Matching Items (6)
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- All Subjects: Operations Research
- Creators: Li, Jing
Description
In a healthcare setting, the Sterile Processing Department (SPD) provides ancillary services to the Operating Room (OR), Emergency Room, Labor & Delivery, and off-site clinics. SPD's function is to reprocess reusable surgical instruments and return them to their home departments. The management of surgical instruments and medical devices can impact patient safety and hospital revenue. Any time instrumentation or devices are not available or are not fit for use, patient safety and revenue can be negatively impacted. One step of the instrument reprocessing cycle is sterilization. Steam sterilization is the sterilization method used for the majority of surgical instruments and is preferred to immediate use steam sterilization (IUSS) because terminally sterilized items can be stored until needed. IUSS Items must be used promptly and cannot be stored for later use. IUSS is intended for emergency situations and not as regular course of action. Unfortunately, IUSS is used to compensate for inadequate inventory levels, scheduling conflicts, and miscommunications. If IUSS is viewed as an adverse event, then monitoring IUSS incidences can help healthcare organizations meet patient safety goals and financial goals along with aiding in process improvement efforts. This work recommends statistical process control methods to IUSS incidents and illustrates the use of control charts for IUSS occurrences through a case study and analysis of the control charts for data from a health care provider. Furthermore, this work considers the application of data mining methods to IUSS occurrences and presents a representative example of data mining to the IUSS occurrences. This extends the application of statistical process control and data mining in healthcare applications.
ContributorsWeart, Gail (Author) / Runger, George C. (Thesis advisor) / Li, Jing (Committee member) / Shunk, Dan (Committee member) / Arizona State University (Publisher)
Created2014
Description
Every year, more than 11 million maritime containers and 11 million commercial trucks arrive to the United States, carrying all types of imported goods. As it would be costly to inspect every container, only a fraction of them are inspected before being allowed to proceed into the United States. This dissertation proposes a decision support system that aims to allocate the scarce inspection resources at a land POE (L-POE), to minimize the different costs associated with the inspection process, including those associated with delaying the entry of legitimate imports. Given the ubiquity of sensors in all aspects of the supply chain, it is necessary to have automated decision systems that incorporate the information provided by these sensors and other possible channels into the inspection planning process. The inspection planning system proposed in this dissertation decomposes the inspection effort allocation process into two phases: Primary and detailed inspection planning. The former helps decide what to inspect, and the latter how to conduct the inspections. A multi-objective optimization (MOO) model is developed for primary inspection planning. This model tries to balance the costs of conducting inspections, direct and expected, and the waiting time of the trucks. The resulting model is exploited in two different ways: One is to construct a complete or a partial efficient frontier for the MOO model with diversity of Pareto-optimal solutions maximized; the other is to evaluate a given inspection plan and provide possible suggestions for improvement. The methodologies are described in detail and case studies provided. The case studies show that this MOO based primary planning model can effectively pick out the non-conforming trucks to inspect, while balancing the costs and waiting time.
ContributorsXue, Liangjie (Author) / Villalobos, Jesus René (Thesis advisor) / Gel, Esma (Committee member) / Runger, George C. (Committee member) / Maltz, Arnold (Committee member) / Arizona State University (Publisher)
Created2012
Description
Functional or dynamic responses are prevalent in experiments in the fields of engineering, medicine, and the sciences, but proposals for optimal designs are still sparse for this type of response. Experiments with dynamic responses result in multiple responses taken over a spectrum variable, so the design matrix for a dynamic response have more complicated structures. In the literature, the optimal design problem for some functional responses has been solved using genetic algorithm (GA) and approximate design methods. The goal of this dissertation is to develop fast computer algorithms for calculating exact D-optimal designs.
First, we demonstrated how the traditional exchange methods could be improved to generate a computationally efficient algorithm for finding G-optimal designs. The proposed two-stage algorithm, which is called the cCEA, uses a clustering-based approach to restrict the set of possible candidates for PEA, and then improves the G-efficiency using CEA.
The second major contribution of this dissertation is the development of fast algorithms for constructing D-optimal designs that determine the optimal sequence of stimuli in fMRI studies. The update formula for the determinant of the information matrix was improved by exploiting the sparseness of the information matrix, leading to faster computation times. The proposed algorithm outperforms genetic algorithm with respect to computational efficiency and D-efficiency.
