Optimization gain and metric evolution in Platform and support for limited resources per location and travel configuration per employee in ESS (v0.35.0)

We’re glad to announce version v0.35.0 of the Timefold Platform and models, including a major update to the Run Details page in the Platform UI and exciting new features for Employee Shift Scheduling.

This new version of the Timefold Platform comes with these platform improvements:

Added optimization gain & metric evolution to Run Details page
: We have added the optimization gain (the difference between the first and the last solution) for every metric on the Run Details page. Hover over the score graph to see the evolution of the metrics to understand the tradeoffs the software is making during optimization. See “Interpreting model run results” for more information. These changes are useful to see the real-world impact (in metrics) of the duration of solving and to understand better how different metrics impact each other (e.g. if assigned visits goes up, this also impacts technician costs and to some extent potentially overtime);
Showing constraints per group in Score Analysis
: The Score Analysis overview of a run on the Run Details page now groups all constraints logically. This way you can see related constraints together to understand the model better. Our documentation site explains the features of a model per constraint group, so this way it’ll be easier to connect the features explained in the documentation to how they behave for a specific model run.
Export justifications
: We have added an export button to the Run Details page that exports the justifications for a run to a
.json
file. You can use this to understand which constraints aren’t fully satisfied and why.
Improved maps documentation
: We have improved our documentation that explains the maps service. Updates are specifically useful for customers that self-host the platform.
Expired trials cleanup procedure
: Expired trials will now automatically be cleaned up when inactive. Trial users will receive an advance notification and have the option to keep their data.
Several smaller stability and performance improvements and minor bugfixes (including a fix for a regression when filtering on deleted runs in the Platform UI).

Next to that, this new version of the Timefold Platform comes with updates to these Timefold Models:

Employee Shift Scheduling (v1 | Stable)

Allow limiting concurrent shifts per location
: We have added the ability to control the maximum number of shifts that can take place at the same time. The feature is useful in scenarios where the shifts use a shared resource with limited capacity. See “new and noteworthy” for more details.
Allow travel configuration per employee
: It is now possible to specify a list of travel configurations per contract, and not just globally. Each travel configuration can limit the distance from the home location to a shift’s location and the time between 2 shifts at different locations, within a certain time period. See “new and noteworthy” for more details.
Configurable rule matching behavior
: Several types of rules (Cost Rules, Period Rules and Global Period Rules) can now be configured with more detail on when they are valid. Previously a rule’s validity period only checked a shift’s start time, but it can now be configured to also take into account the shift’s end time. See “new and noteworthy” for more details.
Introducing input metrics
: The output of a model run now includes an object
inputMetrics
that contains the number of employees, the number of shifts and number of pinned shifts in the input dataset. (See OpenAPI Spec for a description of their meaning.)
We have updated our documentation for this model with several new feature guides: how to use “skills & risk factors”, how to guarantee fairness in schedules, and how to use the recommendations by the model to better handle real-time changes.

Field Service Routing (v1 | Stable)

Introducing input metrics
: The output of a model run will now include an object
inputMetrics
that contains the number of visits, the number of visit groups, the number of vehicles, the number of mandatory visits, the number of optional visits, and number of vehicleShifts in the input dataset. (See OpenAPI Spec for a description of their meaning.)
Improved scalability and memory usage efficiency
: We have improved memory usage efficiency, resulting in better scalability over datasets with large numbers of visits.
Added a new set of KPIs
: The following KPIs have been added to the model output and Platform UI: the number of visits assigned, the number of mandatory visits assigned, the number of optional visits assigned, the number of vehicles used in the schedule, and the total amount of overtime required for the schedule. These new metrics will make it easier to compare different runs, e.g. when you are tweaking optimization goals. See our documentation for details.
We have updated our documentation for this model related to recommendations: we have added pinning in recommendations, and a visit groups recommendations guide.

Please let us know if you have feedback.