PIPOWarehouse – Intelligent Warehouse Optimization Engine
End-to-end design of a research-grade warehouse simulation and optimization platform targeting high-performance 3PL operations. This project demonstrates the ability to architect scalable systems that bridge robotics, AI, and logistics.
Project Overview
PIPOWarehouse is a modular simulation framework designed to model and optimize real-world warehouse operations. It enables experimentation with advanced decision-making strategies, including reinforcement learning and combinatorial optimization.
Key Contributions
- Architected a modular simulation environment for complex warehouse dynamics
- Enabled benchmarking of RL agents and optimization algorithms (e.g., OR-Tools)
- Modeled real-world constraints: storage, stacking, routing, and multi-agent coordination
- Designed as a foundation for AI-driven decision layers in next-generation WMS
Technical Stack
Python, Reinforcement Learning, Operations Research, Simulation Systems