Integrated Modeling and Optimization for Service Systems (iMOSS) Lab
The Integrated Modeling and Optimization for Service Systems (iMOSS) Lab, led by Dr. Eduardo Perez at Texas State University, develops and applies advanced analytical tools and technologies to enhance planning, information sharing and real-time decision-making in service systems operations.
The research at iMOSS will help Service Systems improve efficiency and operations through better prediction and response to unexpected events.
Our Vision
A future in which all service systems, public and private, would pair with efficiency and effectiveness required by the end customer.
Our Mission
Our mission is to foster interdisciplinary education and research efforts to problems in service systems.
Research Interests
Capacity planning and optimal resources allocation for large-scale service systems.
Featured projects
Impact of natural disasters on health
Disasters impact vulnerable communities, exacerbating pre-existing healthcare disparities due to factors such as social determinants of health, geographic isolation, and limited access to healthcare resources. This project investigate the root causes of these disparities and assess the resilience of healthcare systems.
Delivering meals to those in need
Improve the operational efficiency of senior meal delivery services, such as Meals on Wheels. Our role focuses on applying engineering principles to optimize delivery routes and streamline facility processes. Our work helps reduce costs, save time, and ultimately enhances the effectiveness of these programs.
Improving access to trauma care
Develop models to improve access to trauma care centers in Texas. The forecasting and optimization models aims to strategically locate new trauma care centers and aeromedical depots to enhance patient coverage, particularly in rural areas, while accounting for uncertain factors like the COVID-19 pandemic.
Improving airport checkpoint efficiency
The combination of current airport space constraints and limited Transportation Security Officers coupled with projected increases in air travelers and rising number of security threats will put more pressure on TSA staff and resources. This project aims to mitigate issues and enhance outcomes using DES, optimization and ML.
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