The third contribution is a study of optimal experimental designs for more general functional response models. First, the B-spline system is proposed to be used as the non-parametric smoother of response function and an algorithm is developed to determine D-optimal sampling points of a spectrum variable. Second, we proposed a two-step algorithm for finding the optimal design for both sampling points and experimental settings. In the first step, the matrix of experimental settings is held fixed while the algorithm optimizes the determinant of the information matrix for a mixed effects model to find the optimal sampling times. In the second step, the optimal sampling times obtained from the first step is held fixed while the algorithm iterates on the information matrix to find the optimal experimental settings. The designs constructed by this approach yield superior performance over other designs found in literature.
First, we demonstrated how the traditional exchange methods could be improved to generate a computationally efficient algorithm for finding G-optimal designs. The proposed two-stage algorithm, which is called the cCEA, uses a clustering-based approach to restrict the set of possible candidates for PEA, and then improves the G-efficiency using CEA.
The second major contribution of this dissertation is the development of fast algorithms for constructing D-optimal designs that determine the optimal sequence of stimuli in fMRI studies. The update formula for the determinant of the information matrix was improved by exploiting the sparseness of the information matrix, leading to faster computation times. The proposed algorithm outperforms genetic algorithm with respect to computational efficiency and D-efficiency.
The third contribution is a study of optimal experimental designs for more general functional response models. First, the B-spline system is proposed to be used as the non-parametric smoother of response function and an algorithm is developed to determine D-optimal sampling points of a spectrum variable. Second, we proposed a two-step algorithm for finding the optimal design for both sampling points and experimental settings. In the first step, the matrix of experimental settings is held fixed while the algorithm optimizes the determinant of the information matrix for a mixed effects model to find the optimal sampling times. In the second step, the optimal sampling times obtained from the first step is held fixed while the algorithm iterates on the information matrix to find the optimal experimental settings. The designs constructed by this approach yield superior performance over other designs found in literature.
ContributorsSaleh, Moein (Author) / Pan, Rong (Thesis advisor) / Montgomery, Douglas C. (Committee member) / Runger, George C. (Committee member) / Kao, Ming-Hung (Committee member) / Arizona State University (Publisher)
Created2015
Description
Mathematical modeling and decision-making within the healthcare industry have given means to quantitatively evaluate the impact of decisions into diagnosis, screening, and treatment of diseases. In this work, we look into a specific, yet very important disease, the Alzheimer. In the United States, Alzheimer’s Disease (AD) is the 6th leading cause of death. Diagnosis of AD cannot be confidently confirmed until after death. This has prompted the importance of early diagnosis of AD, based upon symptoms of cognitive decline. A symptom of early cognitive decline and indicator of AD is Mild Cognitive Impairment (MCI). In addition to this qualitative test, Biomarker tests have been proposed in the medical field including p-Tau, FDG-PET, and hippocampal. These tests can be administered to patients as early detectors of AD thus improving patients’ life quality and potentially reducing the costs of the health structure. Preliminary work has been conducted in the development of a Sequential Tree Based Classifier (STC), which helps medical providers predict if a patient will contract AD or not, by sequentially testing these biomarker tests. The STC model, however, has its limitations and the need for a more complex, robust model is needed. In fact, STC assumes a general linear model as the status of the patient based upon the tests results. We take a simulation perspective and try to define a more complex model that represents the patient evolution in time.
Specifically, this thesis focuses on the formulation of a Markov Chain model that is complex and robust. This Markov Chain model emulates the evolution of MCI patients based upon doctor visits and the sequential administration of biomarker tests. Data provided to create this Markov Chain model were collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The data lacked detailed information of the sequential administration of the biomarker tests and therefore, different analytical approaches were tried and conducted in order to calibrate the model. The resulting Markov Chain model provided the capability to conduct experiments regarding different parameters of the Markov Chain and yielded different results of patients that contracted AD and those that did not, leading to important insights into effect of thresholds and sequence on patient prediction capability as well as health costs reduction.
The data in this thesis was provided from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). ADNI investigators did not contribute to any analysis or writing of this thesis. A list of the ADNI investigators can be found at: http://adni.loni.usc.edu/about/governance/principal-investigators/ .
