@inproceedings{roberts_pegden_2017, title={The history of simulation modeling}, DOI={10.1109/wsc.2017.8247795}, abstractNote={During the past half-century simulation has advanced as a tool of choice for operational systems analysis. The advances in technology have stimulated new products and new environments without software standards or methodological commonality. Each new simulation language or product offers its own unique set of features and capabilities. Yet these simulation products are the evolution of research, development, and application. In this paper we interpret the historical development of simulation modeling. In our view simulation modeling is that part of the simulation problem-solving process that focuses on the development of the model. It is the interpretation of a real production (or service) problem in terms of a simulation language capable of performing a simulation of that real-world process. While “interpretation” is in the “eyes of the beholder” (namely us) there are some historical viewpoints and methods that influence the design of the simulation model.}, booktitle={2017 winter simulation conference (wsc)}, author={Roberts, S. D. and Pegden, D.}, year={2017}, pages={308–323} }
 @article{kesercioglu_roberts_uzsoy_2016, title={Computing the number of acute-care beds within NC Certificate of Need}, volume={5}, ISSN={["2047-6973"]}, DOI={10.1057/hs.2015.8}, abstractNote={North Carolina’s Certificate of Need legislation is intended to limit unnecessary growth in the number of acute-care beds throughout the state. In contrast to the current method of computing bed needs based on existing administrative units (counties), we estimate the service area of each acute-care facility using Voronoi diagrams such that the maximum distance from any point in the region to the nearest acute-care facility is minimized. The population in the service area is then used to determine the appropriate bed capacity for the hospital serving that region. The approach is applied to the problem of determining the appropriate number of acute-care beds for each hospital in the state. The discrepancies between the existing hospital sizes and the needed acute-care beds indicate areas where geographical inequities may need attention.}, number={2}, journal={HEALTH SYSTEMS}, author={Kesercioglu, Muge Gultekin and Roberts, Stephen D. and Uzsoy, Reha}, year={2016}, month={Jun}, pages={98–108} }
 @inproceedings{smith_roberts_2016, title={Sensitivity analysis for a whole hospital system dynamics model}, booktitle={2016 10th european conference on antennas and propagation (eucap)}, author={Smith, R. L. and Roberts, S. D.}, year={2016}, pages={1305–1316} }
 @book{joines_roberts_2015, title={Simulation modeling with SIMIO: A Workbook V4 (4th ed.)}, ISBN={9781519142207}, publisher={Sewickley, PA: SIMIO LLC}, author={Joines, Jeffrey A. and Roberts, Stephen D.}, year={2015} }
 @article{yarmand_ivy_roberts_2013, title={Identifying optimal mitigation strategies for responding to a mild influenza epidemic}, volume={89}, ISSN={["1741-3133"]}, DOI={10.1177/0037549713505334}, abstractNote={ Mathematical models have been developed to simulate influenza epidemics to help public health officials evaluate different control policies. In these models, often severe influenza epidemics with a considerable mortality rate are considered. However, as was the case for the 2009 H1N1 pandemic, some of the influenza epidemics are mild with insignificant mortality rates. In the case of a mild epidemic, the cost of different control policies becomes an important decision factor in addition to disease-related outcomes such as the attack rate. }, number={11}, journal={SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL}, author={Yarmand, Hamed and Ivy, Julie S. and Roberts, Stephen D.}, year={2013}, month={Nov}, pages={1400–1415} }
 @article{murphy_klein_smolen_klein_roberts_2013, title={Using Common Random Numbers in Health Care Cost-Effectiveness Simulation Modeling}, volume={48}, ISSN={["0017-9124"]}, DOI={10.1111/1475-6773.12044}, abstractNote={ObjectivesTo identify the problem of separating statistical noise from treatment effects in health outcomes modeling and analysis. To demonstrate the implementation of one technique, common random numbers (CRNs), and to illustrate the value of CRNs to assess costs and outcomes under uncertainty.}, number={4}, journal={HEALTH SERVICES RESEARCH}, author={Murphy, Daniel R. and Klein, Robert W. and Smolen, Lee J. and Klein, Timothy M. and Roberts, Stephen D.}, year={2013}, month={Aug}, pages={1508–1525} }
