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.
Learn about black-box expressions in CP Optimizer
- 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.
- 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).
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.
Build and solve complex optimization models to identify the best possible actions that your users should take by using powerful decision optimization algorithms.
Combine optimization and machine learning techniques within IBM Cloud Pak® for Data, a multicloud data and AI platform that enables you to build and run AI models on any cloud and on premises.
Use this platform to build a development platform and GUI, and support data analysis and visualization, scenario management, collaborative planning and what-if analysis.
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