This article will serve as your comprehensive guide to optimization using GAMS, exploring why it remains a gold standard in academia and industry for solving Linear Programming (LP), Nonlinear Programming (NLP), Mixed-Integer Programming (MIP), and large-scale equilibrium problems.
In the meantime, here are a few well-known resources on in Operations Research: Optimization with GAMS- Operations Research Boo...
cost.. z =e= sum((i,j), distance(i,j) * x(i,j)); supply(i).. sum(j, x(i,j)) =l= capacity(i); demand(j).. sum(i, x(i,j)) =g= 500; * Assume fixed demand of 500 This article will serve as your comprehensive guide
For economic equilibrium (e.g., Nash equilibrium in oligopolies, Walrasian equilibrium), traditional optimization fails because there is no single objective function. GAMS supports Mixed Complementarity Problems natively, allowing you to model "supply = demand or price = zero" conditions directly. Nonlinear Programming (NLP)