The overall aim in this proposal is to develop and evaluate an intervention method to promote workers' safety during human-robot collaboration

The current novel coronavirus (covid-19) pandemic is threatening human lives and global economic systems. The objective of this supplemental work is to identify optimal public health response strategies in an epicenter during a pandemic under severe resource constraints to slow human-to-human transmissions and ensure hospital capacity in a large city. Main pandemic response strategies include surge capacity planning in healthcare system (treatment) and social distancing/quarantine (prevention). Critical issues include how to allocate severely limited medical supplies and hospital resources (beds, staffs, protection gears, medicine, food, etc.) under uncertain epidemiological information; and how to design public health policies regarding implementation of large-scale social distancing and quarantine. To address these challenges, the research team proposes a new model based on queuing theory and infectious disease models that relates disease characteristics, transmission dynamics, social distancing, healthcare seeking behaviors, and resource limitations. This research aims to advance queuing methods in healthcare emergency response applied to preventing the loss of human life and social/economic disruptions during a pandemic. A hypothetical large-city use-case (e.g., Seattle) will be analyzed using the proposed model to provide insights into controlling an outbreak, with special consideration of the vulnerable populations (e.g. elderly, immunocompromised people).

In this research we will develop new staffing policies to control and stabilize performance for large-scale service networks with time-varying arrivals, focusing on the challenging case of relatively long service times, abandonment of waiting customers and non-exponential service-time and abandonment-time distributions. We aim to build new design principles, control policies and mathematical methods for analyzing and improving system performance. Because service systems often have complicated network structures (e.g., they may be distributed service centers, each with multiple pools of service representatives, serving multiple classes of customers), our research will produce effective staffing methods for various kinds of systems, ranging from queues having one station (consisting of one customer class and one service pool) to network queues (having multiple customer classes and service pools). On the one hand, we will establish convincing theoretical bases for these staffing techniques using many-server heavy-traffic limit theories; on the other hand, we will provide effective engineering validation using computer simulation experiments. Our research has a wide range of applications in service systems, but we will give special emphasis to healthcare systems.