Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2025Scopus SJRQ4/r/munzur/1948
  Deep learning-based hybrid traffic flow prediction model for freight transport

  Utku, Anıl; Kaya, Sema Kayapinar; Can, Umit

  Urban freight transportation is crucial to sustaining domestic and international trade and fulfilling the regular requirements of community companies and customers. Therefore, this study developed an efficient convolutional neural network (CNN) and long short-term memory (LSTM) based hybrid deep learning model for predicting traffic flow in freight transportation. The number of freight trucks that would travel along 663 Street in Anderlecht, Belgium, and their average speed was predicted. The created model was compared with support vector machine (SVM), linear regression (LR), multilayer perceptron (MLP), random forest (RF), LSTM, and CNN using the success measures. The critical finding of this study is that combining the feature extraction ability of the 1D CNN model with the learning ability of the LSTM model from multivariate traffic data increases the traffic density prediction success compared to other traditional methods. Based on the results of the experiments, the CNN-LSTM model outperformed all the other models employed in this study. With this model, freight density can be successfully predicted, assisting in planning freight transport activities.

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• MakaleYalnız künye2025Scopus SJRQ1/r/munzur/869
  AI-enabled human capital management (HCM) software adoption using full consistency method (FUCOM): evidence from banking industry

  Yadav, Rama Shankar; Kaya, Sema Kayapinar; Pant, Abhay; Tiwari, Anurag

  Purpose: Artificial intelligence (AI)-based human capital management (HCM) software solutions represent a potentially effective way to leverage and streamline a bank’s human resources. However, despite the attractiveness of AI-based HCM solutions to improve banks’ effectiveness, to the best of the authors’ knowledge, there are no current studies that identify critical success factors (CSFs) for adopting AI-based HCM in the banking sector. This study aims to fill this gap by investigating CSFs for adopting AI-based HCM software solutions in the banking sector. Design/methodology/approach: Full consistency method methodology and technology–organization–environment, economic and human framework are used for categorizing and ranking CSFs. Findings: The study identifies the technological and environmental dimensions as the most and least important dimensions for AI-based HCM adoption in banks. Among specific CSFs, compatible technology facilities, sufficient privacy and security and relative advantages of technology over competing technologies were identified as the most important. Implementation of AI-based HCM solutions requires significant outlays of resources, both human and financial, for banks. Originality/value: The study provides bank administrators a set of objective parameters and criterion to evaluate the feasibility of adopting a particular AI-based HCM solution in banks.

  AtıfScopus9·WoS7·OpenAlex17

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• Kitap BölümüYalnız künye2023/r/munzur/2802
  An IT2FBWM Model to Highlight the Significance of Factors Utilized in Determining Pandemic Hospital Site Selection

  Aycin, Ejder; Kayapinar Kaya, Sema; Ecer, Fatih

  The COVID-19 pandemic has spread to the world in a short time since its emergence in Wuhan, China, and mobilized international health authorities with its effects, continues its development seriously. Due to the fast spread of COVID-19, governments have made some decisions to build new temporary hospitals for patients infected with COVID-19. This situation increased the importance of the site selection process for a pandemic hospital during the current critical time. Site selection of a hospital can be considered as multi-criteria decision-making (MCDM) problem since it includes multiple factors. This paper applies an analytical tool for evaluating the criteria of the site selection of a pandemic hospital. For this purpose, the extended best–worst method with trapezoidal interval type-2 fuzzy numbers (IT2FBWM) is employed to determine the importance weights of the pandemic hospital site selection criteria. The paper’s findings point out that the IT2FBWM model can help decision-makers determine the importance weights of the criteria used in the pandemic hospital siting decisions.

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  AtıfScopus1·OpenAlex2

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• MakaleAçık Erişim2023Scopus SJRQ1/r/munzur/2778
  An Integrated Approach for Assessing Suppliers Considering Economic Sustainability Innovation

  Ahmadi, Hadi Badri; Pamucar, Dragan; Pourhejazy, Pourya; Kaya, Sema Kayapinar; Liou, James J. H.