 @book{joines_roberts_2012, title={Simulation modeling with SIMIO: a workbook}, publisher={Sewickley, PA: SIMIO LLC}, author={Joines, J. A. and Roberts, S. D.}, year={2012} }
 @article{yaesoubi_roberts_2011, title={Payment contracts in a preventive health care system: A perspective from Operations Management}, volume={30}, ISSN={["0167-6296"]}, DOI={10.1016/j.jhealeco.2011.08.009}, abstractNote={We consider a health care system consisting of two noncooperative parties: a health purchaser (payer) and a health provider, where the interaction between the two parties is governed by a payment contract. We determine the contracts that coordinate the health purchaser-health provider relationship; i.e. the contracts that maximize the population's welfare while allowing each entity to optimize its own objective function. We show that under certain conditions (1) when the number of customers for a preventive medical intervention is verifiable, there exists a gate-keeping contract and a set of concave piecewise linear contracts that coordinate the system, and (2) when the number of customers is not verifiable, there exists a contract of bounded linear form and a set of incentive-feasible concave piecewise linear contracts that coordinate the system.}, number={6}, journal={JOURNAL OF HEALTH ECONOMICS}, author={Yaesoubi, Reza and Roberts, Stephen D.}, year={2011}, month={Dec}, pages={1188–1196} }
 @inproceedings{roberts_2011, title={Tutorial on the simulation of healthcare systems}, DOI={10.1109/wsc.2011.6147860}, abstractNote={For a variety of reasons, simulation has enjoyed widespread application in health care and health care delivery systems. Although the dominant modeling methodology is discrete event simulation, numerous studies employ system dynamics, agent-based simulation, and hybrid/combined methods. Software has been increasingly adapted to health care through enhanced visualizations and modeling. Virtually every health care environment has been studied using simulation including hospitals, extended care, rehabilitation, specialty care, long-term care, public health, among others. Frequent problems are patient flow, staffing, works schedules, facilities capacity and design, admissions/scheduling, appointments, logistics, and planning. Health care problems are especially complicated by the fact that “people serve people,” meaning people are both the customer and the supply. The customers arrive through a complex decision process that produces uncertain demand. The response is an even more complex organization of health care resources, each of which play a distinctive and overlapping role, providing a unique simulation challenge.}, booktitle={Proceedings of the 2011 winter simulation conference (wsc)}, author={Roberts, S. D.}, year={2011}, pages={1403–1414} }
 @article{yaesoubi_roberts_2010, title={A game-theoretic framework for estimating a health purchaser's willingness-to-pay for health and for expansion}, volume={13}, ISSN={["1572-9389"]}, DOI={10.1007/s10729-010-9135-6}, abstractNote={A health purchaser's willingness-to-pay (WTP) for health is defined as the amount of money the health purchaser (e.g. a health maximizing public agency or a profit maximizing health insurer) is willing to spend for an additional unit of health. In this paper, we propose a game-theoretic framework for estimating a health purchaser's WTP for health in markets where the health purchaser offers a menu of medical interventions, and each individual in the population selects the intervention that maximizes her prospect. We discuss how the WTP for health can be employed to determine medical guidelines, and to price new medical technologies, such that the health purchaser is willing to implement them. The framework further introduces a measure for WTP for expansion, defined as the amount of money the health purchaser is willing to pay per person in the population served by the health provider to increase the consumption level of the intervention by one percent without changing the intervention price. This measure can be employed to find how much to invest in expanding a medical program through opening new facilities, advertising, etc. Applying the proposed framework to colorectal cancer screening tests, we estimate the WTP for health and the WTP for expansion of colorectal cancer screening tests for the 2005 US population.}, number={4}, journal={HEALTH CARE MANAGEMENT SCIENCE}, author={Yaesoubi, Reza and Roberts, Stephen D.}, year={2010}, month={Dec}, pages={358–377} }
