Operational Efficiency Case Study: Optimizing Queue Systems via Monte Carlo Simulation and Julia Programming

Project Background & Objective

Although the canteen generally operates smoothly, significant congestion occurs during peak lunch hours (12:00–14:00 WIB) and special events such as graduation ceremonies. The business challenge was to determine whether existing service capacity was sufficient and to evaluate capacity expansion strategies—such as adding cashiers—without disrupting daily operations or increasing unnecessary costs.

Tech Stack & Skills

Methodology

1. Observed real customer flow across 7 service counters during operating hours.
2. Fitted statistical distributions for inter-arrival times and service durations.
3. Built a baseline DES model with 1 cashier per counter.
4. Simulated demand acceleration scenarios (1.5×–4.0× arrival rates).
5. Developed an enhanced model with additional cashier capacity per counter.
6. Compared performance metrics across scenarios.

Key Insights

• Customer arrivals peaked during lunch hours, contributing to over 55% of daily demand.
• The drink counter (Loket 7) handled ≈31% of total transactions, making it the primary bottleneck.
• Under increased arrival scenarios, the baseline model showed rapid growth in average queue length and waiting time.
• Introducing 1 additional cashier per counter significantly reduced average waiting times and stabilized queue lengths across all demand scenarios.
• Server utilization became more balanced, reducing overload risk during peak and special-event conditions.

Business Recommendations

• Deploy additional cashiers dynamically during peak lunch hours and major campus events.
• Prioritize capacity expansion on high-demand counters (e.g., beverages).
• Use DES as a continuous decision-support tool for staffing and layout planning.
• Combine operational changes with menu simplification to further reduce service time.