Title: Capacity Planning and Scheduling with Applications in Healthcare
Advisor: Dr. Pinar Keskinocak
Committee members: Dr. Turgay Ayer, Dr. David Goldsman, Dr. Paul Griffin (Penn State) and Dr. Julie Swann.
Date, time, and venue: Thursday, December 4, 11:00 AM, Groseclose 204 (Academic Office)

Thesis summary: In this research we address capacity planning problems with different demand and service characteristics inspired by health care applications. In the first topic, we develop, implement, and assess the impact of analytical models, accompanied by a decision-support tool, for operating room (OR) staff planning decisions with different service lines. First, we propose a methodology to forecast the staff demand by service line. We use these results in a 2-phase mathematical model that defines the staffing budget for each service line, and then decides how many staff to assign to each potential shift and day pair; while considering staff overtime and pooling policies, ease of implementation, and other staff planning constraints. We also propose and evaluate a heuristic to solve the model’s second phase. We implement and test these models using historical data from a not-for-profit, community hospital, which performs 8,000 surgical procedures annually in 14 ORs. We reduce delays and staff pooling at no additional cost. We validated these conclusions through a simulation model. We also analyzed the effect of different staff management policies. In the second topic, we consider the problem of staff scheduling when there is an accepted time window between orders arrival and fulfillment, with the objective of obtaining a balanced schedule that focuses on on-time demand fulfillment but also considers staff characteristics and operative practices. This means that solving this problem requires to simultaneously schedule the staff and demand. We propose, implement, and analyze the results of a model for staff and demand scheduling under this setting, accompanied by a decision tool. We implement this model in a company that offers document processing and other back-office services to health care providers. We provide details on the model validation, implementation and results, including an increase of the company’s staff productivity in about 25%. We also provide insights on the effects of some of the model’s parameters and settings and assess the performance of a proposed heuristic. In the third topic, we consider a resource planning problem that arises in systems with non-consumable resources. Demand consists of a set of jobs, where each job has a given scheduled start time and a duration. There are multiple job types, each corresponding to a particular demand class and requiring a predefined subset of resources to be completed. Jobs can be ‘accepted’ or ‘rejected’. The goal is to balance the cost of building these resource capacities and the level of service provided, measured by the acceptance and completion of jobs. We propose a model to find the optimal capacity level for each type of resource that minimizes the total cost of this inventory, subject to certain service constraints. These service constraints are considered at a global level and at a demand class level. We analyze the complexity of this model, as well as a stochastic extension. This problem was motivated by the instrumentation planning of a hospital’s surgical department, where surgical cases are scheduled in advance as requested by surgeons and their patients. We use surgical data from a community hospital to study the effects of different model parameters and settings on the instrumentation cost and service levels.

Monica Villarreal, Ph.D. Student H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology 775 Ferst Drive NW, Atlanta GA 30332, USA Email: monica.v@gatech.edu