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    A hybrid spherical fuzzy logarithmic decomposition of criteria importance and alternative ranking technique based on Adaptive Standardized Intervals model with application
    (Elsevier Inc., 2024) Yalçın, Galip Cihan; Kara, Karahan; Senapati, Tapan
    This study presents a hybrid fuzzy Multi-Attribute Group Decision Making (MAGDM) model with application to commercial insurance selection. The proposed hybrid model uses Spherical Fuzzy (SF) sets using Yager t-norm and t-conorm operations. The Logarithmic Decomposition of Criteria Importance (LODECI) method is used for criterion weighting due to its efficacy in stabilizing scenarios that may challenge other weighting techniques. For alternative ranking, the study employs the Alternative Ranking Technique based on Adaptive Standardized Intervals (ARTASI), offering enhanced flexibility in handling uncertainties inherent in expert evaluations. The combination of these methods and the utilization of SF sets gives rise to the proposed SF-LODECI-ARTASI hybrid model. The paper systematically delineates the procedural steps involving SF sets, Yager t-norm and t-conorm operations, SF-LODECI, and SF-ARTASI methods. Subsequently, the developed hybrid model is applied to a case study of commercial insurance selection, supported by a numerical example. The research application results emphasize the consistency of these findings with other alternative methods. Additionally, sensitivity scenarios are constructed to scrutinize the robustness of the proposed hybrid model. The study concludes by elucidating implications and contributions to the existing literature. © 2024 The Author(s)
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    A neutrosophic WENSLO-ARLON model for measuring sustainable brand equity performance
    (Elsevier Science Inc, 2024) Kara, Karahan; Yalcin, Galip Cihan; Ergin, Elif Akagun; Simic, Vladimir; Pamucar, Dragan
    Sustainable brand equity denotes the perceived brand equity by consumers, influenced by companies' nonfinancial and environmental practices. To cultivate sustainable brand equity, corporations undertake green initiatives and engage in social responsibility endeavors, thereby communicating environmentally oriented messages to consumers. Companies feel the necessity to establish a competitive position in sustainability relative to their counterparts. Hence, the primary objective of this research is to conduct an inquiry aimed at identifying sustainable brand equity to meet this exigency of companies. In this context, the motivation of this study lies in treating the calculation of sustainable brand equity as a decision-making process and developing a decision support system for this purpose. The proposed decision support system for determining sustainable brand equity levels in this research has three fundamental inputsss: experts, criteria, and brands. The impact levels of experts on the decision-making process are quantified utilizing type-2 neutrosophic number (T2NN) sets. The weights of criteria are ascertained employing the weight by envelope and slope (WENSLO) method extended with T2NN sets. The ranking of sustainable brand equities is established through the innovative T2NN-based alternative ranking using two-step logarithmic normalization (ARLON) method. These methodologies are amalgamated, and the T2NN-WENSLO-ARLON hybrid model is posited as a decision support system for evaluating sustainable brand equity performance. An algorithm for T2NN-WENSLO-ARLON is formulated and elucidated alongside a case study focusing on companies operating in the cosmetics sector in Turkey. The case study identifies the green product leadership criterion as the most influential factor in determining sustainable brand equity. Furthermore, the cosmetic company Misbah & ccedil;e Inc. is identified as the brand with the highest sustainable brand equity. Sensitivity and comparative analyses are conducted to ascertain the robustness of the results and the decision model. All scenario outcomes bolster the decision model. The research provides comprehensive insights into both the developed tool for calculating sustainable brand equity and develops implications for the cosmetics industry.
