Setting Up: Rule Basics

Rules are the heart of the automated rostering process. They tell the AI exactly what constraints must be met and what preferences should be prioritized. Understanding the basic components of a rule is essential for effective setup.

Hard vs. Soft Constraints

Rules fall into two main categories:

  • Hard Constraints: These are rules that must be satisfied under all circumstances. The solver will not produce a roster that violates any hard constraints. If it's impossible to satisfy all hard constraints simultaneously (e.g., due to conflicting rules or insufficient staff), the solver will report an error indicating infeasibility.
    Examples: Staffing levels (must have 1 person per shift), minimum days off between shifts, cannot work during approved leave.
  • Soft Constraints (Paid Feature): These are preferences or goals that the solver will try to satisfy as much as possible, but they can be violated if necessary to meet hard constraints or other higher-priority soft constraints. They are used for optimization and improving roster quality.
    Examples: Fairness in shift distribution, honoring specific shift requests, spacing shifts evenly.
Rule Scope: Applying Rules Selectively

Most rules allow you to define their scope – which staff members, shifts, and dates they apply to. This provides fine-grained control over your roster.

Persons

Apply the rule to "All" staff, or select specific individuals or groups (if you've defined groups like 'Seniority' in Staff Details).

Dates

Apply the rule to "All" dates in the roster period, only "Weekdays", only "Weekends", "Public Holidays", "Weekends + Public Holidays", or select specific dates/ranges.

Shift Types

Apply the rule to "All" shift types, "Any Shift", or select specific shift types (e.g., only 'Night' shifts).

Prioritizing Soft Constraints: Weighting (Paid Feature)

When using Soft Constraints (available on paid tiers), you can influence their priority using the Weighting parameter.

  • A higher weighting value gives a rule higher priority. The solver will try harder to satisfy rules with greater weights.
  • Weighting allows you to express the relative importance of different preferences. For example, you might give fairness rules a higher weight than shift distribution rules if fairness is more critical for your team.
  • The default weight is typically 1. You can increase this value (e.g., 2, 5, 10) to make a soft constraint more important.