Solving a model in a Decision
Optimization
experiment
Last updated: Nov 28, 2024
Solving a model in a Decision Optimization experiment
The Decision
Optimizationexperiment UI has different views in which you can select data, create models, solve
different scenarios, and visualize the results. You can also save your scenarios for deployment
In Decision
Optimization, a model with a data set is solved by using optimization engines
(software). These optimization engines (IBM CPLEX® and CP Optimizer) produce precise solutions for
the objectives that are stated in the model, by using mathematical programming and constraint
programming algorithms. The solutions are mathematically proven to be the best possible solutions
that respect the constraints listed in the model and the data. An "optimal solution" means that no
better objective value can be possibly found for that model and data set. A "feasible" solution is
one that satisfies all the constraints of the model and data, but is not necessarily optimal. The
event of solving a model with an optimization engine is often referred to in Decision
Optimization as a "solve".
To solve a model, click Run from the menu bar in one of the following
views: Prepare data, Build model, or Explore solutionviews. During the run you can see the different
stages (Starting, Pending, Running, Finishing) of the job in a graphical display. The "solve" occurs
during the Running phase. The current scenario (model with data) is solved by using IBM CPLEX® or CP
Optimizer. The default run environment is used for the solve or, if it doesn’t exist, it is created
automatically. The do_22.1 is used by default to
solve the model. You can change this run environment by configuring the run for your scenario.
You can also solve a model from the Overview by selecting
Run from the menu icon for a scenario.
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