ILOG CPLEX can work together with ILOG Concert Technology for .NET users, a C#.NET library that allows you to model optimization problems independently of the algorithms used to solve the problem. It provides an extensible modeling layer adapted to a variety of algorithms ready to use off the shelf. This modeling layer enables you to change your model, without completely rewriting your application.

To find a solution to a problem by means of ILOG CPLEX with ILOG Concert Technology for .NET users, you use a three-stage method: describe, model, and solve.

The first stage is to describe the problem in natural language.

The second stage is to use the classes and interfaces of ILOG Concert Technology for .NET users to model the problem. The model is composed of data, decision variables, and constraints. Decision variables are the unknown information in a problem. Each decision variable has a domain of possible values. The constraints are limits or restrictions on combinations of values for these decision variables. The model may also contain an objective, an expression that can be maximized or minimized.

The third stage is to use the classes of ILOG Concert Technology for .NET users to solve the problem. Solving the problem consists of finding a value for each decision variable while simultaneously satisfying the constraints and maximizing or minimizing an objective, if one is included in the model.

In these tutorials, you will describe, model, and solve a simple problem that also appears elsewhere in C, C++, and Java versions of this manual:

The first step is for you to describe the problem in natural language and answer basic questions about the problem.

The second stage is for you to use the classes of ILOG Concert Technology for .NET users to build a model of the problem. The model is composed of decision variables and constraints on those variables. The model of this problem also contains an objective.

The third stage is for you to use an instance of the class Cplex to search for a solution and to solve the problem. Solving the problem consists of finding a value for each variable while simultaneously satisfying the constraints and minimizing the objective.