How we use network science to automatically build the best possible staff schedule - minimising cost while respecting everyone's availability and skills.
Not a bar chart or pie chart - a network of connected things.
In our world, the "cities" are people, days, jobs, and shifts. The "roads" represent relationships like "Alice is available on Monday" or "Bob is qualified for Bakery".
This simple structure lets a computer understand the rules of scheduling and find the best solution automatically.
| Node (dot) | A thing - a person, a day, a shift |
| Edge (line) | A connection - "is available", "is qualified" |
| Graph | The whole picture of nodes + edges together |
We turn synthetic workforce data into a graph the computer can reason about.
Each connection gets a "cost" number that tells the solver how good or bad that assignment would be.
| Factor | Effect | Why |
|---|---|---|
| Higher Skill | ↓ Lower cost | Better-qualified people are rewarded |
| Lower Hourly Rate | ↓ Lower cost | Cheaper labour is preferred (same quality) |
| Preferred Day | ↓ Lower cost | Respects employee preferences where possible |
| Understaffing | ↑↑↑ Huge cost | Leaving a shift empty is always the worst option |
A "minimum-cost flow" algorithm picks the combination of assignments with the lowest total cost.
If no real employee can fill a slot, a "phantom" fills it at enormous cost. This flags genuine staffing gaps without crashing the system.
Every assignment is explainable. We can trace exactly why each person was chosen.
Solves in milliseconds, even for large stores. No manual trial-and-error.
Every decision has a traceable reason - no "black box" mystery.
Never violates working hour limits, availability, or qualification rules.
A robust, scalable, and transparent approach to workforce scheduling that reduces cost, respects constraints, and explains every decision.
Try the Interactive Dashboard →