Assessing the queuing process using data envelopment analysis:an application in health centres

Queuing is one of the very important criteria for assessing the performance and efficiency of any service industry, including healthcare. Data Envelopment Analysis (DEA) is one of the most widely-used techniques for performance measurement in healthcare. However, no queue management application has been reported in the health-related DEA literature. Most of the studies regarding patient flow systems had the objective of improving an already existing Appointment System. The current study presents a novel application of DEA for assessing the queuing process at an Outpatients’ department of a large public hospital in a developing country where appointment systems do not exist. The main aim of the current study is to demonstrate the usefulness of DEA modelling in the evaluation of a queue system. The patient flow pathway considered for this study consists of two stages; consultation with a doctor and pharmacy. The DEA results indicated that waiting times and other related queuing variables included need considerable minimisation at both stages

An advanced approach to forecasting tourism demand in Taiwan

Forecasting has been considered important in a service industry. Many techniques have been applied to improve forecasting results. This study intends to apply a neural network based fuzzy time series model to forecast the international tourist numbers arriving in Taiwan. Neural network is good at handling nonlinear data. On the other hand, the fuzzy time series models have been applied to time series problems in various domains and have been shown to outperform some conventional models. The tourist numbers arriving in Taiwan show a nonlinear characteristic with a structural break. And the application of the neural network based fuzzy time series model is expected to outperform some other models in forecasting these tourist numbers

Forecasting tourism demand by fuzzy time series models

PurposeThis study aims to adapt a neural network based fuzzy time series model to improve Taiwan's tourism demand forecasting.Design/methodology/approachFuzzy sets are for modeling imprecise data and neural networks are for establishing non‐linear relationships among fuzzy sets. A neural network based fuzzy time series model is adapted as the forecasting model. Both in‐sample estimation and out‐of‐sample forecasting are performed.FindingsThis study outperforms previous studies undertaken during the SARS events of 2002‐2003.Research limitations/implicationsThe forecasting model only takes the observation of one previous time period into consideration. Subsequent studies can extend the model to consider previous time periods by establishing fuzzy relationships.Originality/valueNon‐linear data is complicated to forecast, and it is even more difficult to forecast nonlinear data with shocks. The forecasting model in this study outperforms other studies in forecasting the nonlinear tourism demands during the SARS event of November 2002 to June 2003.</jats:sec

Modeling and forecasting tourism demand: the case of flows from Mainland China to Taiwan

Real effective exchange rate, Neural networks, Prices, Tourism demand,