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Simulation Winter Conference

作者： David Cornejo Maria E. Mayorga Kristen Hassmiller Lich Operations Research Graduate Program North Carolina State University Raleigh NC USA Department of Industrial and Systems Engineering North Carolina State University Raleigh NC USA Gillings School of Global Public Health The University of North Carolina Chapel Hill NC USA

The research direction in modeling complex, chronic conditions for health policy evaluation has been to incorporate individual heterogeneity. This detail makes our models more powerful and relevant. In implementing an individual simulation model of colorectal cancer we have recognized two considerations related to incorporating individual heterogeneity that have not been adequately discussed in the literature. First, there are substantial computational gains and interpretation benefits if an individual's life courses are identical except when differences are directly induced by an intervention. Achieving this is not trivial. We have developed the notion of a “common patient” who is the same between scenarios except for intervention-induced changes. We create the common patient using a careful application of Common Random Numbers (CRN). Second, when we model differentiated individuals we can examine differing impacts of polices on specific sub-groups. This leverages the detail in individual attributes to produce useful results for policy makers.

关键词： Cancer Sociology Statistics Synchronization Analytical models Diseases

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Modeling loss of coolant accident frequencies and break sizes

Modeling loss of coolant accident frequencies and break size...

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2013 Winter Meeting on Transactions and Embedded Topical Meetings: Risk Management for Complex Socio-Technical Systems, 2nd ANS SMR 2013 Conference, Nuclear Nonproliferation - 1st Fission to the Future

作者： Morton, David P. Pan, Ying-An Tejada, Jeremy J. Graduate Program in Operations Research and Industrial Engineering University of Texas at Austin 1 University Station C2200 Austin TX 78712 United States

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Designing American Government

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Design Management Review 2016年第1期27卷

作者： John Paul Farmer Ryan Panchadsaram Director of technology and civic innovation at Microsoft and founder/CEO of The Innovation Project a 501(c)3 dedicated to spreading best practices in institutional innovation. He served as senior advisor for innovation in the White House where he spearheaded government innovation initiatives that infused lean startup and agile methods as well as design thinking into the public sector. In this role Farmer co‐founded and directed the Presidential Innovation Fellows program. Earlier in his career he worked in healthcare and finance creating and building new business units from the ground up. Farmer also played professional baseball as a shortstop in the Los Angeles Dodgers and Atlanta Braves minor league systems. He holds an MBA with honors from the Graduate School of Business at Columbia University and an AB with honors from Harvard University. Currently an entrepreneur in residence at venture capital firm Kleiner Perkins Caufield Byers. Formerly he was the deputy chief technology officer for the United States at the White House. Panchadsaram helped shape how an $80 billion budget can be used by federal agencies to deliver on their missions in a more effective design‐centric and data‐driven way. In 2014 Time magazine featured him on its cover as part of the crisis response team tasked with rescuing the rollout of Healthcare.gov. In response to that experience he helped found the US Digital Service—a “startup” within the White House that recruits the country's top technical talent to work on the government's most critical citizen‐facing services. Panchadsaram is an active designer and coder and held various product roles at Microsoft and ***. He holds a degree from the University of California Berkeley in industrial engineering and operations research.

Design is making inroads into the workings of government agencies within the DC Beltway and beyond. Civic‐minded designers take note. An overview of the design/government ecosystem in Washington, DC.

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Application of stochastic optimization to nuclear power plant asset management decisions

Application of stochastic optimization to nuclear power plan...

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International Conference on Mathematics and Computational Methods Applied to Nuclear Science and engineering, M and C 2013

作者： Morton, David Koç, Ali Hess, Stephen M. Graduate Program in Operations Research and Industrial Engineering University of Texas at Austin Austin TX 78712 United States IBM T.J. Watson Research Center Business Analytics and Mathematical Sciences Dept. 1101 Kitchawan Rd. Yorktown Heights NY 10598 United States Electric Power Research Institute 300 Baywood Road West Chester PA 19382 United States

ISBN: (纸本)9781627486439

We describe the development and application of stochastic optimization models and algorithms to address an issue of critical importance in the strategic allocation of resources;namely, the selection of a portfolio of capital investment projects under the constraints of a limited and uncertain budget. This issue is significant and one that faces decision-makers across all industries. The objective of this strategic decision process is generally self evident - to maximize the value obtained from the portfolio of selected projects (with value usually measured in terms of the portfolio's net present value). However, heretofore, many organizations have developed processes to make these investment decisions using simplistic rule-based rank-ordering schemes. This approach has the significant limitation of not accounting for the (often large) uncertainties in the costs or economic benefits associated with the candidate projects or in the uncertainties in the actual funds available to be expended over the projected period of time. As a result, the simple heuristic approaches that typically are employed in industrial practice generate outcomes that are non-optimal and do not achieve the level of benefits intended. In this paper we describe the results of research performed to utilize stochastic optimization models and algorithms to address this limitation by explicitly incorporating the evaluation of uncertainties in the analysis and decisionmaking process.

