Interpreting model run results

In this document you will learn how to see the results of a Model Run using the Platform’s UI, interpret its metrics, analyze the scores, and possible next steps on how to use and tweak the proposed planning solution.

Model run overview page

When a run has completed, the Model Run Overview page gives you a summary of the results from the run. Find this page by clicking the tile of a model, and then picking a run from the Run overview table.

The overview page has the following sections:

  • Sidebar on the right, with (in this order):

    • The run’s status.

    • The run’s metrics.

    • The run’s properties.

  • Optionally, any error or warning messages.

  • The score graph and the hard, medium and soft scores.

  • The list of constraints with their score analysis.

Run status and errors/warnings

A run’s status is indicated at the top right in the sidebar.

Statuses include:

Scheduled

The run is scheduled to start soon.

Solving

The planning problem is currently being solved. During solving the UI will auto-update to reflect the current best solution.

Completed

The run is finished, and completed successfully. The UI will reflect the best solution from the run.

Failed

The run is finished, but there were problems.

If there were any errors or warnings related to the run (e.g. input validation) the overview page will show them.

Run metrics

Each model defines their own metrics. These are metrics that reflect the problem domain. Metrics give an indication of the quality of the provided solution.

Example 1. Field Service Routing metrics
For the field service routing model, the metrics might be the mileage driven, the overall time vehicles spent travelling, or the number of visits that were left unassigned. Read more.
Example 2. Employee Scheduling metrics
For the employee scheduling model, the metrics could include the number of assigned and unassigned shifts. Read more.

The sidebar will show the values for metrics of the final solution, but also indicate the optimization gain. Optimization gain is defined as the difference between the last solution and the first solution. When looking at optimization gain, it’s important to not look at specific metrics in isolation, but put them in context of other metrics.

Run properties

Below the run’s metrics we show the other properties of the run:

  • Any tags added to the run. You can easily add more tags or edit existing ones. Use tags to make runs easier to find and compare.

  • The move speed. This is an indicator of how quickly Timefold is exploring different solutions and Timefold’s performance.

  • The ID and the runtime.

  • The time the run was submitted, started, and completed.

Score graph and scores

The score of a model run is an indication of its quality. The higher the score, the better the constraints are met, and the more optimal the provided solution is.

We distinguish between hard constraints, medium constraints, and soft constraints and compute scores for each.

Hard constraints

Hard constraints are the basic rules of the domain and must never be broken. If they are broken, the solution isn’t even feasible.

Medium constraints

Medium constraints usually incentivise Timefold to assign as many entities as possible. They are used by Timefold to allow for overconstrained planning.

Soft constraints

The soft constraints of a model represent the optimization objectives. They can be broken, but the more they are satisfied, the more optimal a solution is.

Timefold optimizes for a higher hard constraint score first (to find a feasible solution), then a higher medium constraint score (to assign as much as possible), and then a higher soft constraint score (to optimize the solution). The scores are the sums of each of the constraint scores, grouped by type.

The graph below that shows the evolution of the scores for hard, medium, and soft constraints during the model run. You can click the expand button on the right of the chart to see each score on their own graph with Y-axis values.

Score Graph
Figure 1. Score Graph

When you hover over the score graph, you’ll see the values for each of the scores and the metrics of the solution at that time and the difference to the first solution. By exploring the evolution of scores and metrics, you’ll get a glimpse into the dynamic of the model - how it balances all of the different constraints and what the effect on the metrics is.

Score analysis

Below the score graph is a list of all constraints defined by the model. The constraints that aren’t fully satisfied are presented first, ordered by type and then score.

By default, constraints that are fully met are hidden. Click Show satisfied constraints to reveal all constraints.

For each constraint we show:

  • Its name.

  • Its type: hard, medium or soft.

  • The impact: whether it’s a penalty or a reward.

  • The matches: How often this constraint wasn’t fully met.

  • The weight: How much weight this constraint was given. See Configuration and optimization goals to tweak this.

  • The associated score.

The constraints are grouped logically, so it’s easier to understand which constraints are related.

Employee Scheduling Constraint List
Figure 2. Employee Scheduling Constraint List

The image shows constraints that aren’t fully satisfied for a run of the employee scheduling model.

Planning solution output and visualization

A visual representation of this plan can be found on the Visualization page.

The full details of the solution can be found under Output. You can also download the output as a JSON file with the full details of the plan.

Using the API

The information from this overview page is also available by using the Model’s API.

  • The /{id} endpoint returns the best solution, including its metrics.

  • The /{id}/run endpoint returns the status of a run and any validation errors or warnings.

  • The /{id}/score-analysis endpoint returns a list of the constraints, their scores, matches, and justifications.

For more information about the API endpoints, go to a model’s API Spec page.

What’s next? Tweaking the planning solution

Now that Timefold has provided you with an optimized plan for your planning problem, there are several ways you can further tailor the solution to your business needs.

Changing the optimization goal

When there is a feasible solution (meaning all hard constraints are met), Timefold further optimizes for soft constraints. By default each of these constraints are given the same importance, but you can change the optimization goals.

Use configuration profiles to change the optimization goals for your run.

Compare to other runs

The Model Runs Overview page, shows a table with the latest runs of a model. By default we show the scores of each of the runs, as well as the first metrics. Use the search functionality to compare specific runs.

Click Manage columns to customize which columns are shown on the overview page. You can pick which of the model metrics to compare.

Plan around fixed segments

Timefold models allow you to pin certain segments, so you can fully customize a plan. Maybe there is an exception where you want to make sure a certain shift is done by a specific employee, or a certain visit is done by a specific vehicle. If you provide Timefold with pinned segments, it will honour those while planning around them.

Solve for longer, or shorter …​

When a run ends is determined either by a time limit, or when the score of a run no longer changes. The score graph of a run gives an indication whether it is worth solving for longer. Whether the extra time spent solving is worth it depends on your business needs.