Use constraint programming techniques to compute solutions for detailed scheduling problems and combinatorial optimization problems.
Schedule efficiently with 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.
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)
Resources
Explore applications of constraint programming including production problem and scheduling use cases.
Experience the end-to-end journey of solving an optimization problem.
Represent business problems mathematically to create effective analytical decision-support applications.
Learn about CP Optimizer performance comparison for a job shop scheduling problem.
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