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    A picture fuzzy CIMAS-ARTASI model for website performance analysis in human resource management
    (Elsevier, 2024) Kara, Karahan; Yalcin, Galip Cihan; Kaygisiz, Esra Gokcen; Simic, Vladimir; Ornek, Ali Sahin; Pamucar, Dragan
    The central emphasis of human resource management resides in the precise delineation of job responsibilities, engaging in systematic inquiry to identify well-suited candidates, and discerningly electing the most qualified individual from the pool whose qualifications align with the job description. The selection of a well-suited candidate is dependent on the creation of an appropriate candidate pool. Digital platforms are preferred over traditional approaches to reach human resources. The main objective of this research is to identify the most suitable digital platform for posting job advertisements based on website performance. The multiple-attribute group decision-making approach is adopted in this research, considering both qualitative and quantitative criteria. A decision support system for website selection is developed, where expert importance levels are calculated based on picture fuzzy sets (PFS). The weight levels of criteria are determined using the introduced PFS-based criteria importance assessment (CIMAS) method. The ranking of websites is calculated using the proposed PFS-based alternative ranking technique based on adaptive standardized intervals (ARTASI) method. These methods are hybridized into the PFS-CIMAS-ARTASI model. Additionally, an algorithm for this hybrid model is developed. A case study is conducted to demonstrate the applicability of the PFS-CIMAS-ARTASI hybrid model for website performance calculations. Robustness tests based on various sensitivity analysis scenarios are performed. The research results indicate that PFS-CIMAS-ARTASI is both applicable and robust. Comprehensive managerial implications are presented and elaborated on.
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    A single-valued neutrosophic CIMAS-CRITIC-RBNAR decision support model for the financial performance analysis: A study of technology companies
    (Elsevier Science Inc, 2024) Kara, Karahan; Yalcin, Galip Cihan; Cetinkaya, Asli; Simic, Vladimir; Pamucar, Dragan
    Financial performance stands as a fundamental performance indicator that reflects companies' current financial conditions, providing information to investors and stakeholders about companies' financial well-being. In this research, a decision support system is developed for identifying the financial performance of companies traded on stock exchanges. This decision support system is based on a multicriteria decision -making (MCDM) approach and neutrosophic logic. The research introduces a novel method for calculating and ranking financial performance, using financial ratio indicators as selection criteria. The importance levels of financial ratio indicators are weighted through two different approaches. In the first approach, expert opinions and criteria importance assessment (CIMAS)-based on single -valued neutrosophic (SVN) sets are utilized for calculation. In the second approach, criteria importance through intercriteria correlation (CRITIC) -based on reference -based normalization processes is employed for criteria weighting. Subsequently, the results of the two criterion weightings are combined to calculate the final criterion weights. The SVN reference -based normalization alternative ranking (RBNAR) method is presented for ranking companies based on their financial performance. Thus, SVN-CIMASCRITIC-RBNAR is developed and its algorithm is presented. The novel hybrid decision support model is applied to a case study of technology companies traded on the Borsa Istanbul. The research results support the applicability of the SVN-CIMAS-CRITIC-RBNAR hybrid method. The results of the case study and sensitivity analyses affirm the applicability and robustness of the SVN-CIMAS-CRITIC-RBNAR hybrid model. The research provides detailed implications and insights for financial managers.
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    A single-valued neutrosophic-based methodology for selecting warehouse management software in sustainable logistics systems
    (Pergamon-Elsevier Science Ltd, 2024) Kara, Karahan; Yalcin, Galip Cihan; Simic, Vladimir; Onden, Ismail; Edinsel, Sercan; Bacanin, Nebojsa
    Warehouse operations cover activities that must be carried out systematically. Warehouse management systems (WMS) are needed for successful warehouse management. With digitalization, alternative software programs for WMS have increased. In this study, the selection of the WMS software program for the general warehouse type is discussed. The main motivation of the research is to develop a suitable alternative selection methodology based on criteria for warehouse managers. The research is carried out in several stages. In the first stage, the software selection criteria are determined based on the literature review. In the second stage, the criteria are weighted with the criteria importance through the intercriteria correlation (CRITIC) method based on single-valued neutrosophic sets (SVNSs). In the third stage, alternatives for WMS software are ranked with the technique for order preference by similarity to an ideal solution (TOPSIS) based on SVNSs. Three decision-makers, four al-ternatives, and eight criteria are used in the study. The empirical research is conducted on a warehouse providing general storage services in Turkey. In addition, the sensitivity analyses are performed and the results are compared. According to the research findings, the most valuable criterion in WMS software selection is deter-mined as ease of use. As a result of the research, suggestions are developed for WMS software manufacturers, warehouse managers, and researchers. The novelty of this research is that WMS selection is applied for the first time using multi-criteria decision-making methods.
