Return to search

FORECASTING THE WORKLOAD WITH A HYBRID MODEL TO REDUCE THE INEFFICIENCY COST

Time series forecasting and modeling are challenging problems during the past decades, because of its plenty of properties and underlying correlated relationships. As a result, researchers proposed a lot of models to deal with the time series. However, the proposed models such as Autoregressive integrated moving average (ARIMA) and artificial neural networks (ANNs) only describe part of the properties of time series. In this thesis, we introduce a new hybrid model integrated filter structure to improve the prediction accuracy. Case studies with real data from University of Kentucky HealthCare are carried out to examine the superiority of our model. Also, we applied our model to operating room (OR) to reduce the inefficiency cost. The experiment results indicate that our model always outperforms compared with other models in different conditions.

Identiferoai:union.ndltd.org:uky.edu/oai:uknowledge.uky.edu:me_etds-1093
Date01 January 2017
CreatorsPan, Xinwei
PublisherUKnowledge
Source SetsUniversity of Kentucky
Detected LanguageEnglish
Typetext
Formatapplication/pdf
SourceTheses and Dissertations--Mechanical Engineering

Page generated in 0.0015 seconds