  Innovation plays a key role for improving the sustainability performance of corporations. A truly sustainable innovative firm needs to take the social, environmental, and economic aspects of sustainable innovation into consideration. However, limited studies have investigated the economic aspect of sustainable innovation, especially in the context of supplier evaluation from emerging economies. To deal with this issue, this manuscript puts forward a criteria decision framework for evaluating economic sustainable innovative suppliers. A new methodology based on fuzzy Full Consistency Method (FFUCOM) and Improved Combinative Distance-based Assessment (ICODAS) is developed and extended using interval rough Dombi-Bonferroni operators. In the developed approach, the FFUCOM is employed to determine the criteria weights, and the ICODAS is responsible for assessing and ranking the suppliers. A case study from the manufacturing sector of an emerging economy is considered for validating the developed framework and decision model. The findings introduce the 'financial resumption of products' as the most critical economic innovation criterion for evaluating suppliers. This article significantly helps decision-makers and practitioners to better comprehend the underpinnings of sustainable innovation economic dimension, particularly when assessing their suppliers. Furthermore, the applicability and validity of the proposed model was confirmed by the results of sensitivity analysis. Findings demonstrated that the proposed method is a solid and useful MCDM framework, and its outputs are precise and acceptable.

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  AtıfScopus9·OpenAlex6

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• MakaleYalnız künye2023Scopus SJRQ3/r/munzur/2416
  Evaluation of social factors within the circular economy concept for European countries

  Kaya, Sema Kayapinar; Ayçin, Ejder; Pamucar, Dragan

  The circular economy (CE) is a rapidly growing theme, particularly in the European Union (EU), that encourages the responsible and circular use of resources in the field of contributing to long-term development. The environmental and economic magnitude of development are frequently debated in the subject of CE whereas, social aspects have been only rarely and sporadically combined into the CE. Therefore, this study involves a multidisciplinary holistic framework of clustering methods and Multi-Criteria Decision Making (MCDM) for evaluating the CE paradigm in relation to EU countries' social growth. In terms of the social impact of CE strategies, the k-means cluster analysis was used to group the 27 EU members with similar social impact levels. Subsequently, a novel integrated CRITIC (The criteria importance through intercriteria correlation) and MEREC (Method based on the Removal Effects of Criteria) methods were employed for determining the weights of social indicators in order to balance the two weighting methods. In order to select the best country in each cluster, a novel extension of the hybrid MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method combined with Power averaging and Heronian operator was developed. According to final solutions, Western European countries in the first cluster have the lowest unemployment and corruption rates, with the Netherlands having the best performance in this cluster. The second cluster includes countries with the lowest employment rates following university graduates between the ages of 20 and 64. Accordingly, Croatia is the best social performance in this cluster. Countries with the highest income distribution and unemployment rate are in the 3rd cluster, the best country in this group is Lithuania. Finally, the results obtained from the innovative MCDM methods have validated in order to demonstrate the proposed methodology's applicability.

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  AtıfScopus30·WoS23·OpenAlex42

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• Kitap BölümüYalnız künye2023/r/munzur/2352
  Healthcare Logistics and Digital Transformation

  Kaya, Sema Kayapınar

  Due to the complex and diverse structure of the healthcare industry, the healthcare industry's logistics practices have lagged far behind sectors such as retail and manufacturing. Therefore, in today's increasingly competitive and risky environment, it is critical for hospitals to improve their logistics management performance, increase the efficiency of health services, and reduce costs. This chapter synthesizes the contributions of healthcare logistics, the impact of medical logistics on healthcare providers, types of healthcare logistics. Addition, this chapter enables us to capture the research challenges associated with the healthcare logistics process, pharma logistics and show how digitalization initiatives can address them.

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  AtıfScopus2·OpenAlex2

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• MakaleYalnız künye2023Scopus SJRQ1/r/munzur/2264
  A novel two-phase group decision-making model for circular supplier selection under picture fuzzy environment

  Kaya, Sema Kayapinar

  Circular supply chain management (CSCM), which incorporates circular thinking into supply chain management, promotes supply chain sustainability by offering a novel and compelling viewpoint. In the CSCM, supplier selection is crucial in establishing a competitive edge among businesses by decreasing environmental degradation and related supply chain expenses. This paper aims to propose a novel two-phase group decision-making approach, which combines the picture fuzzy Analytical Hierarchical Process (PF-AHP) and grey Measurement of Alternatives and Ranking According to Compromise Solution (MARCOS-G) model to select railway material suppliers within the scope of circular economy perspective. In group decision-making, a picture fuzzy number-based approach aggregates individual decision makers’(DMs) opinions and provides a lower level of computing complexity and a higher level of performance. The PF-AHP has been used to produce the weights of the criteria, and the MARCOS-G technique has been used to rank the suppliers and choose the most appropriate one. The results and verification of the novel method are carried out throughout a comprehensive sensitivity analysis. For this purpose, 50 scenarios with changes in the weight values of criteria were developed. In the validation analysis, a comparison with other grey COPRAS, TOPSIS, ARAS and WASPAS methods were performed. Due to the ranking results obtained from Spearman’s correlation analyses, the MARCOS-G is almost consistent and it can be seen that the A4 remains the top supplier in all 50 scenarios.

