What is Decision Optimization?

Decision Optimization gives you access to IBM's world-leading solution engines for mathematical programming and constraint programming.

People frequently use the term optimization to mean making something better. Although optimization often makes things better, it means a lot more than that: optimization means finding the most appropriate solution to a precisely defined situation. It is a sophisticated analytics technology, also called Prescriptive Analytics, which can explore a huge range of possible scenarios before suggesting the best way to respond to a present or future situation.

The image shows the process followed to solve a business problem using optimization.
  1. The situation is generally a business problem, such as complex planning, scheduling, pricing, inventory, or resource management. It is one a multitude of operational problems that are beyond the capabilities of the human brain or standard office software.
  2. Whatever the problem is, resolving it starts with the optimization model, which is the mathematical formulation of the problem that can be interpreted and solved by an optimization engine. The optimization model specifies the relationships among the goals, limits, and choices that are involved in the decisions. But it is the input data that makes these relationships concrete. An optimization model for production planning, for example, may have the same form whether you are producing three products or a thousand. The optimization model plus the input data creates an instance of an optimization problem.
  3. Optimization engines (or solvers) apply mathematical algorithms to find a solution, a set of decisions that achieves the best values of the goals and respects limits imposed. The optimization engine implements specialized algorithms that have been developed and tuned to efficiently solve a large variety of different problems. Decision Optimization uses the IBM CPLEX optimization engines that have proven particularly useful for real-world applications.
  4. The solution that emerges from the solver details the recommended values for all of the decisions that are represented in the model. Equally important are the metric values that represent the targets. These values measure the quality of the solution in terms of the business goals.
  5. All the useful mathematical optimization machinery is useless without a complementary application that makes it available to business users. Usually, the solution and goals are summarized in tabular or graphical views that provide understanding and insight.