Seyed is a PhD candidate in operations research at École Polytechnique de Montréal and also a researcher at CIRRELT. His research interest is the application of integer programming decomposition methods such as Benders, Dantzig-Wolfe and Lagrangian in large-scale optimization problems with integer variables, especially stochastic integer programs. Robust optimization and dynamic programming are other topics of research interest. He applies these methods in different areas such as healthcare, logistics and distribution, production scheduling and inventory management.
His PhD project focuses on the application of integer programming decomposition methods in healthcare optimization problems. In the first portion of his project, Seyed has developed a constraint-programming-based branch-and-price-and-cut algorithm for an integrated operating room planning and scheduling problem. In the second portion, he proposes a Dantzig-Wolfe decomposition and some Benders algorithms for robust optimization models with integer adversarial variables and applied to the proposed method on a nurse planning problem. For the third part of his project, he is currently working on a home healthcare scheduling problem with stochastic travel and service times.