 @article{yarmand_ivy_roberts_bengtson_bengtson_2010, title={COST-EFFECTIVENESS ANALYSIS OF VACCINATION AND SELF-ISOLATION IN CASE OF H1N1}, ISSN={["0891-7736"]}, DOI={10.1109/wsc.2010.5678918}, abstractNote={In this research, we have conducted a cost-effectiveness analysis to examine the relative importance of vaccination and self-isolation, with respect to the current H1N1 outbreak. We have developed a continuous-time simulation model for the spread of H1N1 which allows for three types of interventions: antiviral prophylaxis and treatment, vaccination, and self-isolation and mandatory quarantine. The optimization model consists of two decision variables: vaccination fraction and self-isolation fraction among infectives. By considering the relative marginal costs associated with each of these decision variables, we have a linear objective function representing the total relative cost for each control policy. We have also considered upper bound constraints for maximum number of individuals under treatment (which is related to surge capacity) and percentage of infected individuals (which determines the attack rate). We have used grid search to obtain insight into the model, find the feasible region, and conduct the cost-effectiveness analysis.}, journal={PROCEEDINGS OF THE 2010 WINTER SIMULATION CONFERENCE}, author={Yarmand, Hamed and Ivy, Julie S. and Roberts, Stephen D. and Bengtson, Mary W. and Bengtson, Neal M.}, year={2010}, pages={2199–2210} }
 @article{carr_roberts_2010, title={PLANNING FOR INFECTIOUS DISEASE OUTBREAKS: A GEOGRAPHIC DISEASE SPREAD, CLINIC LOCATION, AND RESOURCE ALLOCATION SIMULATION}, ISSN={["0891-7736"]}, DOI={10.1109/wsc.2010.5678858}, abstractNote={In the event of an outbreak of a highly contagious communicable disease, public health departments often open mass-vaccination or antiviral dispensing clinics to treat the infected population or reduce the further spread of disease. In this research, we have created a simulation of the disease spread process employing a SEIR compartmental model. The model includes employment patterns and separates the population into age groups and spatial location to more accurately describe disease spread behavior. The analysis involves measuring health-related performance as we change the number of days elapsing between clinic days. We open clinics in locations that maximize the infected population coverage subject to budget and resource-related constraints, using a MIP location-allocation model. An example case is provided in the context of an outbreak occurring in Wake County, NC. The simulation is coded in C++, using ILOG Concert Technology to implement the location-allocation model.}, journal={PROCEEDINGS OF THE 2010 WINTER SIMULATION CONFERENCE}, author={Carr, Sean and Roberts, Stephen}, year={2010}, pages={2171–2184} }
 @article{tafazzoli_roberts_klein_ness_dittus_2009, title={Probabilistic Cost-Effectiveness Comparison of Screening Strategies for Colorectal Cancer}, volume={19}, ISSN={["1558-1195"]}, DOI={10.1145/1502787.1502789}, abstractNote={A stochastic discrete-event simulation model of the natural history of Colorectal Cancer (CRC) is augmented with screening technology representations to create a base for simulating various screening strategies for CRC. The CRC screening strategies recommended by the American Gastroenterological Association (AGA) and the newest screening strategies for which clinical efficacy has been established are simulated. In addition to verification steps, validation of screening is pursued by comparison with the Minnesota Colon Cancer Control Study. The model accumulates discounted costs and quality-adjusted life-years. The natural variability in the modeled random variables for natural history is conditioned using a probabilistic sensitivity analysis through a two-stage sampling process that adds other random variables representing parametric uncertainty. The analysis of the screening alternatives in a low-risk population explores both deterministic and stochastic dominance to eliminate some screening alternatives. Net benefit analysis, based on willingness to pay for quality-adjusted life-years, is used to compare the most cost-effective strategies through acceptability curves and to make a screening recommendation. Methodologically, this work demonstrates how variability from the natural variation in the development, screening, and treatment of a disease can be combined with the variation in parameter uncertainty. Furthermore, a net benefit analysis that characterizes cost-effectiveness alternatives can explicitly depend on variation from all sources producing a probabilistic cost-effectiveness analysis of decision alternatives.}, number={2}, journal={ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION}, author={Tafazzoli, Ali and Roberts, Stephen and Klein, Robert and Ness, Reid and Dittus, Robert}, year={2009}, month={Mar} }
