Optimization with GAMS: Operations Research Bootcamp A-Z

Explore fundamental concepts of optimization, including objective functions, constraints, linear and nonlinear models, continuous and integer variables, convex versus non-convex problems.

Learn to set up the GAMS IDE, verify licenses, access solvers for linear and mixed-integer problems, manage windows, save models, and locate the GAMS directory.

Explore the basics of linear programming: maximize or minimize a linear objective function under linear constraints with real variables, preparing you to tackle your first LP problem.

Learn to build a basic GAMS linear programming model by defining variables x1 and x2, setting the objective, formulating equations and constraints, and solving with an LP solver.

Formulate and solve a transportation linear program to minimize electricity transmission costs from three power plants to four cities, subject to supply and demand constraints, using a sets-based formulation.

Model a two-plant, four-city linear program in GAMS using sets, parameters, variables, and equations from a PIRG data table to obtain an optimal solution.

In GAMS, this lecture builds an integer programming model for a production planning problem, defining sets, parameters, variables, and constraints, solved to optimal plan: 25 pants and a 75 profit.

minimize distance in a milp challenge using gams by building sets, aliasing i and j, and defining a c i j table with x i j binaries to obtain route.

Model and solve a nonlinear programming problem in GAMS by defining the objective function and constraints, then maximize the walkway using an NLP solver like IPO (demo license).

Learn how to build a GAMS model from scratch by defining variables and an objective, adding linear and nonlinear constraints, and solving with different solvers to compare results.

Formulate a nonlinear programming problem to locate a single warehouse in the plane that minimizes total weighted distance to four customers, using the distance formula and shipments, in gams.

Explore a minlp challenge to design a least-cost centrifugal pump configuration across three levels, lines, and pumps, using discrete binaries and continuous flow fractions under pressure and power constraints.

code and solve a MINLP pump configuration problem in gams, reading data from an excel file, defining sets, parameters, variables, bounds, and constraints, and exporting results to excel.

Review the full optimization workflow from understanding the problem to modeling, solving with Gams, and evaluating results. Learn data input, external reporting, multi‑objective handling, and conditional statements, with recommended readings.