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    A spherical fuzzy-based DIBR II-AROMAN model for sustainability performance benchmarking of wind energy power plants
    (Pergamon-Elsevier Science Ltd, 2024) Kara, Karahan; Yalcin, Galip Cihan; Simic, Vladimir; Yildirim, Ali Tugrul; Pamucar, Dragan; Siarry, Patrick
    Wind energy power plants (WEPPs) are renewable energy generation facilities that are increasingly used today. The performance evaluations of these energy plants are generally based on technical performance. Although there are studies in the literature focusing on determining the technical performance of WEPPs, there is a lack of research on evaluating their sustainable performance. The primary motivation of this study is to develop a decision model for identifying the sustainable performance of WEPPs. The model incorporates fuzzy logic and multi-criteria decision-making (MCDM) approaches, incorporating expert opinions and both qualitative and quantitative criteria. To determine the influence levels of experts, spherical fuzzy (SF) sets are utilized. The weighting of criteria is achieved using the SF-defining interrelationships between ranked criteria II (DIBR II) method, which is a novel extension of the DIBR II method based on SF sets. Additionally, an SF-alternative ranking order method accounting for two-step normalization (AROMAN) method is developed for ranking the sustainable performance of WEPPs, building upon the AROMAN method, and adapting it to SF sets. This hybrid model is named the SF-DIBR II-AROMAN hybrid model. The step-by-step procedures of this model are presented in the research, and an algorithm for these procedures is developed. To demonstrate its practicality, a case study is conducted, focusing on WEPPs with a capacity ranging from 25 to 150 megawatts in & Ccedil;anakkale, Turkey. According to the research results, among the fifteen WEPPs in the & Ccedil;anakkale region, Saros WEPP is identified as having the best one. The robustness of the obtained results is ensured through sensitivity analyses. Based on the results of two sensitivity analysis scenarios, Saros WEPP is found to have the highest sustainable performance. The SF-DIBR II-AROMAN hybrid model results are compared with different alternative ranking method results, highlighting its superior aspects. Overall, SF-DIBR II-AROMAN is consistent and robust. The research also provides insights and managerial implications, as well as explains the benefits of the proposed model for evaluating WEPPs' sustainable performance.
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    A type-2 neutrosophic entropy-based group decision analytics model for sustainable aquaculture engineering
    (Pergamon-Elsevier Science Ltd, 2024) Kara, Karahan; Yalcin, Galip Cihan; Simic, Vladimir; Erbay, Murat; Pamucar, Dragan
    Fish cages are crucial for the establishment of sustainable fish farms. The selection of fish cages is an essential subject to be examined for ensuring sustainability. This research introduces an advanced decision support model for the selection of fish cage types used in fish farming in reservoirs (R-FFs). The decision support model is based on multi-criteria decision-making (MCDM) approach and utilizes type-2 neutrosophic numbers (T2NNs). The importance levels of criteria are determined using the T2NN-Entropy method, and the ranking of fish cage types is achieved through the alternative ranking order method accounting for two-step normalization (AROMAN). The developed model is referred to as the T2NN-Entropy-AROMAN hybrid method and relies on expert opinions. Two advanced aggregation operators based on Yager t-norm and t-conorm operations, named T2NN Yager weighted arithmetic mean and T2NN Yager weighted geometric mean, are developed for aggregating T2NNs. The validity of these two aggregation operators is demonstrated. A real-life case study is developed to confirm the applicability of the T2NN-Entropy-AROMAN hybrid method. This case study focuses on the selection of fish cage types for the Artvin-Bor & ccedil;ka R-FF in Artvin province, Turkey. The research results indicate that floating cages are the best alternative type for sustainable fish farming. Sensitivity analysis scenarios confirm the robustness of the research model and findings. The research findings, coupled with the developed model, offer valuable insights and guidance to decision-makers, researchers, and practitioners within the sector.
