Discrete Event Simulation as a Predictor for Factory Traffic Management
Name
ramirezechavarria-estebanr-mba-mgt-2025-thesis.pdf
Description
Thesis PDF
Size
2.02 MB
Format
Adobe PDF
Checksum (MD5)
ac3c32b589c4d1c54d40326b40377cb2
Author(s)
Ramirez Echavarria, Esteban
Advisor(s)
Daniel, Luca
Spear, Steven
Date Issued
May 2025
Publisher
Massachusetts Institute of Technology
Abstract
Manufacturing environments increasingly rely on automation and data-driven decision-making to optimize efficiency and production rates. This study explores the application of Discrete Event Simulation (DES) to model material flow and predict AGV (Automated Guided Vehicle), crane, and cart movements within a factory. The goal is to develop a digital twin that enables real-time decision-making, optimizes scheduling, and minimizes bottlenecks.
To achieve this, we utilize SimPy, an open-source Python-based DES library, in conjunction with a custom-built API and React.js front-end interface. The study evaluates available DES software options and justifies the selection of SimPy based on flexibility, integration capabilities, and its suitability for modeling custom business rules. The solution is structured into modular components handling path planning, transporters, flows, stations, hot-cold starts, and utilities, ensuring adaptability to future improvements.
A validation framework was established, utilizing historical data comparison and real-time validation to assess the simulation’s predictive accuracy. Over a 40-day testing period, the simulation achieved 89.6% accuracy and a sensitivity, or true positive rate (TPR), of 80.2%. The simulation provides a reliable first-pass scheduling tool that can be further refined with improved data collection.
The findings indicate that while full automation of AGV deployment is not yet feasible, this study lays the foundation for future integration with the factory’s Vehicle Management System (VMS). Business implications include the potential for automated scheduling, enhanced material flow visibility, and optimization of capacity planning. Future work should focus on improving data accuracy, integrating live factory data streams, and refining algorithms for predictive scheduling.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Sloan School of Management
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