关键词： Nuclear power plants

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Simulation-based analysis of scheduling decisions in an outpatient clinic

Simulation-based analysis of scheduling decisions in an outp...

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IIE Annual Conference and Expo 2013

作者： Akin, Gokce Ivy, Julie S. Huschka, Todd R. Rohleder, Thomas R. Graduate Program in Operations Research North Carolina State University Raleigh NC 27695 United States Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University Raleigh NC 27695 United States Department of Health Care Policy and Research Mayo Clinic 200 1st ST SW Rochester MN 55905 United States

ISBN: (纸本)9780983762423

In an outpatient clinic, in which multiple classes of patients are given appointments, capacity management and scheduling decisions are particularly important. Once an appointment is scheduled;it is not uncommon for the appointment to be rescheduled or cancelled prior to the appointment day, or a patient may simply not show-up on his/her appointment day. Thus it is crucial to consider all of these patient behaviors when assigning an appointment. In this study we consider multiple classes of patients with different demand rates, service times, and different delay-dependent reschedule, cancellation, and no-show rates. We develop a simulation model to analyze the effects of reducing the appointment planning horizon. In our experimental analysis we also evaluate different calendar structures: traditional slots, standardized 20 or 30 minute slots, and slotless designs. We use capacity utilization and "seen" patient proportions as the performance indicators in our model. Our results indicate that we can significantly increase physician utilizations and the percentage of seen patients by reducing the appointment planning horizon. We also observe improvements in the performance indicators if we use a slotless design or standardized appointments with shorter slot lengths.

关键词： Scheduling

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Risk-informed resolution of Generic Safety Issue 191

Risk-informed resolution of Generic Safety Issue 191

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International Topical Meeting on Probabilistic Safety Assessment and Analysis 2013, PSA 2013

作者： Mohaghegh, Zahra Kee, Ernie Reihani, Seyed A. Kazemi, Reza Johnson, David Grantom, Rick Fleming, Karl Sande, Tim Letellier, Bruce Zigler, Gilbert Morton, David Tejada, Jeremy Howe, Kerry Leavitt, Janet Hassan, Yassin A. Vaghetto, Rodolfo Lee, Saya Blossom, Steve Department of Nuclear Plasma and Radiological Engineering University of Illinois at Urbana-Champaign United States South Texas Project Nuclear Operating Company United States Soteria Consultants LLC United States ABS Consulting United States Knf Consulting Services LLC United States Enercon Services Inc. Germany Alion Science and Technology United States Graduate Program in Operations Research and Industrial Engineering University of Texas at Austin United States Civil Engineering Department University of New Mexico United States Department of Nuclear Engineering Texas A and M University United States

ISBN: (纸本)9781629938172

The United States Nuclear Regulatory Commission (USNRC) relies on Probabilistic Risk Assessment (PRA) as one of the main pillars of its risk-informed regulatory and oversight functions. In 2011, the South Texas Project Nuclear Operating Company (STPNOC) initiated a risk-informed project to resolve Generic Safety Issue 191 (GSI-191), which is related to the performance of the emergency core cooling system (ECCS) following a loss of coolant accident (LOCA). This paper summarizes the results of collaborative work on this project by teams of experts from industry and academia. The progress of the research and its implementation have also significantly benefitted from the discussions and feedback received from regulatory representatives. An integrative framework was developed to explicitly provide failure probabilities for the post-LOCA PRA basic events associated with the concerns raised in GSI-191. These basic event probabilities are estimated using the CASA Grande program, which was developed as part of the project. This program encompasses the time-dependent modeling of the underlying physical phenomena of the basic events and the propagation of the uncertainties in the physical models. This paper summarizes the elements of the integrative framework including PRA and the CASA Grande program, as well as the input parameters, assumptions, methodology, and results of the STPNOC analysis. The results show that the risk of core damage or large early release related to the concerns raised in GSI-191 in the as-built, as-operated design for STPNOC is very small (as defined in Regulatory Guide 1.174).

关键词： Loss of coolant accidents

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Monthly clinic assignments for internal medicine housestaff

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IIE Transactions on Healthcare Systems engineering 2013年第4期3卷 207-239页

作者： Bard, Jonathan F. Shu, Zhichao Leykum, Luci Graduate Program in Operations Research and Industrial Engineering The University of Texas Austin TX 78712 United States School of Medicine The University of Texas Health Science Center San Antonio TX 78229 United States