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    An integrated neutrosophic Schweizer-Sklar-based model for evaluating economic activities in organized industrial zones
    (Pergamon-Elsevier Science Ltd, 2024) Kara, Karahan; Yalcin, Galip Cihan; Simic, Vladimir; Polat, Mustafa; Pamucar, Dragan
    Organized industrial zones (OIZs) are specialized areas where industrial activities are concentrated, and various economic activities take place collectively. Within OIZs, diverse economic activities coexist, forming shared industrial spaces. However, different regions tend to cluster different economic activities within OIZs. Given the significance of determining suitable economic activities for OIZs, developing a model for this decision problem, and testing its practical applicability is crucial. This research presents a multi-criteria decision-making approach to develop an economic activity selection model for OIZs. This model is based on type-2 neutrosophic numbers (T2NNs). Furthermore, two T2NNs aggregation operators, namely T2NN Schweizer-Sklar weighted arithmetic mean and T2NN Schweizer-Sklar weighted geometric mean aggregation operators, are developed based on the Schweizer-Sklar operations for use in determining decision-maker weights and ranking economic activities. The T2NN-based criteria importance through intercriteria correlation (CRITIC) method is employed for criterion weighting. An algorithm specific to the proposed model is devised for ranking economic activities. As a case study, this algorithm is applied to the Artvin-Arhavi OIZ, which is in the process of establishment in Turkey. Sensitivity and comparative analyses are conducted to assess the robustness of the developed aggregation operators and the model. In the case study findings, the most significant criterion is identified as investment risk, and forestry, logging, and related service activities are determined as the best economic activity. This research contributes to the field by introducing the model and novel aggregation operator for the selection of economic activities in OIZs.
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    An interval-valued spherical fuzzy CIMAS-WISP group decision-analytic model for blockchain platform selection in digital projects
    (Elsevier, 2024) Kara, Karahan; Yalcin, Galip Cihan; Simic, Vladimir; Korkuc, Cagatay; Cicek, Ilhan; Afacan, Erkan; Pamucarg, Dragan
    Digital projects aspiring to reach target audiences are executed through decentralized and trustworthy block- chain platforms (BPs). Once the objectives and target audience of a digital project are defined, the selection of suitable BPs is undertaken. The primary objective of this research is to develop a decision support system that aids in the selection of BPs for transferring digital data and assets. Numerous quantitative parameters determine the performance of BPs, alongside qualitative parameters indicating their performance. In this study, the aim is to determine the performance of BPs based on both qualitative and quantitative parameters. Within this scope, a multi-criteria decision-making approach and interval-valued spherical fuzzy (IVSF) sets are adopted. IVSF sets are utilized to determine expert importance levels. The IVSF-criteria importance assessment (CIMAS) method is introduced for the weighting of criteria. IVSF-CIMAS enables the determination of reliability levels in calculating criterion weights. The IVSF-simple weighted sum product (WISP) method is formulated to obtain the performance ranking of BPs. Thus, in this research, the IVSF-CIMAS-WISP hybrid model is developed, and an algorithm for this novel decision-analytic model is presented. A case study is developed focusing on BP selection for a digital project to demonstrate the applicability of the proposed hybrid model. The robustness of IVSF-CIMAS
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    An intuitionistic fuzzy-based model for performance evaluation of EcoPorts
    (Pergamon-Elsevier Science Ltd, 2023) Yalcin, Galip Cihan; Kara, Karahan; Toygar, Arda; Simic, Vladimir; Pamucar, Dragan; Koleoglu, Nilay
    The EcoPort performance level serves as a basic indicator to determine the environmental status of a port and its achievement of certification standards. The European Sea Ports Organization has defined ten essential EcoPort criteria that contribute significantly to the assessment of EcoPort performance. The primary motivation for this study is to determine the importance of the criteria from the perspective of academics and chief officers, as well as to evaluate the EcoPort performance of six port authorities that comply with three basic certification standards for environmental management systems. The research methodology and application are conducted in four phases. In the first phase, experts, criteria, and alternative ports are identified. In the second phase, the importance levels of experts are determined using neutrosophic sets, while the criteria are weighted using the intuitionistic fuzzy weighting averaging operator. In the third phase, the alternative ranking order method ac-counting for two-step normalization (AROMAN) based on the intuitionistic fuzzy sets is introduced to evaluate the EcoPort performance of the ports. In the fourth phase, the results are supported by sensitivity analysis scenarios. The research results identified air pollution as the most critical criterion, while the Valencia Port Authority secured the highest rank in terms of EcoPort performance. Ultimately, this research contributes to the literature by developing and applying the new IF-AROMAN method for EcoPort performance evaluation.
