A Two-Stage Dynamic Programming Model for Nurse Rostering Problem Under Uncertainty

No abstract provided.Master of Science in EngineeringIndustrial and Manufacturing Systems Engineering, College of Engineering and Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/140733/1/WENJIE WANG_Thesis_Embedded.pdfDescription of WENJIE WANG_Thesis_Embedded.pdf : Thesi

Implementation of the WIN Workforce Stability Tool Kit

Precise and accurate staffing is vital to healthcare organizations. Comprehensive workforce planning is the foundation for successful staffing. Healthcare leaders need workforce data and tools that provide visibility and actionable intelligence to drive the best possible resource decisions. A workforce tool kit that nurse leaders can use to maintain visibility into their overall workforce picture can drive cost savings and provide operational wins. Lack of visibility into workforce metrics and tools drove the development of a tool kit that would aid in addressing some of these issues. The tools included provided real-time, market-specific data that was used to drive decisions on how best to use contingent resources to fill gap times in the core schedule. Other tools aided the leadership in tracking contingent resources and using flexible contract terms to more efficiently use the resources, rather than being committed to longer assignments. In addition, applications allowed staff nurses to communicate with each other regarding their schedules and receive instant notifications about open shifts, increasing their ability to step in and pick up shifts, preventing reliance on expensive contract labor resources. This application also provided staff with easy access to scheduling information, creating an online environment in which to propose shift trades in lieu of calling in sick and negatively affecting the organization. Testing of the tool kit took place in an inner-city Level II Trauma Center validated that the use of these tools had a positive impact on reducing and avoiding costs and reducing sick calls on two different units reporting to the same department director

Exploring Leadership Strategy Influence on Nursing Personnel Retention Within Safety-net Hospitals

Frequent turnover among a hospital\u27s nursing staff can profoundly impact organizational operating costs. With a national turnover rate of 17% in 2015, understanding the impact of management approaches on nurse attrition is vital to business success. Guided by Homan\u27s social exchange theory, the purpose of this single case study was to explore leadership strategies used by safety-net hospital leaders to increase nursing personnel retention. Data collection consisted of semistructured interviews from a purposive snowball sampling of 8 senior directors working at a safety-net hospital in southern Maryland. Additional information collected involved documents and artifacts related to human resources management policies and guidelines. Constant comparative method enabled the analysis and identification of latent patterns in words used by respondents. Through methodological triangulation, several themes emerged. These themes included engagement and management support, education and career development, teamwork and work atmosphere, recognition, relationship building and communication, and health reform and innovation. According to the study results, increasing employee engagement, offering training and career development, performing technological upgrades, and developing sustainable relationships are appropriate approaches for gaining nursing personnel commitment. The findings of this study are important to senior leaders and middle managers in healthcare and other industries as they seek to attract talented staff members to sustain their organizations. The conclusions in this study may contribute to positive social change through improved nursing staff retention, leading to better patient experiences, healthier communities, and more satisfied customers

An Integrated Framework for Staffing and Shift Scheduling in Hospitals

Over the years, one of the main concerns confronting hospital management is optimising the staffing and scheduling decisions. Consequences of inappropriate staffing can adversely impact on hospital performance, patient experience and staff satisfaction alike. A comprehensive review of literature (more than 1300 journal articles) is presented in a new taxonomy of three dimensions; problem contextualisation, solution approach, evaluation perspective and uncertainty. Utilising Operations Research methods, solutions can provide a positive contribution in underpinning staffing and scheduling decisions. However, there are still opportunities to integrate decision levels; incorporate practitioners view in solution architectures; consider staff behaviour impact, and offer comprehensive applied frameworks. Practitioners’ perspectives have been collated using an extensive exploratory study in Irish hospitals. A preliminary questionnaire has indicated the need of effective staffing and scheduling decisions before semi-structured interviews have taken place with twenty-five managers (fourteen Directors and eleven head nurses) across eleven major acute Irish hospitals (about 50% of healthcare service deliverers). Thematic analysis has produced five key themes; demand for care, staffing and scheduling issues, organisational aspects, management concern, and technology-enabled. In addition to other factors that can contribute to the problem such as coordination, environment complexity, understaffing, variability and lack of decision support. A multi-method approach including data analytics, modelling and simulation, machine learning, and optimisation has been employed in order to deliver adequate staffing and shift scheduling framework. A comprehensive portfolio of critical factors regarding patients, staff and hospitals are included in the decision. The framework was piloted in the Emergency Department of one of the leading and busiest university hospitals in Dublin (Tallaght Hospital). Solutions resulted from the framework (i.e. new shifts, staff workload balance, increased demands) have showed significant improvement in all key performance measures (e.g. patient waiting time, staff utilisation). Management team of the hospital endorsed the solution framework and are currently discussing enablers to implement the recommendation

