Staff profile

Brief introduction

Gar Goei is an associate professor with the Department of Management and Marketing since November 2023. He is originally from Singapore and has spent about 2 years in the Netherlands. He had also previously did his Bachelor's and Master's in the UK.

Prior to joining academia, Gar Goei had 5 years of experience in public policy with the Singapore Government, in the areas of applying data science to public policy, data governance and human resource analytics. 

Research

Gar Goei's research focuses around decision-making under uncertainty, and developing models, frameworks, methods and algorithms that help decision-makers go from data to decisions. In his primary stream of research, he has applied techniques in robust optimization to the solution of optimization problems in queueing networks. More recently, he is studying what divides existing paradigms in machine learning and optimization and is developing and proposing new ways to integrate them harmoniously. His research is primarily applied to business areas such as service operations management, supply chain managment, healthcare operations management, and energy and water. His research has been published in journals such as Operations Research and Manufacturing & Service Operations Management.

Teaching

Gar Goei teaches the Project Management course for the Online MBA. He is also developing a Data Science for Business course for the Executive Masters in Supply Chain Management. Previously, he had taught business analytics at the undergraduate, graduate and MBA levels. 

Gar Goei is also active in doctoral training. He has graduated two PhD students, and is currently co-supervising two PhD students. 

Journal Article

• Zhu, T., Yeo, N. T. B., Gao, S. Y., & Loke, G. G. (2023). Inventory-Responsive Donor-Management Policy: A Tandem Queueing Network Model. Manufacturing & Service Operations Management, 25(4), 1585-1602. https://doi.org/10.1287/msom.2023.1228
• Jaillet, P., Loke, G. G., & Sim, M. (2022). Strategic Workforce Planning Under Uncertainty. Operations Research, 70(2), 1042-1065. https://doi.org/10.1287/opre.2021.2183
• Zhou, M., Loke, G. G., Bandi, C., Liau, Z. Q. G., & Wang, W. (2022). Intraday Scheduling with Patient Re-entries and Variability in Behaviours. Manufacturing & Service Operations Management, 24(1), 561-579. https://doi.org/10.1287/msom.2020.0959