Human-Centric Decision-Making in Operations Research
PhD candidate: Xinyu Mao
Operations Research (OR) models use mathematical algorithms to optimize complex systems ranging from logistics networks to hospital schedules. However, theoretically perfect plans often face friction in practice because operational decisions are rarely executed automatically. Human planners frequently interpret, adjust, or override algorithms based on their experience and unforeseen contexts. This PhD project explores how to systematically integrate these human elements into OR based decision support systems. Grounded in the Action Design Research, the study begins with a Systematic Literature Review to build a Human-Centric Decision-Making framework that maps decision processes against organizational autonomy and temporal constraints. The research then validates and refines this framework through empirical case studies in logistics, production, and healthcare. By combining quantitative optimization with interactive tools, the work evaluates behavioral impacts like trust and cognitive load, testing practical ways to embed human in the loop mechanisms. The core objective is to design adaptive systems where algorithmic efficiency and human judgment function as a unified whole.
