Joint optimization of allocation and release policy decisions for surgical block time under uncertainty

The research presented in this dissertation contributes to the growing literature on applications of operations research methodology to healthcare problems through the development and analysis of mathematical models and simulation techniques to find practical solutions to fundamental problems facing nearly all hospitals. In practice, surgical block schedule allocation is usually determined regardless of the stochastic nature of case demand and duration. Once allocated, associated block time release policies, if utilized, are often simple rules that may be far from optimal. Although previous research has examined these decisions individually, our model considers them jointly. A multi-objective model that characterizes financial, temporal, and clinical measures is utilized within a simulation optimization framework. The model is also used to test “conventional wisdom” solutions and to identify improved practical approaches. Our result from scheduling multi-priority patients at the Stafford hospital highlights the importance of considering the joint optimization of block schedule and block release policy on quality of care and revenue, taking into account current resources and performance. The proposed model suggests a new approach for hospitals and OR managers to investigate the dynamic interaction of these decisions and to evaluate the impact of changes in the surgical schedule on operating room usage and patient waiting time, where patients have different sensitivities to waiting time. This study also investigated the performance of multiple scheduling policies under multi-priority patients. Experiments were conducted to assess their impacts on the waiting time of patients and hospital profit. Our results confirmed that our proposed threshold-based reserve policy has superior performance over common scheduling policies by preserving a specific amount of OR time for late-arriving, high priority demand

The role of statistical methodology in simulation

statistical methods;simulation;operations research

An Analysis of the Supplier Selection Process

Customers select suppliers based on the relative importance of different attributes such as quality, price, flexibility, and delivery performance. This study examines the difference between managers\u27 rating of the perceived importance of different supplier attributes and their actual choice of suppliers in an experimental setting. We use two methods: a Likert scale set of questions, to determine the importance of supplier attributes; and a discrete choice analysis (DCA) experiment, to examine the choice of suppliers. The results indicate that although managers say that quality is the most important attribute for a supplier, they actually choose suppliers based largely on cost and delivery performance

Scheduling surgical cases in a day-care environment: a branch-and-price approach.

In this paper we will investigate how to sequence surgical cases in a day-care facility so that multiple objectives are simultaneously optimized. The limited availability of resources and the occurrence of medical precautions, such as an additional cleaning of the operating room after the surgery of an infected patient, are taken into account. A branch-and-price methodology will be introduced in order to develop both exact and heuristic algorithms. In this methodology, column generation is used to optimize the linear programming formulation of the scheduling problem. Both a dynamic programming approach and an integer programming approach will be specified in order to solve the pricing problem. The column generation procedure will be combined with various branching schemes in order to guarantee the integrality of the solutions. The resulting solution procedures will be thoroughly tested and evaluated using real-life data of the surgical day-care center at the university hospital Gasthuisberg in Leuven (Belgium). Computational results will be summarized and conclusions will eventually be formulated.Branch-and-price; Column generation; Health care operations; Scheduling;

A Market Utility-Based Model for Capacity Scheduling in Mass Services

Only a small set of employee scheduling articles have considered an objective of profit or contribution maximization, as opposed to the traditional objective of cost (including opportunity costs) minimization. In this article, we present one such formulation that is a market utility-based model for planning and scheduling in mass services (mums), mums is a holistic approach to market-based service capacity scheduling. The mums framework provides the structure for modeling the consequences of aligning competitive priorities and service attributes with an element of the firm’s service infrastructure. We developed a new linear programming formulation for the shifts-scheduling problem that uses market share information generated by customer preferences for service attributes. The shift-scheduling formulation within the framework of mums provides a business-level model that predicts the economic impact of the employee schedule. We illustrated the shift-scheduling model with empirical data, and then compared its results with models using service standard and productivity standard approaches. The result of the empirical analysis provides further justification for the development of the market-based approach. Last, we discuss implications of this methodology for future research

Using response surface design to determine the optimal parameters of genetic algorithm and a case study

Copyright © 2013 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Production Research on 09 June 2013, available online: http://www.tandfonline.com/10.1080/00207543.2013.784411Genetic algorithms are efficient stochastic search techniques for approximating optimal solutions within complex search spaces and used widely to solve NP hard problems. This algorithm includes a number of parameters whose different levels affect the performance of the algorithm strictly. The general approach to determine the appropriate parameter combination of genetic algorithm depends on too many trials of different combinations and the best one of the combinations that produces good results is selected for the program that would be used for problem solving. A few researchers studied on parameter optimisation of genetic algorithm. In this paper, response surface depended parameter optimisation is proposed to determine the optimal parameters of genetic algorithm. Results are tested for benchmark problems that is most common in mixed-model assembly line balancing problems of type-I (MMALBP-I)

