Capacity Planning for Services with Customers Having Time-Dependent Priorities

Researchers: Jae Yoon Hwang, Tianyu Xu, Xinran Yu

With the increase in product or service complexity, and queueing-time-dependent urgencies among customers, service providers are facing difficulties in setting priorities to their customer needs. Hence, capacity planning using a discrete-event simulation can help deal with evaluating various forms of prioritizations. In these settings, a customer’s urgency will increase the longer they remain in queue.

Examples of applications are listed below:

1. In healthcare, triage systems prioritize patient care based on severity, improving resource use, and reducing wait times.
2. Transportation systems optimize schedules and capacity, enhancing service and prioritizing disabled passengers.
3. Call centers use prioritization systems, shortening waiting times and increasing satisfaction.
4. In law enforcement, calls are prioritized by severity, allowing for quicker responses to high threat incidents.
5. Repair technicians use prioritization systems to address urgent issues first, reducing downtime for critical equipment.

The project will consist of two parts. The first part will evaluate different prioritization rules such as a comparing segmenting customer to systems with more general service providers. The second part explores the use of machine learning to model the ways that experienced service providers make decisions when setting priorities, then creating ways to evaluate students during training.