 @article{roberts_wang_klein_ness_dittus_2008, title={Development of a simulation model of colorectal cancer}, volume={18}, ISSN={["1558-1195"]}, DOI={10.1145/1315575.1315579}, abstractNote={Colorectal cancer (CRC) is deadly if not found early. Any protocols developed for screening and surveillance and any policy decisions regarding the availability of CRC resources should consider the nature of the disease and its impact over time on costs and quality-adjusted life years in a population. Simulation models can provide a flexible representation needed for such analysis; however, the development of a credible simulation model of the natural history of CRC is hindered by limited data and incomplete knowledge. To accommodate the extensive modeling and remodeling required to produce a credible model, we created an object-oriented simulation platform driven by a model-independent database within the .NET environment. The object-oriented structure not only encapsulated the needs of a simulation replication but created an extensible framework for specialization of the CRC components. This robust framework allowed development to focus modeling on the CRC events and their event relationships, conveniently facilitating extensive revision during model construction. As a second-generation CRC modeling activity, this model development benefited from prior experience with data sources and modeling difficulties. A graphical user interface makes the model accessible by displaying existing scenarios, showing input variables and their values, and permitting the creation of new scenarios and changes to its input. Output from the simulation is captured in familiar tabbed worksheets and stored in the database. The eventual CRC model was conceptualized through a series of assumptions that conformed to beliefs and data regarding the natural history of CRC. Throughout the development cycle, extensive verification and validation calibrated the model. The result is a simulation model that characterizes the natural history of CRC with sufficient accuracy to provide an effective means of evaluating numerous issues regarding the burden of this disease on individuals and society. Generalizations from this study are offered regarding the use of discrete-event simulation in disease modeling and medical decision making.}, number={1}, journal={ACM TRANSACTIONS ON MODELING AND COMPUTER SIMULATION}, author={Roberts, Stephen and Wang, Lijun and Klein, Robert and Ness, Reid and Dittus, Robert}, year={2008} }
 @article{yaesoubi_roberts_2008, title={HOW MUCH IS A HEALTH INSURER WILLING TO PAY FOR COLORECTAL CANCER SCREENING TESTS?}, ISBN={["978-1-4244-2707-9"]}, DOI={10.1109/wsc.2008.4736246}, abstractNote={Colorectal Cancer (CRC) screening tests have proven to be cost-effective in preventing cancer incidence. Yet, as recent studies have shown, CRC screening tests are noticeably underutilized. Among the factors influencing CRC screening test utilization, the role of health insurers has gained considerable attention in recent studies. In this paper, we propose an analytical model for the market of CRC screening tests and show how the insurer can benefit from a computer simulation model to cope with the problem of incomplete and asymmetric information inherent in this market. Our estimates reveal that promoting CRC screening tests is not necessarily economically attractive to the insurer, unless the insurer¿s valuation of life is greater than a certain limit. We use the proposed model to estimate such a threshold - the insurer¿s willingness-to-pay to acquire one additional life year by covering the CRC screening tests.}, journal={2008 WINTER SIMULATION CONFERENCE, VOLS 1-5}, author={Yaesoubi, Reza and Roberts, Stephen D.}, year={2008}, pages={1624–1631} }
 @article{melton_culbreth_joines_roberts_2002, title={Design and analysis of furniture finishing systems}, volume={52}, number={7-8}, journal={Forest Products Journal}, author={Melton, R. and Culbreth, C. T. and Joines, J. A. and Roberts, S. D.}, year={2002}, pages={27–33} }
 @article{melton_culbreth_roberts_joines_2002, title={Design and evaluation of furniture finishing systems}, volume={52}, number={7-8}, journal={Forest Products Journal}, author={Melton, R. H. and Culbreth, C. T. and Roberts, S. D. and Joines, J. A.}, year={2002}, pages={27–33} }