Amineh Ghorbani is an assistant professor at the Engineering Systems and Services Department, Delft University of Technology, the Netherlands. She is also an affiliated member of the “Institutions for Collective Action” at Utrecht University. She obtained her M.Sc. in Computer Science (Artificial intelligence) from University of Tehran (Iran) (2009, honours) and her PhD from Delft University of Technology (2013, cum laude).

During her PhD, Amineh developed a meta-model for agent-based modelling, called MAIA, which describes various concepts and relations in a socio-technical system. This modelling perspective helped her develop a modelling paradigm that she refers to as institutional modelling.

Her current area of research is understanding the emergence and dynamics of institutions (set of rule organizing human society) using modelling. She is interested in how bottom-up collective action emerges and how institutions emergence and change within communities.

I am an Associate Professor in the Department of Industrial Engineering at the University of Engineering and Technology Taxila, Pakistan. I hold a PhD in Industrial Engineering and have more than 20 years of academic experience in teaching, research, and student supervision.

My research focuses on industrial and systems engineering and data-driven decision analytics. It combines multi-criteria decision-making, agent-based modeling, simulation, optimization, and machine learning to examine complex decisions involving uncertainty, competing objectives, and multiple stakeholders. Applications include manufacturing, energy systems, sustainability, and organizational decision-making.

My published research includes assessments of hybrid energy policies, Industry 4.0 readiness, and sustainable cement production. I am also a coauthor of the peer-reviewed CoMSES computational model “Integrating Reinforcement Learning in Agent-Based Modeling for Dynamic Investment Decisions.” The associated 2026 journal article combines reinforcement learning, response surface methodology, and neural networks within an agent-based modeling framework. This work uses NetLogo simulations to investigate adaptive investment decisions under uncertainty.

My ongoing research and student supervision explore multi-agent reinforcement learning, large language models, and agent-based modeling for supply-chain decision support under disruptions. These activities extend my interests in adaptive decision-making and the analysis of complex industrial systems.

Alongside research, I teach undergraduate and graduate courses, supervise postgraduate research and undergraduate projects, and review manuscripts for scholarly journals. I welcome academic and industry research collaborations involving computational modeling, simulation experiments, decision-support systems, and sustainable industrial operations.