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    Assessing Railway Transportation Performance of European Countries with CRITIC and ROV Techniques
    (2023) Kara, Karahan; Yalçın, Galip Cihan
    Rail transport is among the modes of transport that provides safe and reliable logistics services for the transport of passengers, goods, and dangerous goods. The decrease in railway transport volumes in recent years reveals the necessity of examining the railway transport performance. In this research, it is aimed to determine the railway transport performance of European countries in 2020. Sixteen railway performance criteria have been determined. Three of these criteria are cost-based and thirteen criteria are benefit-based. The criterion weights have been calculated by the Criteria Importance Through Intercriteria Correlation (CRITIC) technique. The railway transport performance of twenty-three European countries is presented using the Range of Value (ROV) technique. The data set has been obtained from the Eurostat database. According to the research findings, the three criteria with the highest weight are determined as rail accidents victims, rail accidents, accidents involving transport of dangerous goods. The three countries with the highest railway transport performance are Germany, Italy, and Sweden. Suggestions for increasing the railway transportation performance levels of the countries are presented.
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    Clustering Countries on Logistics Performance and Carbon Dioxide (CO2) Emission Efficiency: An Empirical Analysis
    (2022) Polat, Mustafa; Kara, Karahan; Yalçın, Galip Cihan
    The logistics industry is among the industries that affect carbon dioxide\remissions. The logistics activities of the countries produce CO2 emissions. For this\rreason, there is a significant relationship between the logistics performance of countries\rand their CO2 emissions. In this study, it is aimed to make a cluster analysis by\rconsidering the CO2 emission per capita efficiency of the countries and their logistics\rperformance. The empirical study was completed in three stages. In the first stage,\rhierarchical clustering analysis was conducted with the logistics performances of the\rcountries and the CO2 emission per capita. In the second stage, the CO2 emission per\rcapita efficiency based on the logistics performance sub-indicators of the countries were\rdetermined by data envelopment analysis. In the third stage, non-hierarchical clustering\ranalysis was performed with the variables of logistics performances and CO2 emission\rper capita efficiency of the countries. 2018 logistics performance index (LPI) and CO2\remission per capita data of 150 countries were used. According to the research findings,\rthere are differences in the findings of hierarchical clustering analysis and nonhierarchical\rclustering analysis. In the conclusion part of the study, the differences\rbetween the clusters were explained and suggestions were developed for the\rresearchers.
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    DETERMINATION OF LOGISTICS INNOVATION PERFORMANCE INDEX WITH ENTROPY AND COMBINED COMPROMISE SOLUTION TECHNIQUES
    (Symmetrion, 2022) Kara, Karahan; Yalcin, Galip Cihan; Kaygisiz, Esra Gokcen
    Logistics innovation performance is the resultant force of countries' logistics and innovation performance. Indices show the logistics performance and innovation performance of countries. However, the countries' logistics innovation performance index (LIPI) has not yet developed. The primary purpose of this research is to create the LIPI of developing countries for 2021. The global innovation index (GII) and Agility Emerging Market Logistics Index (AEMLI) scores of 48 developing countries were used. Ten criteria of the research have been used. Three criteria (domestic logistics opportunities, international logistics opportunities, business fundamentals) are from AEMLI, and seven criteria (institutions, human capital, research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, and creative outputs) are from GII. The criteria are weighted using the Entropy technique. The Combined Compromise Solution (CoCoSo) technique is used to determine LIPI scores. As a result of the research, 2021 LIPI scores and rankings of developing countries have been created. Both AEMLI and GII scores have been compared with LIPI scores. In comparison, symmetric and asymmetric rank distributions are presented. In addition, suggestions have been shown to developing countries and researchers based on the implications.
