Endüstri Mühendisliği Bölümü Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.11779/1942

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Start date: 2022Publisher: search.filters.publisher.Springer

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  • Book Part
    Citation - Scopus: 1
    Interval Valued Intuitionistic Fuzzy Z Extensions of Ahp&codas: Comparison of Energy Storage Alternatives
    (Springer, 2023) Sergi, Duygu; Sarı, İrem Uçal; Ucal Sari, Irem
    Energy storage technologies are receiving increasing attention due to the trend toward renewable energy sources. Energy storage systems are a promising technology as they provide the low carbon emissions needed in the future, contribute to renewable energy production, and offer an alternative to petroleum-derived fuels. It is not possible to say precisely how the energy will be stored, and often more than one method must be used together. In this study, battery technologies from electrochemical energy storage systems are discussed. This chapter proposes a multi-criteria decision-making (MCDM) model combining fuzzy IVIF-Z-AHP and fuzzy IVIF-Z-CODAS methods to choose the optimal battery ESS. The priority weights of 4 main and 11 sub-criteria related to energy storage efficiency are determined using the IVIF-Z-AHP method. After that, 5 different batteries are evaluated using the IVIF-Z-CODAS method, and the most appropriate battery ESS is selected by doing a performance evaluation regarding the storage of energy at maximum efficiency.
  • Article
    Citation - WoS: 5
    Citation - Scopus: 12
    Predicting Cash Holdings Using Supervised Machine Learning Algorithms
    (Springer, 2022-05-18) Özlem, Şirin; Tan, Ömer Faruk
    This study predicts the cash holdings policy of Turkish firms, given the 20 selected features with machine learning algorithm methods. 211 listed firms in the Borsa Istanbul are analyzed over the period between 2006 and 2019. Multiple linear regression (MLR), k-nearest neighbors (KNN), support vector regression (SVR), decision trees (DT), extreme gradient boosting algorithm (XGBoost) and multi-layer neural networks (MLNN) are used for prediction. Results reveal that MLR, KNN, and SVR provide high root mean square error (RMSE) and low R2 values. Meanwhile, more complex algorithms, such as DT and especially XGBoost, derive higher accuracy with a 0.73 R2 value. Therefore, using advanced machine learning algorithms, we may predict cash holdings considerably.