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IBM ILOG CP OptimizerIBM

Use constraint programming techniques to compute solutions for detailed scheduling problems and combinatorial optimization problems.

Vendor

Vendor

IBM

Company Website

Company Website

Product details

Solve detailed scheduling problems using CP Optimizer

IBM® ILOG® CP Optimizer is a necessary and important complement to the optimization specialist's toolbox for solving real-world operational planning and scheduling problems. ILOG CP Optimizer contains a robust optimizer that handles the side constraints that are invariably found in such challenges. For pure academic problems such as job-shop, open-shop and flow-shop, it finds solutions that are comparable to solutions found by state-of-the-art specialized algorithms.

Certain combinatorial optimization problems cannot be easily linearized and solved with traditional mathematical programming methods. To handle these problems, ILOG CP Optimizer provides a large set of arithmetic and logical constraints, as well as a robust optimizer that brings all the benefits of a model-and-run development process to combinatorial optimization.

Features

  • Detailed scheduling problems - Use modeling features specialized to scheduling like intervals (for activities) and cumul functions (for resources). - Support business goals by optimizing earliness and tardiness costs, duration costs and non-execution costs. - Model the work breakdown structure of the schedule and task dependencies as well as multiple production modes. - Model finite capacity resources and reservoirs. - Model setup times to compute schedules that define the best possible sizes for batches.
  • Combinatorial optimization problems: - Use specialized constraints such as all-different, pack, lexicographic, count and distribute for business problems such as facility location, routing and configuration. - Model with logical constraints as well as a full range of arithmetic expressions, including modulo, integer division, minimum, maximum or an expression, which indexes an array of values by a decision variable. - Model with discrete decision variables (boolean or integer).