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- BildiriYalnız künye2017Forecasting daily patient visits in an emergency department by ga-ann hybrid approach
Pekel, Engin; Gul, Muhammet; Celik, Erkan; Drobne, S.; Zerovnik, J.; Zadnik Stirn, L.; Kljajic Borstar, M.
An Emergency Department (ED) plays a crucial role in the health system by providing acute care for patients who attend hospital without prior appointment. An accurate forecasting of patient visits in EDs contributes to health care decision makers to better allocate ED human resources and medical equipment. In this paper, a hybrid genetic algorithm–artificial neural network (GA–ANN) approach is developed. The forecasting performance of the hybrid approach is obtained using the real-world data set collected from a public hospital in Istanbul, Turkey. The hybrid GA–ANN model is shown to perform well in terms of forecasting accuracy. In order to contribute to the current knowledge, this paper is a novel attempt of applying GA-ANN to model ED patient arrivals, and the results can be used to aid in strategic decision-making on ED resource planning in response to predictable arrival variations.
AtıfScopus4
İncele - BildiriYalnız künye2017An ann and ga approach for demand forecasting and routing for cash management
Gumus, Alev Taskin; Celik, Erkan; Ömerustaoğlu, Furkan; Drobne, S.; Zerovnik, J.; Zadnik Stirn, L.; Kljajic Borstar, M.
Forecasting of cash demand and cash transportation are critical processes for all bank asynchronous transfer modes (ATMs). In this paper, an application is presented to accurate forecasting of cash demand and minimizing the transportation cost. In the first step of the proposed application, artificial neural network is implemented for forecasting the amount of withdrawn and deposited money from an ATM in one day. Hence, a more accurate way is provided to determine the amount of money that will be loaded into the ATM, and opportunity costs are minimized. Then, we applied genetic algorithm for minimizing the transportation cost of cash deposit to the ATMs.
- BildiriYalnız künye2017Optimizing the location-allocation problem of bike sharing stations: A case study in gaziantep university campus
Özceylan, Eren; Mete, Süleyman; Çil, Zeynel Abidin; Drobne, S.; Zerovnik, J.; Zadnik Stirn, L.; Kljajic Borstar, M.
A growing number of cities are implementing bike-sharing programs to increase bicycle use. One of the key factors for the success of such programs is the location of bike stations in relation to potential demand. In this paper, potential locations of bike sharing stations in Gaziantep University campus are investigated to provide optimal utilization for users. To do so, first of all, 20 demand points and 20 potential bike stations are determined. Second, set covering mathematical model is handled to determine coverage capability of potential bike stations. Finally, P-center and P-median mathematical models are applied to set up potential stations and to assign demand points to the opened stations so that the total walking distance can be minimized. Computational experiments prove that our approach can find new potential bike stations which cover all demand points.
AtıfScopus5İncele