Specifically, this thesis focuses on the formulation of a Markov Chain model that is complex and robust. This Markov Chain model emulates the evolution of MCI patients based upon doctor visits and the sequential administration of biomarker tests. Data provided to create this Markov Chain model were collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The data lacked detailed information of the sequential administration of the biomarker tests and therefore, different analytical approaches were tried and conducted in order to calibrate the model. The resulting Markov Chain model provided the capability to conduct experiments regarding different parameters of the Markov Chain and yielded different results of patients that contracted AD and those that did not, leading to important insights into effect of thresholds and sequence on patient prediction capability as well as health costs reduction.
The data in this thesis was provided from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). ADNI investigators did not contribute to any analysis or writing of this thesis. A list of the ADNI investigators can be found at: http://adni.loni.usc.edu/about/governance/principal-investigators/ .
ContributorsCamarena, Raquel (Author) / Pedrielli, Giulia (Thesis advisor) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2018
Description
In today's global market, companies are facing unprecedented levels of uncertainties in supply, demand and in the economic environment. A critical issue for companies to survive increasing competition is to monitor the changing business environment and manage disturbances and changes in real time. In this dissertation, an integrated framework is proposed using simulation and online calibration methods to enable the adaptive management of large-scale complex supply chain systems. The design, implementation and verification of the integrated approach are studied in this dissertation. The research contributions are two-fold. First, this work enriches symbiotic simulation methodology by proposing a framework of simulation and advanced data fusion methods to improve simulation accuracy. Data fusion techniques optimally calibrate the simulation state/parameters by considering errors in both the simulation models and in measurements of the real-world system. Data fusion methods - Kalman Filtering, Extended Kalman Filtering, and Ensemble Kalman Filtering - are examined and discussed under varied conditions of system chaotic levels, data quality and data availability. Second, the proposed framework is developed, validated and demonstrated in `proof-of-concept' case studies on representative supply chain problems. In the case study of a simplified supply chain system, Kalman Filtering is applied to fuse simulation data and emulation data to effectively improve the accuracy of the detection of abnormalities. In the case study of the `beer game' supply chain model, the system's chaotic level is identified as a key factor to influence simulation performance and the choice of data fusion method. Ensemble Kalman Filtering is found more robust than Extended Kalman Filtering in a highly chaotic system. With appropriate tuning, the improvement of simulation accuracy is up to 80% in a chaotic system, and 60% in a stable system. In the last study, the integrated framework is applied to adaptive inventory control of a multi-echelon supply chain with non-stationary demand. It is worth pointing out that the framework proposed in this dissertation is not only useful in supply chain management, but also suitable to model other complex dynamic systems, such as healthcare delivery systems and energy consumption networks.
ContributorsWang, Shanshan (Author) / Wu, Teresa (Thesis advisor) / Fowler, John (Thesis advisor) / Pfund, Michele (Committee member) / Li, Jing (Committee member) / Pavlicek, William (Committee member) / Arizona State University (Publisher)
Created2010
Description
With the increased demand for genetically modified T-cells in treating hematological malignancies, the need for an optimized measurement policy within the current good manufacturing practices for better quality control has grown greatly. There are several steps involved in manufacturing gene therapy. These steps are for the autologous-type gene therapy, in chronological order, are harvesting T-cells from the patient, activation of the cells (thawing the cryogenically frozen cells after transport to manufacturing center), viral vector transduction, Chimeric Antigen Receptor (CAR) attachment during T-cell expansion, then infusion into patient. The need for improved measurement heuristics within the transduction and expansion portions of the manufacturing process has reached an all-time high because of the costly nature of manufacturing the product, the high cycle time (approximately 14-28 days from activation to infusion), and the risk for external contamination during manufacturing that negatively impacts patients post infusion (such as illness and death).
The main objective of this work is to investigate and improve measurement policies on the basis of quality control in the transduction/expansion bio-manufacturing processes. More specifically, this study addresses the issue of measuring yield within the transduction/expansion phases of gene therapy. To do so, it was decided to model the process as a Markov Decision Process where the decisions being made are optimally chosen to create an overall optimal measurement policy; for a set of predefined parameters.
The main objective of this work is to investigate and improve measurement policies on the basis of quality control in the transduction/expansion bio-manufacturing processes. More specifically, this study addresses the issue of measuring yield within the transduction/expansion phases of gene therapy. To do so, it was decided to model the process as a Markov Decision Process where the decisions being made are optimally chosen to create an overall optimal measurement policy; for a set of predefined parameters.
ContributorsStarkey, Michaela (Author) / Pedrielli, Giulia (Thesis advisor) / Li, Jing (Committee member) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2020