SOFTWARE TOOLS FOR SOLVING LINEAR PROGRAMMING PROBLEMS
Description
Linear programming has become one of the most powerful and practical mathematical optimization techniques for decision making in diverse fields such as economics, engineering, transportation, and management science. The continuous growth in computational power and the availability of sophisticated software environments have significantly simplified the formulation and solution of complex LP problems. This paper provides an analytical review of major software tools used for solving LP problems including MATLAB, IBM ILOG CPLEX, Gurobi, LINGO, GLPK, and Python based libraries such as PuLP, SciPy, and Pyomo along with a detailed overview of Excel Solver as an accessible tool for beginners. The article highlights their algorithmic structures, performance characteristics, and real world applicability, concluding with a comparative discussion of their ad
vantages and limitations.
Files
DIZZW #116_October_jornal-79-81.pdf
Files
(401.0 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:efedb7080927ec274c55b08eb0a587bb
|
401.0 kB | Preview Download |
Additional details
References
- 1. Winston, W. L. Operations Research: Applica tions and Algorithms. Cengage Learning 2. IBM Corporation. (2024). IBM ILOG CPLEX Optimization Studio Documentation. 3. Gurobi Optimization LLC. (2024). Gurobi Optimizer Reference Manual. 4. The MathWorks, Inc. (2023). MATLAB Opti mization Toolbox User's Guide. 5. Makhorin, A. (2023). GNU Linear Program ming Kit (GLPK) User Manual. Free Software Founda tion. 6. Mitchell, S., & Hart, W. E. (2020)Pyomo – Optimization Modeling in Python. Springer. 7. Microsoft Corporation. (2023). Excel Solver Reference Guide.