PhD Thesis Defense

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TITLE: Capacity Planning and Scheduling with Applications in HealthcareSPEAKER:  Monica VillarrealADVISER: Dr. Pinar KeskinocakSUMMARY:The healthcare expenditure in the United States reached $3.8 trillion dollars in 2013, and it is projected that it will continue to outpace the rest of the economy. There are strong incentives to improve the efficiency of the healthcare system through a better planning and management of resources. In this thesis we address three capacity planning problems motivated by healthcare applications. In the first application, 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 and other staff planning constraints.  We also propose a heuristic to solve the model's second phase. We implement these models using historical data from a community hospital and analyze the effect of different model parameters and settings. Compared with the current practice, we reduce delays and staff pooling at no additional cost. We validate these conclusions through a simulation model. In the second application, we consider the problem of staff planning and scheduling when there is an accepted time window between orders arrival and fulfillment, with the goal 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 simultaneously scheduling the staff and the forecasted 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 healthcare 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 to solve this problem. In the third application, we consider a non-consumable resource planning problem. Demand consists of a set of jobs. Each job has a scheduled start time and duration, and corresponds to a particular demand class that requires a subset of resources. Jobs can be `accepted' or `rejected', and the service level is measured by the acceptance of jobs. The goal is to find the capacity level that minimizes the total cost of the resources, subject to service level constraints, globally and by demand class. We first analyze the complexity of this problem under different special cases, and then we propose a model to find the optimal inventory for each type of resource. We show the convergence of the sample average approximation method to solve a stochastic extension of the model. This problem is motivated by the inventory planning of surgical instruments for ORs. We study the effects of different model parameters and settings on the cost and service levels, based on surgical data from a community hospital.


  • Workflow Status: Published
  • Created By: Anita Race
  • Created: 12/01/2014
  • Modified By: Fletcher Moore
  • Modified: 04/13/2017


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