Here are some topics for your final project in Industrial Engineering, focusing on diverse areas that showcase innovation, problem-solving, and real-world applications:
Operations Research & Optimization
- AI-Driven Production Scheduling: Use machine learning to optimize production schedules in manufacturing systems to minimize costs and lead times.
- Multi-Criteria Decision Making for Supplier Selection: Develop a decision-support system for selecting suppliers using advanced optimization methods like AHP, TOPSIS, or AI-driven models.
- Dynamic Facility Layout Design: Explore how AI can improve dynamic layouts in manufacturing to adapt to changes in demand and production processes.
Supply Chain & Logistics
- Blockchain Integration in Supply Chains: Study how blockchain can improve transparency, traceability, and efficiency in global supply chains.
- Last-Mile Delivery Optimization: Investigate the role of drones or autonomous vehicles in enhancing the efficiency of last-mile delivery.
- Demand Forecasting with AI: Use machine learning to predict demand patterns for inventory management and supply chain planning.
Quality Management & Reliability
- AI in Predictive Maintenance: Develop a predictive maintenance model using IoT and machine learning to reduce downtime in industrial equipment.
- Quality Control Automation: Implement computer vision for defect detection in production lines to improve quality assurance processes.
- Risk Assessment in Manufacturing Systems: Design a risk management framework using probabilistic modeling and simulation.
Human Factors & Ergonomics
- VR-Based Ergonomic Analysis: Investigate how virtual reality can be used to optimize workplace ergonomics and reduce workplace injuries.
- Human-Robot Collaboration: Study the design and safety considerations of collaborative robots (cobots) in industrial environments.
- Workforce Scheduling Optimization: Use AI to create fair and efficient workforce schedules that consider ergonomic and psychological factors.
Sustainable Industrial Systems
- Green Supply Chain Design: Propose strategies to minimize environmental impact using lifecycle assessment and reverse logistics.
- Energy Optimization in Manufacturing: Develop models to optimize energy consumption in production processes using AI and IoT.
- Circular Economy in Manufacturing: Design systems for reusing, recycling, or refurbishing industrial materials and products.
Data Analytics & AI Applications
- Digital Twin for Smart Factories: Create a digital twin of a manufacturing process to simulate and optimize operations.
- Big Data in Industrial Process Control: Analyze large datasets to improve process efficiency and reduce variability.
- AI for Process Improvement: Apply neural networks or other AI techniques to enhance lean manufacturing practices.
These topics are general enough to be adapted to specific interests while demonstrating the application of industrial engineering principles. Let me know if you’d like detailed guidance on any of these!
