My research is focused on service operation and interpretable decision making in innovative business models. I am primarily interested in utilizing data science to derive decision support tools for complex service systems. My current work study congestion and waiting time prediction problems, and data-driven process improvement.
We study the problem of predicting congestion risk in service systems, a factor associated with poor service experience, higher costs, and even medical risk (e.g., in ICUs). We define "high-risk states" in queuing models as system states that are likely to lead to a congested state in the near future, and strive to formulate simple, interpretable, rules for determining whether a given system state is high-risk. We employ methods from queueing theory, simulation, and machine learning (ML) to devise simple prediction rules, and demonstrate their effectiveness through extensive computational study, which includes a large scale ICU model validated using data. Our study suggests combining custom model-based interpretable features with linear models can accurately predict congestion in ICUs.
We study the problem of predicting in real-time customers' waiting time in partially visible queueing systems by a predictor who does not observe some of the customers that interact with the system. The problem arises due to technological limitations that prevent monitoring all customers.
We derive closed-form expressions for the prediction problem in a partially visible M/M/1 queue, and propose effective data-driven approaches to solve the prediction problem for more general queues. The effectiveness of our approach is evaluated using both simulated data and real data from a large outpatient hospital.
Omnichannel system is a common operation strategy which provides multiple ways for customers to experience the business. One feature of such system is the information heterogeneity across channels. For example, to customers, the queue length in the physical store is visible, while the queue length in the online ordering channel is invisible. We study customers’ decision making in an omnichannel system with partially observable queue (FCFS discipline or visible-class priority). We find that when market sizes under fully and partially observable systems are comparable, partially observable system generates higher throughput if customers are not scared away by invisibility. Surprisingly, even with less information, partially observable system can have higher social welfare when the invisible arrival rate is high. In that case, the customers are scared away and the system becomes less congested.
Staff Sharing Under Uncertainty
Finished preliminary analysis (work in progress)
Queueing model | Staffing
Hospitals encounter great challenges to maintain patient care as the coronavirus pandemic exacerbates the staff shortages. Many measures have been taken to alleviate this problem, such as hiring temporary nurses from travel agencies, and retaining the current workforce with monetary incentives. However, these measures are often very costly and need to be carefully planned. I propose a two step staff sharing policy that allows the hospital to distribute its capacity to its units after random demand is realized. The policy is derived based on fluid approximation for the queueing model representing the hospital system. The preliminary results show that staff sharing could reduce the overall cost by allocating expensive capacity more effectively, especially when the demand for different units is highly variable (as in the pandemic) and negatively correlated.
Operations Management, MBA (Rating: 4.5/5)
Advanced Business Modeling, MBA/MS
Programming for Analytics, MS
Social Media Analytics, MS
Information System for Management, MBA
Introduction to Business Analytics, MBA/MS
INFORMS Healthcare Conference, Virtual, 2021
CORS Annual Conference, Virtual, 2021
POMS 31th Annual Conference, Virtual, 2021
INFORMS Annual Meeting, Virtual, 2020
INFORMS Annual Meeting, Seattle, WA, 2019
INFORMS Workshop on Data Science, Seattle, WA, 2019
INFORMS Healthcare Conference, Cambridge, MA, 2019
POMS 30th Annual Conference, Washington D.C., 2019