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Solving a model in a Decision Optimization experiment

Solving a model in a Decision Optimization experiment

The Decision Optimization experiment 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 solution views. 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 3 dots icon for a scenario.

You can set and modify certain optimization parameters by clicking the Configure run icon Configure run icon next to the Run button. These parameters are then applied each time that you click Run. For more information, see Configuring the run parameters for a scenario in a Decision Optimization experiment.

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