Model Development and Solver Demonstrations Using Randomized Test Problems

Frank J. Kampas, János D. Pintér and Ignacio Castillo ()
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Frank J. Kampas: Physicist at Large Consulting LLC
János D. Pintér: Rutgers University
Ignacio Castillo: Wilfrid Laurier University

SN Operations Research Forum, 2023, vol. 4, issue 1, 1-15

Abstract: 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.

Keywords: Scientific-technical computing systems in education and research; Model development and nonlinear optimization in Mathematica; Facility dispersion problems; Randomly generated model instances; Numerical solution and visualization (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s43069-022-00190-4

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