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How to Use IBM ILOG CPLEX Optimization Studio v12.6 ED-EAT for Solving Complex Optimization Problems

IBM ILOG CPLEX Optimization Studio v12.6 ED-EAT is a prescriptive analytics solution that enables rapid development and deployment of decision optimization models using mathematical and constraint programming[^4^]. It can help you transform your business decision-making with data science and optimize your planning and resource allocation problems[^5^]. In this article, we will show you how to use IBM ILOG CPLEX Optimization Studio v12.6 ED-EAT for solving complex optimization problems.

What is IBM ILOG CPLEX Optimization Studio v12.6 ED-EAT

IBM ILOG CPLEX Optimization Studio v12.6 ED-EAT is a version of IBM ILOG CPLEX Optimization Studio that is available for free for students and faculty members through the IBM Academic Initiative[^6^]. It contains all the features of the commercial edition, except that it does not support autoscaling and load balancing, and it has some limitations on deployment options. It allows you to use the Optimization Programming Language (OPL) with the IDE, or model with APIs in C, C++, Java, C# or Python. It also supports modeling with SPSS Modeler deployment facilities. It contains advanced solver functionality for mathematical programming and constraint programming, such as CPLEX Optimizer and CP Optimizer[^4^].

How to Install IBM ILOG CPLEX Optimization Studio v12.6 ED-EAT

To install IBM ILOG CPLEX Optimization Studio v12.6 ED-EAT, you need to register for the IBM Academic Initiative at http://ibm.biz/CPLEXonAI. Once your registration is accepted, you will see a link to download the AI version. You can choose from Windows64, Linux64, and macOS versions. The installation process is straightforward and you can follow the instructions in the documentation[^4^].

How to Use IBM ILOG CPLEX Optimization Studio v12.6 ED-EAT

To use IBM ILOG CPLEX Optimization Studio v12.6 ED-EAT, you can either use the OPL IDE or one of the APIs. The OPL IDE is a graphical user interface that allows you to create, edit, run, and debug optimization models written in OPL. OPL is a high-level modeling language that can express complex optimization problems in a concise and natural way. You can also display results in various formats, such as tables, charts, or Gantt charts[^4^].

If you prefer to use one of the APIs, you can choose from C, C++, Java, C# or Python. The APIs allow you to embed optimization models into your applications and interact with the solvers programmatically. You can also use the APIs to create custom data sources, data structures, or algorithms[^4^].

Example: Solving a Scheduling Problem with OPL

To illustrate how to use IBM ILOG CPLEX Optimization Studio v12.6 ED-EAT, we will solve a simple scheduling problem with OPL. The problem is to assign tasks to machines in a way that minimizes the total completion time of all tasks. Each task has a duration and a machine requirement, and each machine has a capacity. The problem data is given in the following table:

Task Duration Machine

-------------------------

1 10 A

2 15 A

3 20 B

4 25 B

5 30 A or B

The capacity of machine A is 2 and the capacity of machine B is 1.

To model this problem in OPL, we need to define the sets, parameters, variables, objective function, and constraints. The OPL code is shown below:

```

// Sets

{string} Tasks = ...; // set of tasks