Workforce Scheduling
Create optimal shift plans matching staffing to demand, respecting labor laws, skill requirements, employee preferences, and budget constraints.
NP-hardComplexity
Why Workforce Scheduling Is Hard
Labor law complexity
Working time regulations, mandatory rest periods, maximum consecutive days, overtime rules. These create a dense web of constraints that vary by jurisdiction.
Demand variability
Staffing needs change by hour. A retail store needs 3 people at 10 AM and 12 people at 2 PM on Saturday. Matching staff to these curves is hard.
Employee heterogeneity
Skills, certifications, preferences, part-time contracts, availability windows. Each employee is different, making assignment combinatorially complex.
Real-World Example
What This Looks Like in Practice
A retail chain schedules 300 employees across 45 locations for peak season. Predicted foot traffic varies from 50 to 500 visitors per hour per location. 40% of staff are part-time with varying availability. Each location needs at least one certified first-aid responder per shift. Labor law requires 11 hours between shifts. The scheduling team currently spends 3 days per week building the schedule. The result covers 85% of demand peaks. kint builds a schedule that covers 98% of peaks, costs 20% less, and is ready in minutes.
Approach
How kint Solves Workforce Scheduling Problems
Forecast demand per location
ML predicts foot traffic, call volume, or workload by hour and location from historical patterns.
Model shifts and constraints
Employee availability, skills, labor laws, budget limits, fairness rules. All become constraints in the optimization model.
Solve with MIP + CP hybrid
Integer programming assigns shifts. Constraint programming handles complex labor law rules. The hybrid approach handles both efficiently.
Publish and adjust
Employees see their schedules. Swap requests trigger re-optimization. Coverage stays optimal even as plans change.
Technical Details
Workforce scheduling combines integer programming for shift assignment with constraint programming for complex labor law compliance. kint handles multi-skill, multi-location scheduling with demand forecasting integration.
Impact
What Changes
Scheduling time
3 days/week
Minutes
Demand coverage
85% of peak hours
98%
Labor cost
Baseline
-20%
Position fill rate
91%
98%
Shift fairness
Inconsistent patterns
Balanced distribution
Example
Input
Schedule 300 staff across 45 locations for peak season, matching predicted foot traffic with skill requirements and labor law constraints.
Output
Optimal shift plans with 20% lower labor cost, 15% better coverage, and 98% position fill rate.
-20%
labor cost
+15%
coverage
98%
fill rate
Connections
Related Problem Types
Have a Workforce Scheduling Problem?
Show us your data. We'll show you what optimal looks like. No commitment.