Exploring the intersection of Industrial Engineering and Computer Technology in designing the warehouses of the future.
📖 Related Reading:
🔗 Warehouse Automation Article (NetSuite)
🔗 Past Publication (ResearchGate)
Exploring the intersection of Industrial Engineering and Computer Technology in designing the warehouses of the future.
This repository explores the theoretical framework, methodologies, and computational models behind the design of automated warehouse layouts — a critical challenge where industrial engineering principles meet intelligent automation systems.
As warehouses evolve into autonomous, data-driven environments, layout design must adapt to the constraints and opportunities offered by AGVs, AS/RS, AI, and IoT. This project aims to formalize those design considerations using proven theories and emerging tools.
- 📐 Formalize the automated layout design problem (ALDP)
- 🤖 Explore how automation technologies shape spatial design
- 🧮 Bridge classical layout theory with modern computational tools
- 🧠 Develop groundwork for AI-based layout generation and simulation
- 🔄 Enable future integration with digital twin platforms
A well-known NP-hard problem concerned with placing functional areas in a facility to:
- Minimize material handling cost
- Optimize flow distances and adjacency preferences
- Subject to spatial and operational constraints
Common Approaches:
- 🔢 Quadratic Assignment Problem (QAP)
- 📊 Mixed-Integer Linear Programming (MILP)
- 🧬 Genetic Algorithms & Metaheuristics
A qualitative, heuristic framework originally developed by Richard Muther. Still widely used in early design stages for:
- Identifying activity relationships
- Defining space requirements
- Mapping material flows
- Evaluating layout alternatives
Though not computational, it provides a solid human-centered starting point.
As automation is introduced, layout constraints and design priorities shift:
| Automation Element | Layout Impact |
|---|---|
| AGVs/AMRs | Require wide aisles, dynamic routing |
| AS/RS | Introduce vertical design, zoning |
| Robotic Picking | Demands ergonomic item placement |
| IoT & WMS/WCS | Enables real-time, adaptive layout logic |
| Digital Twin | Requires 1:1 simulation-ready spatial model |
Automation systems transform space into programmable infrastructure.
| Topic | Focus Area |
|---|---|
| 🗺️ Layout Modeling | Spatial and logical modeling of warehouse zones |
| 🔁 Flow Optimization | Dynamic path planning, congestion reduction |
| ⚙️ Automation Integration | Aligning robotic requirements with design geometry |
| 🧠 AI-Driven Layouts | Using ML or GAs to evolve high-performance layouts |
| 🌐 Digital Twins | Real-time, closed-loop simulation environments |
- Simulation & Optimization:
Simio,AnyLogic,OR-Tools,SimPy - Modeling & Design:
AutoCAD API,Revit,SketchUp,Unity3D - AI/ML:
scikit-learn,TensorFlow,PyTorch,DEAP(for GAs) - Robotics & Control:
ROS,Webots,Gazebo - Digital Twin Platforms:
AWS IoT TwinMaker,Siemens NX,Unity Reflect
- 🧾 Formal definition of ALDP (Automated Layout Design Problem)
- 🧮 Solver for basic FLP with automation constraints
- 🧪 Modular simulation engine for layout validation
- 🤖 AI-driven layout generation using Genetic Algorithms
- 🧵 Digital twin prototype with live flow simulation
- 📊 Comparative study: Traditional vs Automation-First layouts
RAUL EDUARDO FERNANDEZ PACHAS
🎓 Master of Computer Technology
🎓 B.Eng (Hons) in Industrial Engineering
Researcher in automation systems and intelligent facility design.
"Where spatial engineering meets intelligent automation."
📫 Feel free to connect on LinkedIn or contribute ideas!
This project is licensed under the MIT License. See the LICENSE file for details.
Pull requests, academic insights, and simulation collaborations are welcome. Please fork the repo and open an issue to get started.
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