| 1. |
- Yousefi, Milad, et al.
(författare)
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Patient visit forecasting in an emergency department using a deep neural network approach
- 2020
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Ingår i: Kybernetes. - : Emerald Group Publishing Limited. - 0368-492X .- 1758-7883. ; 49:9, s. 2335-2348
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Tidskriftsartikel (refereegranskat)abstract
- This study aims to investigate factors affecting daily demand in an emergency department (ED) and to provide a forecasting tool in a public hospital for horizons of up to 7 days.In this study, first the important factors to influence the demand in EDs were extracted from literature then the relevant factors to our study are selected. Then a deep neural network is applied for constructing a reliable predictor.Although many statistical approaches have been proposed for tackling this issue, better forecasts are viable through employing the abilities of machine learning algorithms. Results indicate that the proposed approach outperforms statistical alternatives available in the literature such as multiple linear regression (MLR), autoregressive integrated moving average (ARIMA), support vector regression (SVR), generalized linear models (GLM), generalized estimating equations (GEE), seasonal ARIMA (SARIMA) and combined ARIMA and linear regression (LR) (ARIMA-LR).We applied this study in a single ED to forecast the patient visits. Applying the same method in different EDs may give us a better understanding of the performance of the model. The same approach can be applied in any other demand forecasting after some minor modifications.To the best of our knowledge, this is the first study to propose the use of long short-term memory (LSTM) for constructing a predictor of the number of patient visits in EDs.
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| 2. |
- Fathi, Masood, et al.
(författare)
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Production Sustainability via Supermarket Location Optimization in Assembly Lines
- 2020
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Ingår i: Sustainability. - : MDPI. - 2071-1050. ; 12:11, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- Manufacturers worldwide are nowadays in pursuit of sustainability. In the Industry 4.0 era, it is a common practice to implement decentralized logistics areas, known as supermarkets, to achieve production sustainability via Just-in-Time material delivery at assembly lines. In this environment, manufacturers are commonly struggling with the Supermarket Location Problem (SLP), striving to efficiently decide on the number and location of supermarkets to minimize the logistics cost. To address this prevalent issue, this paper proposed a Simulated Annealing (SA) algorithm for minimizing the supermarket cost, via optimally locating supermarkets in assembly lines. The efficiency of the SA algorithm was tested by solving a set of test problems. In doing so, a holistic performance index, namely the total cost of supermarkets, was developed that included both shipment cost and the installation cost across the assembly line. The effect of workload balancing on the supermarket cost was also investigated in this study. For this purpose, the SLP was solved both before and after balancing the workload. The results of the comparison revealed that workload balancing could significantly reduce the total supermarket cost and contribute to the overall production and economic sustainability. It was also observed that the optimization of material shipment cost across the assembly line is the most influencing factor in reducing the total supermarket cost.
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