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Browsing Engineering by Subject "Industrial engineering"
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Item Crowding Reduction And Waiting Time Analysis In Health-Care System Using Machine Learning(2022-05-20) Hijry, Hassan Mohmmed; Olawoyin, Richard; McDonald, Gary; Edward, William; Debnath, DebatoshIn the hospital setting, the emergency room (ER) offers timely emergency care for patients and is considered the busiest department because of the urgency of cases. Emergency rooms have the highest number of patients overcrowding within any hospital; more than 50% of the patients admitted to the hospital come through the ER. Healthcare management is continuously trying to minimize wait times and optimize the hospital's allocated resources, but most ERs still suffer from the overcrowding crisis due to the stochastic arrival and random arrival distribution. Advanced techniques, such as machine learning algorithms, are useful for determining real life queue scenarios and patient flow (e.g., waiting time in queue and length of stay), which are considered measures of ER overcrowding. As such, we began by building a model to predict patient length of stay through predictive input factors such as patient age, mode of arrival, and patientÕs type of condition using three machine learning algorithms (e.g., artificial neural networks (ANN), linear regression, and logistic regression). The best model accuracy ANN resulted in an increase of 19.5% compared to the performance from previous studies. Then, the Deep Learning Model was applied for historical queueing variables to vi predict patient waiting time in a system alongside, or in place of, queueing theory (QT). Four optimization algorithms (SGD, Adam, RMSprop, and AdaGrade) were applied and compared to find the best model with the lowest mean absolute error. The results showed that the SGD algorithm achieved better prediction accuracy than the traditional approach and reduced the use of assumptions. Moreover, the model decreased the error reduction by 24% when compared to prior literature. Lastly, we proposed a model to predict the patient waiting time based on the lab test results. Multi-algorithms were implemented by using real-life COVID-19 test results data recorded during the pandemic. Among the eight proposed models, the results showed that decision tree regression performed better for predicting waiting times. Based on experiments performed in the research, this dissertation provides a guideline for waiting time analysis in the queue--not only in healthcare, but also in other sectors, considering model understandability and the feature extraction process.Item Stochastic Planning And Scheduling For Reconfigurable Job Shops And Flow Lines(2022-11-11) Imseitif, Jad Taysir; Nezamoddini, Nasim; Aydas, Osman; Pandey, Vijitashwa; Cheok, Ka CThe uncertain and competitive market is leading manufacturers to look for fast and effective technological solutions to manage their production systems and make them highly responsive to market needs. Moreover, customers are requesting customized, high-quality products quickly and at low costs. Utilizing rigid manufacturing systems such as dedicated manufacturing systems (DMSs) or flexible manufacturing systems (FMSs) limits manufacturers’ responsiveness. Reconfigurable manufacturing systems (RMSs) were introduced to cope with these challenges. These systems are built around modularity and reconfigurability and use reconfigurable machine tools (RMTs) as their main component. The adjustable structure of RMT allows the system to adapt to market requirements. However, production management in RMSs is a particularly challenging task compared to traditional systems, which makes manufacturers skeptical about adopting these systems. To address this issue, this dissertation presents novel methodologies to manage production activities within RMSs regarding planning, scheduling, and control. The research was conducted in two main parts based on the system type (i.e., job shop or flow line). A novel mixed-integer linear programming (MILP) model for planning and scheduling is formulated for the former. Then, it was extended to a two-stage stochastic (TSS) formulation to incorporate the uncertainties in volume and machines’ productivity. A data-driven controller with predictive capabilities was developed for the latter. It collects real-time data to reschedule raw material injection time and control the inner-stage movement of work-in-process (WIP) units to optimize their levels. The applicability of the proposed models was validated using case studies adopted from the literature. The result of this dissertation showed the cost-benefits of utilizing RMSs and the effectiveness of adopting the proposed methodologies to manage RMSs.