Schedules built on patterns your staff can predict
Each employee follows a shift pattern (like 2-2-3) you define — so they know their rhythm months ahead. The solver finds the best collective coverage for your demand within those patterns.
50 employees · proven-optimal in seconds
Detailed system description
Try it live — no file needed
Edit the staffing demand below — how many people you need each hour — then click Calculate. The CP-SAT solver assigns an optimal schedule and shows how well it covers your demand.
This runs on the server, like the file upload below.
Achieved coverage vs demand
Why patterns matter
Predictable for staff
Most tools optimize coverage but hand each worker a scattered, unpredictable roster. Here every employee follows a defined pattern (e.g. 2-2-3) — they can predict how they'll work two months from now.
Why it needs a solver
Keeping every employee inside a fixed pattern makes the collective schedule harder to fit to demand — much harder than placing shifts freely. That's exactly what the solver is for: it finds the best assignment of patterns and offsets so the combined coverage matches your demand.
Compliance by construction
Because you define the patterns yourself, you encode union rules (rest periods, max hours, rotation) at the pattern stage — so the resulting schedule is compliant by construction, with no paid vendor change request.
Typical Scheduling Challenge
- 50 employees × 20 shifts per month = 1,000 manual Excel entries
- After completion: schedule doesn't match production demand
- Start over, repeat many times
- Result: Scattered schedules, dissatisfied employees
How the System Works
Instead of manually copying shifts from period to period — slow across many employees over a full year, and often a poor match for demand — you enter each employee's pattern, the scheduling period, and any unavailability (e.g. vacation). In seconds you get both a per-employee schedule and the combined team schedule.
- Define shift patterns in Excel (e.g. 7 days work, 3 days off)
- CP-SAT solver proves the best assignment (patterns + shifts) in seconds
- Each combination is compared against production requirements
- Cost calculated (overtime, night shifts, holidays)
Built for the first stage: long-horizon planning
Use it to build a solid, pattern-based base schedule for the whole period (e.g. a year) — then feed that into whatever system you already use for day-to-day changes like sick-leave cover. It complements your daily-ops tools rather than replacing them.
Processing Time
Scenario 1: Patterns Already Assigned
When you've already chosen which pattern each employee gets:
~1 second
System just generates schedule for 50 employees
Scenario 2: Combination Analysis
When system needs to find the best combination:
seconds
Prove the optimal schedule (CP-SAT)
Try Live Demo
Upload Excel file with shift patterns and employee list. System analyzes the best combination.
Get Started
Download sample with shift patterns, employee list, and production demand.
Sample includes:
- • Shift patterns (7 days work + 3 off, etc.)
- • List of 50 employees with vacations
- • Production demand (how many per hour)
- • Instructions on how to modify
Shift Pattern Example
Excel Setup
| work_days | break_days | start_hour | end_hour |
|---|---|---|---|
| 7 | 3 | 6 | 14 |
| 6 | 4 | 14 | 22 |
| 5 | 2 | 22 | 6 |
What This Means
Pattern 1: Work 7 days (6AM-2PM), then 3 days off
Pattern 2: Work 6 days (2PM-10PM), then 4 days off
Pattern 3: Work 5 days (10PM-6AM), then 2 days off
System tries all combinations: which employee gets which pattern, and with how much shift offset.
Key Features
Unlimited Patterns
Create as many shift patterns as needed in Excel
Vacation Handling
System automatically considers employee vacations
Demand Comparison
Each combination compared against production requirements
Cost Calculation
Total calculation incl. overtime, night shifts, holidays
Excel
Input and output data in familiar Excel format
Python
Powerful analysis algorithms for consistent performance
Want to See the Complete Solution?
Detailed description of algorithm, technical architecture, and usage examples