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    DETERMINING THE LOGISTICS MARKET PERFORMANCE OF DEVELOPING COUNTRIES BY ENTROPY AND MABAC METHODS
    (Poznan Sch Logistics, 2022) Kara, Karahan; Bentyn, Zbigniew; Yalcin, G. Cihan
    Background: The levels of logistics market performance of developing countries are published with Agility Emerging Markets Logistics Index (AEMLI) reports. The main purpose of this research is to propose a new model to determine the logistics market performance of developing countries in 2022 and to reorder the developing countries according to their logistics market performance.Methods: AEMLI indicators have been accepted as the basic criteria for determining the logistics market performance. The importance levels of these criteria have been determined by the Entropy technique. The logistics market performance rankings of developing countries according to the criteria were determined using the Multi-Attributive Border Approximation Area Comparison (MABAC) technique. The data set of 50 developing countries included in the 2022 AEMLI report has been used in the investigation.Results: According to the proposed new model, the weights of the criteria and logistics market performance rankings of developing countries have been determined. The importance levels of the criteria have been determined as Business Fundamentals (BF), Digital Readiness (DR), International Logistics Opportunities (ILO), and Domestic Logistics Opportunities (DLO), respectively. The ranking based on the new model was compared with the rankings in the 2022 AEMLI report. 21 of the 50 developing countries have improved their rankings. The ranking of 20 countries has been dropped. There is no change in the ranking of 9 countries. Additionally, according to AEMLI, the country with the highest logistics market performance is China, while the country with the best logistics market performance according to the proposed model is the United Arab Emirates (UAE). Conclusions: Contrary to the literature, Entropy and MABAC techniques were used to rank the logistics market performances of developing countries by making use of AEMLI reports. The issues that countries should focus on in the development of their logistics market performance are shown.
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    Developing a hybrid methodology for green-based supplier selection: Application in the automotive industry
    (Pergamon-Elsevier Science Ltd, 2024) Kara, Karahan; Acar, Avni Zafer; Polat, Mustafa; Onden, Ismail; Yalcin, Galip Cihan
    The green performance values of businesses are of great importance in terms of sustainability, which includes long-term economic, social, and environmental effects. Thus, today, enterprises and managers are increasingly interested in this issue, and related topics, including supplier selection, have been inserted into decision-making procedures. However, how to predict the effects of green performance criteria, which represent environmental sustainability, on social and economic sustainability remains unclear. In this regard, the main purpose of this research is to develop a supplier selection methodology considering green performance criteria by applying multiple regression analysis and the Evidential Fuzzy Multi-Criteria Decision Making (F-MCDM) method based on Dempster-Shafer Theory (DST), which are both powerful methods in statistical analysis and decision-making under uncertainty. In the first phase of the research, variables that significantly affect green performance have been determined by testing the eight generated hypotheses with multiple regression analysis. Then, the best supplier was determined using those green supplier selection criteria in the Evidential F-MCDM method. Since using environmentally hazardous paints in the production process continues, the automobile paint production sector has been chosen as the application area of this green-based supplier selection methodology. In this respect, green dynamic capacity, green purchasing, eco-design, investment recovery, and green product innovation variables have been inserted into the Evidential F-MCDM method as the determinant variables of green performance. This research reveals that integrating multiple regression and Evidential F-MCDM methods can be a hybrid methodology in supplier selection. Thus, a different perspective is introduced into the green supplier selection decision-making process by considering the effects of criteria in the MCDM model on green performance. This innovation enhances the criteria determination and selection processes in classical MCDM approaches. In addition, green dynamic capacity is the most critical criterion in supplier selection based on their green performance, especially in the scope of this research.
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    Enhancing decision support system for finished vehicle logistics service provider selection through a single-valued neutrosophic Dombi Bonferroni-based model
    (Pergamon-Elsevier Science Ltd, 2024) Kara, Karahan; Yalcin, Galip Cihan; Gurol, Pinar; Simic, Vladimir; Pamucar, Dragan
    Finished vehicle logistics (FVL) activities are executed through agreements with service provider firms. Vehicle manufacturers make efforts to select the most successful FVL service provider. The motivation for this research lies in developing and demonstrating the application of a decision support system for FVL service provider selection. Within this scope, it is necessary to develop a decision model that includes both quantitative and qualitative criteria. Considering the decision process as a group decision-making process allows for simultaneous consideration of different perspectives on the decision. Single-valued neutrosophic (SVN) sets are used for expert evaluations. The SVN weighted Dombi Bonferroni mean aggregation operator is employed for criteria weighting. The SVN-alternative ranking using two-step logarithmic normalization (ARLON) method is developed for ranking FVL service providers. The feasibility of the SVN-Dombi Bonferroni-ARLON hybrid algorithm is supported by the case study application. It is concluded that supply chain and logistics managers hold greater importance in FVL service provider selection, with the number of ships being the most critical selection criterion. The robustness of the proposed hybrid method for FVL service provider selection is supported by sensitivity analyses, while its consistency is supported by comparative analyses.