A Simulation-Based Evaluation Of Efficiency Strategies For A Primary Care Clinic With Unscheduled Visits

In the health care industry, there are strategies to remove inefficiencies from the health delivery process called efficiency strategies. This dissertation proposed a simulation model to evaluate the impact of the efficiency strategies on a primary care clinic with unscheduled walk-in patient visits. The simulation model captures the complex characteristics of the Orlando Veteran\u27s Affairs Medical Center (VAMC) primary care clinic. This clinic system includes different types of patients, patient paths, and multiple resources that serve them. Added to the problem complexity is the presence of patient no-shows characteristics and unscheduled patient arrivals, a problem which has been until recently, largely neglected. The main objectives of this research were to develop a model that captures the complexities of the Orlando VAMC, evaluate alternative scenarios to work in unscheduled patient visits, and examine the impact of patient flow, appointment scheduling, and capacity management decisions on the performance of the primary care clinic system. The main results show that only a joint policy of appointment scheduling rules and patient flow decisions has a significant impact on the wait time of scheduled patients. It is recommended that in the future the clinic addresses the problem of serving additional walk-in patients from an integrated scheduling and patient flow viewpoint

Application of agent-based simulation to the modelling and management of hospital-acquired infections

Hospital-acquired infections (HAIs) are a big threat to the well-being of patients and place a heavy burden on hospital resources. The thesis provides the first attempt to apply agent-based simulation (ABS) to describe the transmission dynamics and evaluate the intervention policies of HAIs in general and Methicillin-resistant Staphylococcus aureus (MRSA) in particular. Based on the proposed taxonomy of potential methods for modelling HAIs, the relative advantages of ABS compared to other modelling methods are investigated. The comparison provides a theoretical justification to the use of ABS. The main methodological issues, including the representation of patient agents and the modelling of the transmission process, are discussed and a framework of applying ABS on HAI modelling is proposed. Guided by the framework, a MRSA model is built and validated using observed data from an empirical study. The model is more realistic and flexible than previous MRSA models and embeds intervention policies that have not been systematically studied such as the turnaround time and frequency of screening tests and the decolonisation treatment. Various interventions and influencing factors are systematically evaluated by formal experimental design methods including the fractional factorial design and the response surface design. The experimental results indicate that the use of rapid screening tests with shorter test turnaround time is the most effective policy to reduce MRSA transmission in the hospital setting. The introduction of admission and repeat screening is another effective policy; however, the effectiveness is not linear and may depend on patients’ lengths of stay. Providing more isolation facilities is also an effective policy but its effectiveness is significantly dependent on the efficacy of isolation. To demonstrate the potential and flexibility of ABS, the MRSA model is extended to include a competitive infection, to include multiple hospital units and HCW agents, and the wider community

The Military Inventory Routing Problem: Utilizing Heuristics within a Least Squares Temporal Differences Algorithm to Solve a Multiclass Stochastic Inventory Routing Problem with Vehicle Loss

Military commanders currently resupply forward operating bases (FOBs) from a central location within an area of operations mainly via convoy operations in a way that closely resembles vendor managed inventory practices. Commanders must decide when and how much inventory to distribute throughout their area of operations while minimizing soldier risk. Technology currently exists that makes utilizing unmanned cargo aerial vehicles (CUAVs) for resupply an attractive alternative due to the dangers of utilizing convoy operations. Enemy actions in wartime environments pose a significant risk to a CUAV\u27s ability to safely deliver supplies to a FOB. We develop a Markov decision process (MDP) model to examine this military inventory routing problem (MILIRP). In our first paper we examine the structure of the MILIRP by considering a small problem instance and prove value function monotonicity when a sufficient penalty is applied. Moreover, we develop a monotone least squares temporal differences (MLSTD) algorithm that exploits this structure and demonstrate its efficacy for approximately solving this problem class. We compare MLSTD to least squares temporal differences (LSTD), a similar ADP algorithm that does not exploit monotonicity. MLSTD attains a 3:05% optimality gap for a baseline scenario and outperforms LSTD by 31:86% on average in our computational experiments. Our second paper expands the problem complexity with additional FOBs. We generate two new algorithms, Index and Rollout, for the routing portion and implement an LSTD algorithm that utilized these to produce solutions 22% better than myopic generated solutions on average. Our third paper greatly increases problem complexity with the addition of supply classes. We formulate an MDP model to handle the increased complexity and implement our LSTD-Index and LSTD-Rollout algorithms to solve this larger problem instance and perform 21% better on average than a myopic policy