Optimize your machine-learning decisions

Learn how optimization and machine learning techniques can be combined for improved business outcomes.

Solve detailed scheduling problems using CP Optimizer

IBM ILOG® CP Optimizer is a necessary and important complement to the optimization specialists' toolbox for solving real-world operational planning and scheduling problems. CP Optimizer contains a robust optimizer that handles the side constraints that are invariably found in such challenges. For pure academic problems (for example, 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)


ILOG CPLEX Optimization Studio

Build and solve complex optimization models to identify the best possible actions that your users should take by using powerful decision optimization algorithms.

Decision Optimization for Watson Studio

Build and deploy optimization models in a unified environment and drive business results by combining machine learning and optimization techniques.

IBM Decision Optimization Center

Use this platform to build a development platform and GUI, and support data analysis and visualization, scenario management, collaborative planning and what-if analysis.

IBM Decision Optimization on Cloud

Use optimization software on the cloud to quickly find optimal solutions for your planning and scheduling, supply chain and asset management challenges.



Explore key capabilities of ILOG CPLEX Optimization Studio.

Product tour: ILOG CPLEX Optimization Studio

Experience the end-to-end journey of solving an optimization problem.

Decision Optimization for Watson Studio

Capitalize on the power of prescriptive analytics and build innovative solutions by combining decision optimization and machine learning.

Constraint programming technology

Constraint programming technology is used to find solutions to scheduling and combinatorial optimization problems.

Optimization model

Represent business problems mathematically to create effective analytical decision-support applications.

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