Stochastic scheduling of chemotherapy appointments considering patient acuity levels
Serhat Gül, Department of Industrial Engineering, TED University
The uncertainty in infusion durations and non-homogeneous care level needs of patients are the critical factors that lead to difficulties in chemotherapy scheduling. We study the problem of scheduling patient appointments and assigning patients to nurses under uncertainty in infusion durations for a given day. We consider instantaneous nurse workload, represented in terms of total patient acuity levels, and chair availability while scheduling patients. We formulate a two-stage stochastic mixed-integer programming model with the objective of minimizing expected weighted sum of excess patient acuity, waiting time and nurse overtime. We propose a scenario bundling-based decomposition algorithm to find near-optimal schedules. We use data of a major university hospital in Ankara to generate managerial insights related to the impact of acuity consideration, and number of nurses and chairs on the performance measures. We compare the schedules obtained by the algorithm with the baseline schedules and those found by applying several relevant scheduling heuristics. Finally, we assess the value of stochastic solution.
Serhat Gül is an assistant professor in the Department of Industrial Engineering at TED University. He completed his B.Sc. in Industrial Engineering at Sabancı University, and his M.Sc. and PhD in Industrial Engineering at Arizona State University. Before joining TED University, he worked as a postdoctoral research fellow at Northeastern University and Georgia Institute of Technology, and as a visiting faculty at Sabancı University. His primary research interests include stochastic optimization and its applications to health care delivery systems and public health planning. His articles appeared in journals such as Production and Operations Management, INFORMS Journal on Computing, Naval Research Logistics, European Journal of Operational Research, Omega and Service Science.
Friday, June 2, 2023, 4.00 pm - Zoom