Demand shift generation, infeasibility handling, and more improvements

Today, we're announcing version v1.3.0 of the Timefold Platform and updates to the Timefold models.

This new version of the Timefold Platform comes with several smaller bug, stability, reliability and security fixes.

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

Employee Shift Scheduling (v1 | Stable)

Demand-curve based shift generation (preview)
: The model can now generate shifts from demand curves and shift templates. When shift generation is enabled, the shifts are automatically created as a part of the scheduling workflow. The feature can operate with different shift durations and start times to provide flexibility for scenarios like overtime. See Shift generation for more details.

Consecutive shifts worked
: Contracts now support
consecutiveShiftsWorkedRules
to limit how many shifts employees work in a row. This feature is useful for managing employee fatigue and ensuring compliance with labor regulations regarding consecutive work periods, especially for demanding shifts like night shifts. See Consecutive shifts worked for more details.
Tag and tag type order
: The order in which tags and tag types are defined in the input no longer affects the result. This makes it easier to upload datasets without needing to sort your input. See Changelog for more details.

Field Service Routing (v1 | Stable)

Improved solution quality and validation for visit dependencies
: The model now produces better results from pre-assigned solutions. See Changelog for how we did this.
New validation warning for visit dependency timing conflicts
: Some timing conflicts made a feasible solution impossible; we've introduced a validation worning for this. See Changelog for details.

Pick-up and Delivery Routing (v1 | Preview)

Real-time planning scenarios
: We have added documentation for different real-time planning scenarios in the Pick-up and Delivery Routing model. Real-time planning allows you to update the plan in response to changes in the environment, such as a no show from a customer, a driver calling in sick or a stop taking longer than expected. This documentation provides guidance on how to use the model for these real-time planning scenarios. See real-time planning for more details. We’ve also added documentation for the from-patch preview API for real-time planning. See real-time planning using patches for more details.

Please let us know if you have feedback.