Research Area

The research areas of SOLab are not limited to specific fields; instead, it conducts various applied research utilizing management science, optimization, and data science.

1. Optimization of Last-Mile Transportation Systems

The themes are

- Last-mile delivery considering backhaul

- Vehicle routing problem with heterogeneous delivery vehicles

- Delivery station using heterogeneous delivery vehicles

- Cold chain last-mile delivery

- Underground delivery

- Location problem for UAV vertipad

- Mobile delivery station

- Battery swapping stations for UAVs

2. Optimization of Healthcare System Operations 

The themes are

- Prediction and utilization of emergency room stay duration based on machine learning (MIMIC-IV data)

- Prediction of ICU readmissions and classification of critical care patients based on machine learning (MIMIC-IV data)

- Location problem for UAV vertipad for cardiac arrest patients

3. Supply Chain Optimization 

The theme is

- Logistics optimization strategies for mature products

4. Efficiency in Business Decision-Making

The themes are

- Machine learning-based corporate environmental controversy prediction model

- Production design efficiency model

- Prediction of companies' export potential using machine learning

- Machine learning-based aircraft climb rate modeling for fuel efficiency improvement

- Improving the accuracy of castings classification using generative adversarial networks (GAN)

- Development of an alternative viewership measurement model

5. Systematization of Investment Decisions

The themes are

- US IPO Underpricing prediction model using natural language processing (NLP)

- Machine learning-based decision support for venture capital investments 

- Portfolio optimization 

6. Development of Optimization and Heuristic Methodologies

The themes are

- Quantum-inspired genetic algorithm

- Effective problem-specific heuristic design

- Efficient combinatorial optimization modeling based on analytics

- Decomposition techniques

- Sample average approximation for stochastic optimization