How Auto-Assignment works

Auto-Assignment uses a linear programming model and optimization logic to solve your scheduling problem based on your defined Auto-Assignment rules, coverage, and preferences. The optimization model behind Auto-Assignment is built to be flexible and to work with many different rule configurations and preferences that allow users to generate a schedule that is suitable for their particular scenario and business needs. Auto-Assignment uses mathematical formulas to look at the whole problem and solve the scheduling problem while minimizing the penalties that are incurred when Auto-Assignment chooses to break the rules, coverage and preferences that are configured by the user.

The optimization model aims to minimize the penalties incurred during schedule generation and uses a tiered approach when choosing to incur a penalty to solve a scheduling problem. Auto-Assignment rules that are considered constraints incur the largest penalty, followed by coverage, and then preferences. Because preferences incur the smallest penalty, they will be broken more often in order to satisfy coverage or a constraint.

A constraint is something the system will try not to break, but will do it on occasion if the solution to the scheduling problem calls for it. As an example, you may configure a rule where employees want a minimum of 40 hours per week, but there are only enough shifts to give everyone 30 hours per week. In this situation the system will break the constraint since the only other solution is to not schedule anyone.

Rules can also be set as hard constraints. The system will not break a rule set as a hard constraint. If it cannot find a solution that does not break a hard constraint, no shifts will be assigned. Hard constraints are disabled by default, as they can make it impossible for Auto-Assignment to assign shifts unless they are carefully considered and configured.

See Hard constraints.

Although the model is very methodical and always tries to aim for the minimum penalty, it will usually produce a schedule with a shift fill-rate of 85-98%, in the case of MVS, or a schedule that is slightly above or below 100% staff coverage, in the case of LFSO. In many cases the system is very close to what it considers a perfect solution for the scheduling problem, but will choose to stop and generate the schedule in a reasonable time frame rather than spending three days looking for improvements that may or may not exist. Each of the 20+ Auto-Assignment rules have different configuration points and the optimization model uses a common process that solves the scheduling problem no matter which rules are chosen. These compromises ensure the system is flexible and robust and will suit the needs of many different users and scenarios.

Due to the aforementioned reasons, it should be noted that in some cases after generating the schedule with Auto-Assignment, the user will need to manually intervene to complete the schedule to suit their business needs before publishing the schedule.

For information on best practices for Auto-Assignment and improving the quality of your generated schedule, see Best practices for Auto-Assignment implementation.