
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  2. János D. Pintér
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  3. Ignacio Castillo
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Correspondence to Ignacio Castillo.

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Kampas, F.J., Pintér, J.D. & Castillo, I. Model Development and Solver Demonstrations Using Randomized Test Problems. Oper. Res. Forum 4, 13 (2023). https://doi.org/10.1007/s43069-022-00190-4

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