Guidelines for Creating a Workforce Scheduling Policy

Start With Work Rules

Work rules narrow the pool of eligible resources. Fewer candidates means faster scheduling and better focus on the resources that can do the work.

• Add the Service Resource Availability work rule. Every policy needs it to respect operating hours, breaks, existing appointments, and absences.
• Add the Match Time work rule with entries for Earliest Start Permitted and Due Date. Both entries are required in every policy.
• Add Match Skills and Match Working Territories rules to reflect your workforce structure and skill requirements.

Choose Service Objectives That Reflect Your Priorities

Service objectives grade the remaining candidates. Pick the objectives that map to how your business measures a good schedule.

• Common combinations include Minimize Travel plus ASAP for productivity and cost reduction, Preferred Resource plus Minimize Travel for customer-first scheduling, and Resource Priority plus Skill Level for tiered workforces. See the Default Scheduling Policies in Workforce Scheduling.
• Fewer objectives are usually better. Each objective you add competes with the others for influence over a candidate’s grade — more objectives create more competition and more trade-offs, which dilutes each objective’s effect. Two to four objectives give the engine clear direction.
• If you don’t need graded scheduling, leave service objectives off the policy. Workforce Scheduling then returns the first eligible candidate without grading all options, which speeds up scheduling when you’re using the flow (Schedule New Appointment or Select Time Slot).

Weight Objectives to Reflect Relative Importance

Weights tell the scheduling engine how much each objective matters relative to the others in the same policy — but they aren’t a simple “higher weight means more important” dial. Each objective measures something different, so the scheduling engine applies a penalty formula per objective that converts each candidate slot’s raw measurement into a comparable penalty. The weight is then applied to that penalty, so weights can be compared across objectives — a higher weight increases how much that objective’s penalty contributes to the slot’s total grade.

What each objective measures:

• Minimize Travel: travel minutes.
• Minimize Overtime: overtime minutes.
• ASAP: customer wait time.
• Same Site: whether the appointment reuses a location already on the resource’s schedule.
• Preferred Resource: whether the assigned resource matches the one specified on the appointment.

The ratio between weights is what matters, not the absolute number — weights of 100/50/25 behave the same as 4/2/1. A higher weight does increase an objective’s influence, but only after the per-objective penalty formula has run: doubling the weight on Minimize Travel roughly doubles how much travel differences move the grade, relative to other objectives at their current weights.

The default Cost Optimized policy is a good example. Its default weights are Minimize Overtime at 360, Minimize Travel at 300, ASAP at 100, Same Site at 80, and Preferred Resource at 30. Reading the weights alone suggests Minimize Overtime will always dominate scheduling decisions — but it doesn’t, because what actually contributes to a slot’s grade is the weight multiplied by that slot’s penalty on that objective. A slot may add 20 minutes of travel and just 1 minute of overtime, in which case the travel term (300 × the penalty for 20 travel minutes) easily outweighs the overtime term (360 × the penalty for 1 overtime minute) even though overtime has the higher weight. The weights say “per unit of penalty, avoiding overtime matters slightly more than avoiding travel” — they don’t say “overtime always wins.” This is why the same policy can favor different resources for different appointments: the weights are constant, but which objective ends up dominating depends on how much of each is at stake in the slots being compared.

Validate Before Rolling Out

Simulate the policy on production-like data before you make it your org’s default.

1. Copy the policy to a sandbox with a recent data refresh.
2. Choose two or three service territories that represent different parts of your business.
3. Unschedule the test appointments and run scheduling or global optimization with the new policy.
4. Compare the results (runtime, appointments scheduled, utilization, travel time) against a baseline run of your current policy.
5. Roll the tuned policy out to one territory in production before you apply it broadly.

What to Avoid

• Building a policy from scratch when a default policy is close to your needs. Copy the closest default and adjust.
• Editing the default policies directly. Make copies so you keep an unmodified reference version.
• Skipping simulation. Small weight changes often have larger schedule effects than you expect.