AI-Powered Automatic Shift Scheduling System for Contact Centers

Contact center operators work in three shifts, and the load fluctuates every 15 minutes. Manually creating a weekly schedule is a pain: it takes 8–16 hours of hourly tweaking, and there are still overtime or underload issues. We, a team of AI engineers with 10+ years of experience in MLOps and schedule orchestration, have developed over 50 systems for contact centers. Our AI-based schedulers solve the task in 5–15 minutes, taking into account SLA, labor law, skills, preferences, and budget. In one project for a network of 400 operators, overtime decreased by 28% and preference satisfaction reached 72%. Order a preliminary analysis of your project — we will evaluate it in 2 days.

How does the AI system collect data?

The scheduler uses load forecasts in 15–30 minute intervals, HR system data (contracts, vacations, skills, preferences), and labor law and SLA constraints. All data is imported from WFM systems.

Optimization methods

We use a combination of Integer Linear Programming (ILP) with solvers CBC and Gurobi for fixed horizons, Constraint Programming (CP-SAT) for complex hard constraints, and Reinforcement Learning for adaptive adjustments. CP-SAT from OR-Tools outperforms ILP on problems with logical conditions — for example, "if night shift, then next day shift after 24 hours" — without linearization. In our projects, CP-SAT reduces schedule cost by 5–10% under the same constraints.

Why CP-SAT outperforms ILP?

Constraint Programming SAT allows expressing logical conditions without linearization, simplifying the model and speeding up computation. Additionally, CP-SAT is built into OR-Tools, providing a convenient API for combinatorial problems. For fairness, we use the Gini coefficient for undesirable shift distribution — CP-SAT supports such constraints natively.

How does the system handle operator preferences?

A technical solution for fair scheduling includes:

• Uniform distribution: each operator gets ±5% of night, weekend, and holiday shifts
• Preference satisfaction: target over 70%
• Bidding system: operators bid on shifts, the scheduler maximizes total satisfaction while maintaining SLA

Preference satisfaction vs. cost minimization is a multi-criteria problem. The dashboard displays a Pareto front with different trade-offs. The bidding system is implemented using a weighted maximum matching algorithm, guaranteeing Pareto optimality.

Scheduling process

Horizon: 1–4 weeks. Pipeline:

1. Import load forecast from WFM
2. Load HR data
3. Generate a set of possible shifts
4. CP-SAT optimization
5. Post-processing: fairness check
6. Publish schedule

Intra-day adjustments: When an operator is unexpectedly absent, an RL agent trained on historical replacement data finds a replacement in seconds. This allows reacting to sick leaves, lateness, and other emergencies.

Mobile app for operators

Operators can view schedules, request shift swaps, and time off through the app. Supervisors spend 60–70% less time on organizational issues. The system sends push notifications about upcoming shifts and changes.

Metrics

• Build time: < 10 min for 200 operators
• SLA coverage: > 98% of intervals within ±10% of target
• Overtime reduction: 15–25%
• Preference satisfaction: > 65%
• Stability: < 15% changes within 48 hours
• Fairness: Gini coefficient for undesirable shifts < 0.2

What is included in the work?

• Analysis of current scheduling and requirements gathering
• Development of mathematical model (ILP/CP-SAT/RL)
• Integration with WFM, HR, and mobile apps
• Testing on historical data and A/B pilot
• Documentation, training, 1-month warranty support

Get a detailed savings estimate for your contact center. Contact us for a consultation — we will evaluate your project in 2 days. Find out how an AI scheduler can reduce your operational costs.

Timelines: basic scheduler with ILP/CP-SAT — 6–8 weeks. Full system with mobile app and intra-day adjustments — 4–5 months.