Optimization Programming Language

OPL allows users to define models using high-level mathematical notation. Its primary features include: • Separation of Model and Data: OPL promotes a clean architecture where the optimization logic (stored in files) is kept separate from the instance data (stored in files). • Hybrid Modeling: It is one of the few AMLs that natively supports both Mathematical Programming (MP) and Constraint Programming (CP) within the same environment. • Scheduling Support: OPL includes specialized primitives for scheduling problems, such as interval variables, sequence variables, and cumulative functions. • Scripting: It includes IBM ILOG Script, a JavaScript-based language used for data pre-processing, controlling the solving flow (e.g., solving a sequence of models), and post-processing results. == Example ==

The following is a simple OPL model (.mod) for a Knapsack problem: // Knapsack capacity, provided externally in the data file int capacity = ...; // Item index set range Items = 1..5; // Item parameters: one value and one weight per item in Items int value[Items] = [6, 7, 3, 9, 6]; int weight[Items] = [11, 7, 9, 13, 12]; // Decision variable: // x[i] = 1 if item i is selected // x[i] = 0 otherwise dvar boolean x[Items]; // obj: maximize total value of selected items maximize obj: sum(i in Items) value[i] * x[i]; subject to { // total weight of selected items cannot exceed capacity sum(i in Items) weight[i] * x[i] The associated data (.dat) file is capacity = 10 == Integration ==

While OPL is typically used within the CPLEX Studio IDE, it can also be deployed in production environments. IBM provides APIs (such as the Concert Technology) that allow OPL models to be called from Java, C++, .NET, and Python. == See also ==