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  AtıfScopus34·WoS30·OpenAlex39

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• MakaleYalnız künye2023Scopus SJRQ1/r/munzur/2177
  Evaluation of container port sustainability using WASPAS technique using on type-2 neutrosophic fuzzy numbers

  Kaya, Sema Kayapinar; Kundu, Pradip; Görçün, Ömer Faruk

  According to the common opinion in the literature, the sustainability of container ports is a tremendously complex topic owing to the maritime sector's excessively dynamic form and many highly complex, predictable and unpredictable uncertainties in this industry. The current paper proposes two powerful, practical, inspiring approaches to fill these gaps. It proposes a novel type-2 neutrosophic fuzzy numbers (T2NFNs) based Delphi method to determine the criteria logically and optimal and extends the WASPAS technique based on the T2NFNs for evaluating the alternatives. The current paper presents practical managerial implications that many stakeholders can consider, such as port authorities, ship owners, logistics service providers, governments, and local authorities, when making strategic and managerial decisions. In addition, the results of a comprehensive sensitivity analysis performed to test the robustness and applicability of the model approve the validity of the proposed T2NFN-based integrated approach.

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  AtıfScopus20·WoS16·OpenAlex24

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• MakaleYalnız künye2023Scopus SJRQ2/r/munzur/2091
  New Deep Learning-Based Passenger Flow Prediction Model

  Utku, Anıl; Kaya, Sema Kayapinar

  The ability to predict passenger flow in transport networks is an important aspect of public transport management. It helps improve transport services, aids those responsible for management to obtain early warning signals of emergencies and unusual circumstances and, in general, makes cities smarter and safer. This paper develops a long short-term memory-based (LTSM-based) deep learning model to predict short-term transit passenger volume on transport routes in Istanbul. This prediction model has been created using a dataset that included the number of people who used different transit routes in Istanbul at one-hour intervals between January and December 2020. The proposed multilayer LSTM-based deep learning model has been compared with popular models such as random forest (RF), support vector machines, autoregressive integrated moving average, multilayer perceptron, and convolutional neural network. The experimental findings showed that the proposed multilayer LSTM-based deep learning model outperformed the other models with regard to prediction for each transfer route. Furthermore, RF, one of the machine learning models used, produced remarkably successful results.

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  AtıfScopus19·WoS12·OpenAlex16

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• MakaleYalnız künye2023Scopus SJRQ1/r/munzur/1772
  Sustainability performance analysis of micro-mobility solutions in urban transportation with a novel IVFNN-Delphi-LOPCOW-CoCoSo framework

  Ecer, Fatih; Küçükönder, Hande; Kayapınar Kaya, Sema; Faruk Görçün, Ömer

  Almost all megacities worldwide are under pressure from environmental pollution and excessive resource usage due to rapid urbanization and increasing urban population. Within this scope, micro-mobility solutions (MMSs), an extension to the primary urban transportation network, have become even more critical recently. However, when we executed a preliminary investigation concerning MMSs' sustainability, we noted some critical theoretical and managerial gaps: (1) the number of studies assessing MMSs using decision-making approaches is excessively scarce, (2) no paper in the literature deals with the MMSs' sustainability performance, and (3) there are no commonly accepted criteria set to assess the sustainability of the MMSs in the literature. The current study aims to present a practical and robust decision-making framework that can overcome excessively complicated uncertainties to evaluate the sustainability performance of MMSs. Further, it suggests a novel methodological framework, i.e., Delphi, Logarithmic Percentage Change-Driven Objective Weighting (LOPCOW), and Combined Compromise Solution (CoCoSo) methods with interval-valued fuzzy neutrosophic number (IVFNN) information, which can be defined as a reconciliation tool to set the criteria affecting the assessment processes. Findings present that computing time, scenic adoption, and accidents are the foremost criteria, whereas mini-electric VH, powered standing scooter, and powered seated scooter are the most promising MMSs, respectively. Findings reveal that computing time, scenic adoption, and accidents are the critical factors for the sustainability assessment of MMSs, whereas mini-electric VH, powered standing scooter, and powered seated scooter are the most promising MMSs, respectively. The current paper's overall outcomes and findings can assist practitioners and policy-makers in the urban transportation industry in setting sustainability policies and investing in the more rational and sustainable MMSs.

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  AtıfScopus104·WoS91·OpenAlex112

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