This article presents a new model for constructing monthly clinic schedules for interns and residents (i.e., housestaff) training in Internal Medicine. Clinical experiences during the three years of residency occur in inpatient and outpatient settings, and on generalist and specialist clinical services. These experiences include spending time in a primary care setting caring for an assigned group of patients over time. Housestaff rotate through different clinical experiences monthly, with their primary care clinic time overlaid longitudinally on these other clinical services. The exact amount of primary care time spent varies between clinical rotations. In fact, it is the variable clinic hour requirements that drive the scheduling process, and is what distinguishes our problem from most personnel scheduling problems. Typically, staff schedules are driven by shift or hourly demand and are designed to minimize some measure of cost. The objective in our work is to both maximize clinic utilization and minimize the number of violations of a prioritized set of goals while ensuring that certain clinic-level and individual constraints are satisfied. The corresponding problem is formulated as an integer goal program in which several of the hard constraints are temporarily allowed to be violated to avoid infeasibility. To find solutions, a three-phase methodology is proposed. In the first phase (pre-processing step), clinic assignments for a subset of the housestaff are either fixed or excluded each month in light of restrictions imposed by their current rotation. In the second phase, tentative solutions are obtained with a commercial solver. In the final phase (post-processing step), all violations of the relaxed hard constraints are removed and an attempt is made to lexicographically reduce violations of the major goals. The effectiveness of the methodology is demonstrated by analyzing eight monthly rosters provided by the Internal Medicine Residency program at the Universi

关键词： goal programming internal medicine mixed-integer programming Resident scheduling soft constraints

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Capacity management and patient scheduling in an outpatient clinic using discrete event simulation 13

Capacity management and patient scheduling in an outpatient ...

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Simulation Winter Conference

作者： Gokce Akin Julie S. Ivy Todd R. Huschka Thomas R. Rohleder Yariv N. Marmor Graduate Program in Operations Research North Carolina State University Raleigh NC USA Edward P. Fitts Department of Industrial and Systems Engineering North Carolina State University Raleigh NC USA Department of Health Care Policy and Research Mayo Clinic Rochester MN USA

ISBN: (纸本)9781479920778

Capacity management and scheduling decisions are important for managing an outpatient clinic in which multiple classes of patients are treated. After an appointment is scheduled, it can be rescheduled, cancelled, or a patient may not show-up on their appointment day. This study simulates the behavior of patients with regard to the time to appointment, examining different demand rates and service times for each patient class (new external patients, internal patients, established patients and subsequent visit patients); we also consider different delay-dependent reschedule, cancellation, and no-show rates. A discrete event simulation model is developed to analyze the effects of allowing different appointment windows, i.e., the maximum time between the appointment request date and the actual appointment date, for different patient classes. Capacity utilization, patient access, and financial rewards are used as the performance indicators.

关键词： Analytical models Delays Medical services Calendars Mathematical model Data models Data analysis

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Compensability of errors in product quality in multistage manufacturing processes

Compensability of errors in product quality in multistage ma...

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作者： Jiao, Yibo Djurdjanovic, Dragan Department of Mechanical Engineering Graduate Program in Operations Research and Industrial Engineering University of Texas at Austin 1 University Station C2200 Austin TX 78712-0292 United States Department of Mechanical Engineering Advanced Manufacturing Research Center University of Texas at Austin 1 University Station C2200 Austin TX 78712-0292 United States

Recent advances in model-based feedforward control of quality in multistage manufacturing processes (MMPs) are used to introduce the concept of compensability of errors in product quality. It is defined as the system-level ability to automatically eliminate quality errors accumulated in the product, given the allocation of measurements and controllable tooling. Significant similarities are observed with the control theory concepts of controllability and observability. In addition, just like in control theory, it is shown that relatively simple matrix rank tests can be used to determine the compensability of a MMP and are related with the previously introduced notion of diagnosability. The theory introduced in this paper enables rapid quantitative analysis of one's ability to actively control the quality in a manufacturing system, which is crucial for optimal allocation of measurements and controllable tooling in an MMP. Compensability studies based on the newly introduced methods are conducted using models of the flow of errors in product quality in two automotive cylinder head machining lines and a multi-layer semiconductor lithography process.

关键词： Errors

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A combined deterministic and sampling-based sequential bounding method for stochastic programming 11

A combined deterministic and sampling-based sequential bound...

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Simulation Winter Conference

作者： Péguy Pierre-Louis Güzin Bayraksan David P. Morton Department of Systems and Industrial Engineering University of Arizona Tucson Tucson AZ USA Graduate Program in Operations Research University of Texas Austin Austin TX USA

We develop an algorithm for two-stage stochastic programming with a convex second stage program and with uncertainty in the right-hand side. The algorithm draws on techniques from bounding and approximation methods as well as sampling-based approaches. In particular, we sequentially refine a partition of the support of the random vector and, through Jensen's inequality, generate deterministically valid lower bounds on the optimal objective function value. An upper bound estimator is formed through a stratified Monte Carlo sampling procedure that includes the use of a control variate variance reduction scheme. The algorithm lends itself to a stopping rule theory that ensures an asymptotically valid confidence interval for the quality of the proposed solution. Computational results illustrate our approach.

关键词： Approximation algorithms Partitioning algorithms Monte Carlo methods Upper bound Linear approximation Vectors

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