Stochastic scheduling of chemotherapy appointments considering patient acuity levels

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 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

Stochastic scheduling of chemotherapy appointments considering patient acuity levels

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 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

MODELS AND OPTIMIZATION FOR ELECTIVE SURGERY SCHEDULING UNDER UNCERTAINTY CONSIDERING PATIENT HEALTH CONDITION

The managerial aspects to run a healthcare system are becoming increasingly important for patient safety. More than one patients are competing each other to be treated using limited medical resources in a healthcare system. The limited medical resources include surgeons, physicians, anesthesiologists, nurses, operating rooms, wards, etc. Therefore, patient safety is related to how to run a healthcare system with the limited resources

A stochastic programming approach for chemotherapy appointment scheduling

Chemotherapy appointment scheduling is a challenging problem due to the uncertainty in pre-medication and infusion durations. In this paper, we formulate a two-stage stochastic mixed integer programming model for the chemotherapy appointment scheduling problem under limited availability and number of nurses and infusion chairs. The objective is to minimize the expected weighted sum of nurse overtime, chair idle time, and patient waiting time. The computational burden to solve real-life instances of this problem to optimality is significantly high, even in the deterministic case. To overcome this burden, we incorporate valid bounds and symmetry breaking constraints. Progressive hedging algorithm is implemented in order to solve the improved formulation heuristically. We enhance the algorithm through a penalty update method, cycle detection and variable fixing mechanisms, and a linear approximation of the objective function. Using numerical experiments based on real data from a major oncology hospital, we compare our solution approach with several scheduling heuristics from the relevant literature, generate managerial insights related to the impact of the number of nurses and chairs on appointment schedules, and estimate the value of stochastic solution to assess the significance of considering uncertainty

Scheduling Under Uncertainty: Applications to Aviation, Healthcare and Aerospace

When scheduling a project or a mission, it is often challenging to know in advance the exact duration of each task or which resource will be available. Processing times and resource availability are often subject to variability and may only be known at the last minute. Ignoring this uncertainty when planning a project can lead to adverse outcomes such as additional costs, missed deadlines or failed tasks. Conversely, modeling uncertainty in the scheduling decision process has potential to create more robust schedules that will mitigate these negative outcomes. However, the complexity of deterministic scheduling problems is further increased in their stochastic counterpart and many challenges arise when attempting to model and solve scheduling problems subject to uncertainty. In this dissertation we specifically study four scheduling problems arising from the transportation and the healthcare industries. In each of these four examples, we consider the limitations of deterministic approaches and the impact of uncertainty on the solution's structures and costs. Two problems come from the airline industry. We first create a model to generate flights gate assignments so as to reduce the probability of conflict between planes and mitigate delays. Then we develop a simulation tool to analyze delay recovery strategies under uncertainty. A third project deals with scheduling patient appointment times for chemotherapy infusion under uncertainty of their treatment time. The last area of application that we consider is satellite mission scheduling. We develop several models to solve the download planning problem for a single satellite while considering uncertainty in the availability of multiple receiving ground stations distributed across Earth.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137038/1/jctg_1.pd

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making