Keivan Tafakkori

I am a Ph.D. candidate in Industrial Engineering at the University of Tehran, specializing in developing sustainable and resilient strategies, tactics, and operations for manufacturing and service companies in the Industry 4.0 and 5.0 eras. I focus on finding economically efficient, environmentally friendly, and socially responsible solutions with real-world implications, which make systems, industries, and supply chains sustainable and resilient.

My expertise lies in developing optimization, data science, and machine learning models to solve challenging business problems. By combining operations research and machine learning, I contribute to the fields of operations research and management science.

I am eager to collaborate with industry leaders and fellow researchers to drive innovation and progress for systems, industries, and supply chains with sustainability and resiliency goals. I believe that different theoretical/practical problems just need different ways of explaining reality, representing reality, or learning from data observed or experienceable in reality, for a specific system, industry, or supply chain, to obtain feasible, logical, and optimal (or best possible) solutions, regions or policies. The way we algorithmize, model, and solve these problems to be scalable and stable, both in computation time and accuracy, matters a lot. I am optimistic about the future of sustainable and resilient industries and supply chains and am committed to contributing to this vision.

Some highlights of my achievements include the following:

Experienced 4+ years of developing mathematical models that have real-world impacts
Published papers in international journals and conferences such as JRESS, EAAI, CAIE, JRCS, and JMTM.
Reviewed papers for prestigious journals, including ESWA, TRE, ASOC, ORP, CAOR, SCA, IJPR, SMCA, and JCDE.
Received the Best Theoretical Thesis Award from the Iranian Institute of Industrial Engineering in 2020.
Gained valuable real-world experience through involvement in industrial and business projects, including Supplier Relationship Management (Hamrah -e Avval), Visual Management (MAPNA TUGA), and Business and Big Data Analytics (SAPCO ISACO).
Developed an integrated optimization environment in Python called feloopy which supports exact and heuristic optimization, constraint programming, convex optimization, multi-objective optimization, and multi-criteria decision-making with the most straightforward optimization programming language possible, which uses around 400 algorithms.
Experienced in working with cplex, gurobi, xpress, gams, pyomo, pulp, ortools, scikit-learn, pytorch, and tensorflow, typical for operations research and machine learning.