Robust Analytics & System Simulation (RASS) Lab
At Robust Analytics & Systems Simulation (RASS) lab, we bring together advanced artificial intelligence (AI) models and operations management decisions. Our interdisciplinary team focus on applying machine learning algorithms, quantitative mathematical modeling, and system simulation techniques to improve business decisions in various industries including supply chain management, logistics, and healthcare.
Research Streams:
Data Analytics & Revenue Optimization
Healthcare Analytics & Simulation
Smart Supply Chain Management
Publications & Working Papers:
Data Analytics & Revenue Optimization
Sison, N., Li, L., Han, M. (2021) A Configurable Data Preprocessing Framework to Improve Existing Travel Industry Time Series Prediction Models. (Working paper)
Sison, N., Li, L., Han, M. (2021) Survey of Machine Learning and Deep Learning Techniques for Travel Demand Forecast. (Accepted) Proceeding of the 5th IEEE International Conference on Smart City Innovations.
Li, L., & Zhou, Y. (2019). Mitigating Customers’ Downsell Risk for Single-leg Revenue Management with Demand Dependencies. 2019 Winter Simulation Conference (WSC), IEEE
Gallego, G., Li, L., & Ratliff, R. (2009). Choice-Based EMSR Methods for Single-leg Revenue Management with Demand Dependencies, Journal of Revenue and Pricing Management, 8(2), 207-240
Healthcare System Modeling and Simulation
Zhou, Y., Nikolaev, A., Bian, L., Lin, L., & Li, L. (2021) Investigating Transmission Dynamics of Influenza in a Public Indoor Venue: An agent-based modeling approach. Computers & Industrial Engineering, 157, 107327
Zhou, Y., Li, L. & Ghasemi, Y. (2021) An Agent-Based Model for Simulating COVID-19 Transmissions on University Campus and Its Implications on Mitigation Interventions: A Case Study. Information Discovery and Delivery, special issue on Using Data Science to Understand Coronavirus Pandemic.
Vyas, J. D., Han, M., Li, L., Pouriyeh, S., & He, J. S. (2020). Integrating Blockchain Technology into Healthcare. Proceedings of the 2020 ACM Southeast Conference.
Smart Supply Chain Management
Li, L., Katircioglu, K., & Sourijan, K. (2010). Empirical methods for two-echelon inventory management with service level constraints based on simulation-regression, Winter Simulation Conference 2010
Engineering Education
Others
Wan, M., Han, M., Li, L., Li, Z., & He, S. (2020). Effects of and Defenses Against Adversarial Attacks on a Traffic Light Classification CNN. Proceedings of the 2020 ACM Southeast Conference.
Rudd, J. M., Henshaw, A. M., Staples, L., Akkineni, S., Li, L., & DeMaio, J. (2020). Genetic Algorithm Guidance of a Constraint Programming Solver for the Multiple Traveling Salesman Problem. KSU digital commons