Alireza Yazdani Esfidvajani, MSc

Designing optimization methods that make complex decisions reliable under uncertainty.

I am a doctoral candidate in the Operations Planning, Accounting and Control (OPAC) group at Eindhoven University of Technology. My research focuses on developing data-driven optimization methods that address uncertainty in decision-making, with a particular emphasis on robust optimization and decomposition techniques.

I work on Minimum-Volume Norm-Based Clustering (MVNBC), an approach to construct flexible uncertainty sets for robust optimization. This research combines mathematical modeling, convex analysis, and Benders decomposition, aiming to design algorithms that are both theoretically strong and computationally efficient.

Beyond methodology, I apply these techniques to domains such as supply chain management. In parallel, I conduct behavioral operations experiments, studying how human decision-makers interact with stochastic and robust optimization models in practice.

At TU/e, I am part of the AI-Planner of the Future programme, where I collaborate with academic and industry partners to bridge optimization theory with real-world decision support systems.

I obtained my Master’s degree in Civil Engineering (Transportation Planning) from Sharif University of Technology in 2018, after completing my Bachelor’s degree in Civil Engineering at Amirkabir University of Technology in 2015. My early research focused on transportation systems and decision modeling.

In 2021, I joined Eindhoven University of Technology as a PhD candidate in the OPAC group. My current research centers on robust optimization and decomposition methods for mixed-integer nonlinear problems, with applications in clustering and supply chain decision-making.

Alongside my research, I have gained experience in teaching, student supervision, and industry collaboration, supporting both academic progress and practical impact.