Md Abdul Quddus
Textile Engineering, Chemistry and Science
Assistant Professor
Textile Engineering, Chemistry and Science
Textiles Complex 3310
919.515.6553 mquddus@ncsu.edu WebsiteBio
Md Abdul Quddus is an Assistant Professor in the Department of Textile Engineering, Chemistry and Science at NC State University. He also serves as an Associate Faculty in the Operations Research Graduate Program at NC State University. He served as an assistant professor in industrial & systems engineering in the Department of Engineering & Technology at Southeast Missouri State University. He also has over five years of experience at FedEx Express as a Senior Operations Research Advisor, where he worked on various logistics research projects.
Quddus’s publications have appeared in journals such as Transportation Research Part E, International Journal of Production Economics, Expert Systems with Applications, Applied Energy, Annals of Operations Research, and several conference proceedings. He is a member of the Institute for Operations Research and the Management Sciences (INFORMS) and the Institute of Industrial and Systems Engineers (IISE).
Education
Ph.D. Industrial and Systems Engineering Mississippi State University 2018
M.S. Industrial Engineering Lamar University 2015
B.S. Industrial and Production Engineering Khulna University of Engineering and Technology 2011
Area(s) of Expertise
Quddus's research focuses on supply chain and logistics, big data analytics, stochastic programming, geospatial analytics for optimization, and simulation. Furthermore, his specialties also include on the application of AI, deep learning, cloud computing, and operations research techniques to solve large scale supply chain network and risk management problems, and sustainable manufacturing.
Publications
- Enhancing cotton yield prediction with robust deep neural network-based framework , European Journal of Agronomy (2025)
- Hybrid Drone and Truck Delivery Optimization in Remote Areas Using Geospatial Analytics , Sustainability (2025)
- Designing a reliable electric vehicle charging station expansion under uncertainty , International Journal of Production Economics (2021)
- Assessing the Efficiency of 4-Way Stops Compared to Traditional Traffic Signals: A Simulation Approach , methodology (2020)
- A stochastic programming approach for electric vehicle charging station expansion plans , International Journal of Production Economics (2019)
- Managing load congestion in electric vehicle charging stations under power demand uncertainty , Expert Systems with Applications (2019)
- Modeling electric vehicle charging station expansion with an integration of renewable energy and Vehicle-to-Grid sources , Transportation Research Part E Logistics and Transportation Review (2019)
- A collaborative energy sharing optimization model among electric vehicle charging stations, commercial buildings, and power grid , Applied Energy (2018)
- Designing a Reliable and Congested Multi-Modal Facility Location Problem for Biofuel Supply Chain Network , Energies (2018)
- A chance-constrained two-stage stochastic programming model for reliable microgrid operations under power demand uncertainty , Sustainable Energy Grids and Networks (2017)
Grants
During continuous dyeing, the final shade does not match target shade. Looking to leverage AI/ML to predict adds to dyebath upstream to get final shade to CIE ��E2000 < 0.8. Factors that could affect final share: fabric properties (fiber properties, yarn properties, fabric construction), wet pickup, dye strength, machine parameters, and dye formulation. Machine Learning is heavily dependent on data to train and validate models. The first stage is to design a data acquisition system that will collate data from varies sensors in the continuous dyeing process. (See Figure 1) into a central relational database.
This proposal aims to investigate the economic viability and policy strategies for sustainable transportation in hemp supply chains, specifically focusing on hemp fibers for textiles and construction materials. By applying principles of industrial ecology, the study seeks to enhance cost-effectiveness and reduce environmental impacts. This research will use a lifecycle approach to examine hemp transportation, offering insights into optimized logistics, resource reuse, and policy recommendations for sustainable growth in the hemp industry.