A Discrete Event Simulation (DES) is a method of modeling dynamic systems where events occur at distinct points in time. Unlike continuous simulation, which models processes that change continuously over time, a DES models discrete events that influence a system's behavior.

I have conducted dozens of DES projects involving complex material handling designs. This particular project focuses on optimizing the efficiency of a sawtooth merge within a larger conveyor system. Sawtooth merges are used in warehousing to handle the convergence of multiple input streams onto a single output conveyor in a staggered or "sawtooth" pattern.

Key Objectives:
System Design — determine material flow requirements and evaluate appropriate equipment and control logic based off the constraints of the physical layout and the client’s budget.

System Modeling — develop a detailed model that reflects the real-world dynamics of the sawtooth merge system, including flow rates, input variability, conveyor velocity and acceleration, and control logic.

Performance Metrics Definition – identify and define key performance metrics for the sawtooth merge, such as throughput, cycle time, queue lengths, and resource utilization. These metrics serve as benchmarks to evaluate the system's efficiency.

Optimization (Tuning) — adjust parameters like the lane release sequence, conveyor speeds, slug lengths, release timing, and implement buffer management techniques to improve the throughput and reduce chance of upstream gridlock.

Scenario Testing — conduct scenario testing by varying input assumptions such as mean arrival rates and variability. Assess the impact of different scenarios on key performance metrics.

Report and Recommendations — generate a comprehensive report summarizing the simulation results and providing data-driven recommendations for creating the control logic and tuning the merge.

Results:
Before any equipment was purchased, the simulation provided recommendations that improved upon the initial design of the sawtooth by increasing the merge’s maximum throughput and reducing the risk of gridlock. It also showed how adjusting parameters such as lane release sequence, conveyor speeds, slug lengths, and release timing, would achieve success over a broader range of operating conditions. These were turned into actionable insights for the team of controls engineers and were easily presented using the model’s animations. Overall, these results highlight the effectiveness of discrete event simulation in optimizing complex conveyor systems and testing these scenarios in a risk-free virtual environment. A standard template was later developed and used in all future sawtooth merge applications, as a requirement during design phase validation.