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In Turkey, the transportation industry's greenhouse gas (GHG) emissions increased by 147.1% between 1990 and 2019. Today, this transportation industry (i.e., freight and passenger) is among the significant contributors to greenhouse gas emissions in Turkey's megacities. Moreover, 65.43% of short-distance trips between home to work and home to school have been made by private automobiles in Istanbul and increasing concerns about environmental pollution have led practitioners to seek practical, robust, and effective solutions to reduce GHG emissions. Shared electric scooters have rapidly become popular for end-users and practitioners in megacities, depending on their valuable advantages. However, the rapid spread of micro-mobility, characterized by e-scooters, has also raised questions about this system's sustainability, suitability, and applicability. Thus, there are some critical and noteworthy gaps in this issue. This study investigates the factors affecting the suitable e-scooter selection for a sustainable urban transport system. Besides, it aims to develop a methodological framework for assessing the available e-scooter alternatives. For this purpose, a novel negotiation approach, a new form of the Delphi technique, was developed with the help of Fermatean fuzzy sets to identify the influential criteria. Also, the current paper presents a consensus-based MULTIMOORA (Multiple Objective Optimization on the basis of Ratio Analysis plus Full Multiplicative Form) decision-making model based on Fermatean fuzzy sets to address the appraisal problem concerning e-scooter selection. The current paper indicated that economic measures such as acquisition price and upkeep costs affect the e-scooter selection processes. In addition, an optimization model based on cross-entropy and dispersion measures is utilized to compute criteria weights. It highlighted that the costs of e-scooters are still high, and operators consider these criteria instead of the technical and operational features of the e-scooters. Finally, the validity check executed to test the robustness and trustworthiness of the model affirms the model's firmness and trustworthiness. [ABSTRACT FROM AUTHOR]

In this study, the electricity demands of some Fortune 500 companies in Türkiye have been forecasted by using deep learning techniques. This is a quite harder problem than the forecasting of the aggregated electricity demand in which the negative and positive fluctuations are absorbed on paper. Forecasting of firm-level electricity demand is an important problem since it can help automating firms' routine forecasting operations, reducing the electricity supply costs of the firms by improving the quality of the forecasts, and improving the quality of the electricity on the transmission network. Long Short-Term Memory (LSTM) and Multilayer Perceptron (MLP) techniques have been preferred concerning the successful results in the literature. As the originality of this paper, the Multiple Seasonal-Trend Decomposition using Loess (MSTL) technique is used for the electricity demand forecasting problem for the first time. The obtained results showed that although it is simple to implement, MSTL outperforms MLP and LSTM for most of the firms operating in mass production form. It is seen that the complexity of the model does not always guarantee good results and simple methods sometimes can work well. Load balancing studies are also very important for the economic sustainability of the industry since the electricity price and imbalance penalty have extremely increased (i.e., 8 times in Türkiye) during the post-pandemic period. Therefore, the energy cost reduction potential of the companies has also been assessed. This study resulted in cost savings of approximately 378 minimum wages for the pilot company. [ABSTRACT FROM AUTHOR]

Abstract: Training neural networks (NNs) is a complex task of great importance in the supervised learning area. However, performance of the NNs is mostly dependent on the success of training process, and therefore the training algorithm. This paper addresses the application of harmony search algorithms for the supervised training of feed-forward (FF) type NNs, which are frequently used for classification problems. In this paper, five different variants of harmony search algorithm are studied by giving special attention to Self-adaptive Global Best Harmony Search (SGHS) algorithm. A structure suitable to data representation of NNs is adapted to SGHS algorithm. The technique is empirically tested and verified by training NNs on six benchmark classification problems and a real-world problem. Among these benchmark problems two of them have binary classes and remaining four are n-ary classification problems. Real-world problem is related to the classification of most frequently encountered quality defect in a major textile company in Turkey. Overall training time, sum of squared errors, training and testing accuracies of SGHS algorithm, is compared with the other harmony search algorithms and the most widely used standard back-propagation (BP) algorithm. The experiments presented that the SGHS algorithm lends itself very well to the training of NNs and also highly competitive with the compared methods in terms of classification accuracy. [Copyright &y& Elsevier]

Abstract: Outsourcing refers to a company that contracts with another company to provide services that might otherwise be performed by in-house employees. Information system (IS) outsourcing policies define the criteria that organizations utilize to decide upon the scope and degree of reliance of their IS capabilities upon external sources. IS outsourcing is an innovative organizational tool for IS management in both private and public sector organizations. In this paper, an interactive group decision-making methodology is proposed to select/rank IS providers under multiple criteria. A measure for the consensus level of the group preferences is developed to satisfy an acceptable level of group agreement and reliability. The Spearman coefficients for both the aggregated rank order and each DM''s rank order have also been calculated. The group and the individual evaluations are gathered through a fuzzy TOPSIS approach. The proposed methodology is applied in the largest office furniture manufacturer in Konya-Turkey. Eight alternative IS providers are evaluated based on seven criteria by five decision makers. Sensitivity analyses are also provided to see the effects of parameter changes on the final decision. [Copyright &y& Elsevier]