STOCHASTIC MODELS FOR RESOURCE ALLOCATION, SERIES PATIENTS SCHEDULING, AND INVESTMENT DECISIONS

We develop stochastic models to devise optimal or near-optimal policies in three different areas: resource allocation in virtual compute labs (VCL), appointment scheduling in healthcare facilities with series patients, and capacity management for competitive investment. A VCL consists of a large number of computers (servers), users arrive and are given access to severs with user-specified applications loaded onto them. The main challenge is to decide how many servers to keep “on”, how many of them to preload with specific applications (so users needing these applications get immediate access), and how many to be left flexible so that they can be loaded with any application on demand, thus providing delayed access. We propose dynamic policies that minimize costs subject to service performance constraints and validate them using simulations with real data from the VCL at NC State. In the second application, we focus on healthcare facilities such as physical therapy (PT) clinics, where patients are scheduled for a series of appointments. We use Markov Decision Processes to develop the optimal policies that minimize staffing, overtime, overbooking and delay costs, and develop heuristic secluding policies using the policy improvement algorithm. We use the data from a local PT center to test the effectiveness of our proposed policies and compare their performance with other benchmark policies. In the third application, we study a strategic capacity investment problem in a duopoly model with an unknown market size. A leader chooses its capacity to enter a new market. In a continuous-time Bayesian setting, a competitive follower dynamically learns about the favorableness of the new market by observing the performance of the leader, and chooses its capacity and timing of investment. We show that an increase in the probability of a favorable market can strictly decrease the leaders expected discounted profit due to non-trivial interplay between leaders investment capacity and timing of the dynamically-learning follower.Doctor of Philosoph

Mitigating Hard Capacity Constraints in Facility Location Modeling

In many real-world settings, the capacity of processing centers is flexible due to a variety of operational tools (such as overtime, outsourcing, and backlogging demand) available to managers that allow the facility to accept demands in excess of the capacity constraint for short periods of time. However, most capacitated facility location models in the literature today impose hard capacity constraints that don’t capture this short term flexibility. Thus, current capacitated facility location models do not account for the operational costs associated with accepting excess daily demand, which can lead to suboptimal facility location and demand allocation decisions. To address this discrepancy, we consider a processing distribution system in which demand generated on a daily basis by a set of demand sites is satisfied by a set of capacitated processing facilities. At each demand site, daily demands for the entirety of the planning horizon are sampled from a known demand distribution. Thus, the day to day demand fluctuations may result in some days for which the total demand arriving at a processing facility exceeds the processing capacity, even if the average daily demand arriving at the processing facility is less than the daily processing capacity. We allow each processing facility the ability to hold excess demand in backlog to be processed at a later date and assess a corresponding backlog penalty in the objective function for each day a unit of demand is backlogged. This dissertation primarily focuses on three methods of modelling the aforementioned processing distribution system. The first model is the Inventory Modulated Capacitated Location Problem (IMCLP), which utilizes disaggregated daily demand parameters to determine the subset of processing facilities to establish, the allocation of demand sites to processing facilities, and the magnitude of backlog at each facility on each day that minimizes location, travel, and backlogging costs. Whereas the IMCLP assumes each demand site must be allocated to exactly one processing facility, the second model relaxes this assumption and allows demand sites to be allocated to different processing facilities on various days of the week. We show that such a cyclic allocation scheme can further reduce the system costs and improve service metrics as compared to the IMCLP. Finally, while the first two models incorporate daily fluctuations in demand over an extended time horizon, the problems remain deterministic in the sense that only one realization of demand is considered for each day of the planning horizon. As such, our final model presents a stochastic version of the IMCLP in which we assume a known demand distribution but assume the realization of daily demand is uncertain. In addition to assessing a penalty cost, we consider three types of chance constraints to restrict the amount of backlogged demand to a predetermined threshold. Using finite samples of random demand, we propose two multi-stage decomposition schemes and solve the mixed-integer programming reformulations with cutting-plane algorithms. In summary, this dissertation mitigates hard capacity constraints commonly found in facility location models by allowing incoming demand to exceed the processing capacity for short periods of time. In each of the modelling contexts presented, we show that the location and allocation decisions obtained from our models can result in significantly reduced costs and improved service metrics when compared to models that do not account for the likelihood that demands may exceed capacity on some days.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137042/1/leekayse_1.pd

Addressing Nonlinearity and Uncertainty via Mixed Integer Programming Approaches

The main focus of the dissertation is to develop decision-making support tools that address nonlinearity and uncertainty appearing in real-world applications via mixed integer programming (MIP) approaches. When making decisions under uncertainty, knowing the accurate probability distributions of the uncertain parameters can help us predict their future realization, which in turn helps to make better decisions. In practice, however, it is oftentimes hard to estimate such a distribution precisely. As a consequence, if the estimated distribution is biased, the decisions thus made can end up with disappointing outcomes. To address this issue, we model uncertainty in the decision-making process through a distributionally robust optimization (DRO) approach, which aims to find a solution that hedges against the worst-case distribution within a pre-defined ambiguity set, i.e., a collection of probability distributions that share some distributional and/or statistical characteristics in common. The role of the ambiguity set is crucial as it affects both solution quality and computational tractability of the DRO model. In this dissertation, we tailor the ambiguity sets based on the available historical data in healthcare operations and energy systems, and derive efficient solution approaches for the DRO models via MIP approaches. Through extensive numerical studies, we show that the DRO solutions yield better out-of-sample performance, and the computational performance of the proposed MIP approaches is encouraging. Finally, we provide some managerial insights into the operations of healthcare and energy systems.PHDIndustrial & Operations EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/155030/1/msryu_1.pd