Presented By: U-M Industrial & Operations Engineering
IOE Lunch & Learn Seminar Series: Minseok Ryu, U-M IOE
Nurse Staffing under Absenteeism: A Distributionally Robust Optimization Approach
This event is open to all IOE PhD students, faculty, and staff. Lunch will be provided. In order to get an accurate count for food, please RSVP by Tuesday, November 5, 2019. Space is limited to 20 participants.
Title:
Nurse Staffing under Absenteeism: A Distributionally Robust Optimization Approach
Abstract:
We study the nurse staffing problem under random nurse demand and absenteeism. While the demand uncertainty is exogenous (stemming from the random patient census), the absenteeism uncertainty is endogenous, i.e., the number of nurses who show up for work partially depends on the nurse staffing level. For the quality of care, many hospitals have developed float pools of nurses by cross-training, so that a pool nurse can be assigned to the units short of nurses. In this paper, we propose a distributionally robust nurse staffing (DRNS) model that considers both exogenous and endogenous uncertainties. We derive a separation algorithm to solve this model under an arbitrary structure of float pools. In addition, we identify several pool structures that often arise in practice and recast the corresponding DRNS model as a monolithic mixed-integer linear program, which facilitates off-the-shelf commercial solvers. Furthermore, we optimize the float pool design to reduce the cross-training while achieving a specified target staffing costs. The numerical case studies, based on the data of a collaborating hospital, suggest that the units with high absenteeism probability should be pooled together.
Bio:
Minseok Ryu is a Ph.D. candidate in the Department of Industrial and Operations Engineering (IOE) at the University of Michigan (U-M). He obtained both bachelor's and master's degrees from Korea Advanced Institute of Science and Technology (KAIST). His research interests are in the fields of operations research and data analytics, especially methodologies for data-driven prescriptive analytics, with applications in healthcare operations and energy systems. Minseok is a Michigan Institute of Computational Discovery and Engineering (MICDE) student fellow (since 2015), and a recipient of the U-M Rackham graduate student research grant and IOE fellowship. He worked in the Los Alamos National Lab as a research intern in Summer 2019 and is the sole instructor for an undergraduate core course - IOE 310: Introduction to Optimization Methods in Fall 2019.
Title:
Nurse Staffing under Absenteeism: A Distributionally Robust Optimization Approach
Abstract:
We study the nurse staffing problem under random nurse demand and absenteeism. While the demand uncertainty is exogenous (stemming from the random patient census), the absenteeism uncertainty is endogenous, i.e., the number of nurses who show up for work partially depends on the nurse staffing level. For the quality of care, many hospitals have developed float pools of nurses by cross-training, so that a pool nurse can be assigned to the units short of nurses. In this paper, we propose a distributionally robust nurse staffing (DRNS) model that considers both exogenous and endogenous uncertainties. We derive a separation algorithm to solve this model under an arbitrary structure of float pools. In addition, we identify several pool structures that often arise in practice and recast the corresponding DRNS model as a monolithic mixed-integer linear program, which facilitates off-the-shelf commercial solvers. Furthermore, we optimize the float pool design to reduce the cross-training while achieving a specified target staffing costs. The numerical case studies, based on the data of a collaborating hospital, suggest that the units with high absenteeism probability should be pooled together.
Bio:
Minseok Ryu is a Ph.D. candidate in the Department of Industrial and Operations Engineering (IOE) at the University of Michigan (U-M). He obtained both bachelor's and master's degrees from Korea Advanced Institute of Science and Technology (KAIST). His research interests are in the fields of operations research and data analytics, especially methodologies for data-driven prescriptive analytics, with applications in healthcare operations and energy systems. Minseok is a Michigan Institute of Computational Discovery and Engineering (MICDE) student fellow (since 2015), and a recipient of the U-M Rackham graduate student research grant and IOE fellowship. He worked in the Los Alamos National Lab as a research intern in Summer 2019 and is the sole instructor for an undergraduate core course - IOE 310: Introduction to Optimization Methods in Fall 2019.
Related Links
Explore Similar Events
-
Loading Similar Events...