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    Exploring the adoption of the metaverse and chat generative pre-trained transformer: A single-valued neutrosophic Dombi Bonferroni-based method for the selection of software development strategies
    (Pergamon-Elsevier Science Ltd, 2024) Onden, Abdullah; Kara, Karahan; Onden, Ismail; Yalcin, Galip Cihan; Simic, Vladimir; Pamucar, Dragan
    The contemporary era has witnessed remarkable developments that seek to transform and reshape traditional software development methodologies. Notably, artificial intelligence (AI) supported software development as well as software development in virtual reality environments have gained considerable prominence. This article introduces software development strategies to examine how software developers and companies respond to this transformation. Also, an advanced decision model is developed using the alternative ranking order method accounting for two-step normalization (AROMAN) method and further analyzed with the single-valued neutrosophic set-based AROMAN technique. The single-valued neutrosophic weighted Dombi Bonferroni operator is employed in the analysis process. This research offers two case studies investigating the preferences of developers and managers in software development strategies. The first case study examines the preferences of developers, while the second focuses on the preferences of managers. In both case studies, three fundamental software development methods are presented. These include the traditional developers approach, AI-supported developers approach, and mixed reality and AI-supported developers approach. These methods are ranked based on expert opinions concerning 10 criteria that influence the software development process. In both case studies, output quality is identified as the most influential criterion. From the perspective of software development methods, in both case studies, the mixed reality and AI-supported developers approach is identified as the most effective. Recommendations are provided for developers and managers. The findings also have significant implications for guiding developers and managers in making informed decisions and optimizing software development practices to align with the evolving AI and virtual reality landscape.
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    Strategic location analysis for offshore wind farms to sustainably fulfill railway energy demand in Turkey
    (Elsevier Ltd, 2024) Önden, İsmail; Kara, Karahan; Yalçın, Galip Cihan; Deveci, Muhammet; Önden, Abdullah; Eker, Mert
    Providing the energy demands of various sectors through the utilization of renewable energy sources offers a more sustainable and long-term approach compared to fossil fuel consumption. In this study, the proposition is made to address Turkey's rail industry energy demand through Offshore Wind Farms (OWF). Accordingly, the spatial analysis for determining the most suitable OWF location is explored. The research methodology is conducted based on a Geographic Information System-Multi-Criteria Decision Making (GIS-MCDM) hybrid approach. Alternative OWF locations are identified using GIS. Location selection criteria are established based on literature review, and a fuzzy-based decision matrix is constructed, incorporating expert opinions. To leverage two distinct fuzzy number sets, Intuitionistic Fuzzy Weighted Averaging (IFWA) and Spherical Fuzzy Stepwise Weight Assessment Ratio Analysis (SF-SWARA) methods are employed for criteria weighting, followed by the aggregation of criterion weights using the Einstein Operations t-norm technique. To ensure comparable OWF alternative rankings, five different alternative ranking methods are employed to obtain alternative rankings. Ultimately, the most suitable OWF location for meeting the energy demand of the rail industry is determined to be the Izmir region. The research findings contribute to managerial perspectives by proposing recommendations concerning strategic location selection, optimized resource allocation, risk mitigation and policy alignment and sustainable long-term planning. © 2023 Elsevier Ltd
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    Supplier selection of companies providing micro mobility service under type-2 neutrosophic number based decision making model
    (Pergamon-Elsevier Science Ltd, 2024) Onden, Ismail; Deveci, Muhammet; Kara, Karahan; Yalcin, Galip Cihan; Onden, Abdullah; Eker, Mert; Hasseb, Mouad
    Environmental approaches play an active role in the production platform as well as in the supply chain. Studies that deal with supplier selection problems in the supply chain with a green focus have recently intensified. In this research, the supplier selection problem of companies providing last mile service, which is seen as the last step of transportation, is handled with a green focus. The supplier selection problem of these companies is differentiated according to strategic and non -strategic product groups. In this study, a green -oriented supplier selection proposal is presented for both the strategic product group and the non -strategic product group. The supplier selection problem is accepted as a multi criteria decision making problem (MCDM). Thus, the type -2 neutrosophic numbers (T2NNs) - based CRiteria Importance Through Intercriteria Correlation (CRITIC) - based Multi -Attributive Border Approximation Area Comparison (MABAC) hybrid model is applied to evaluate the green -oriented supplier. The motivation of the study is to determine the best alternative supplier type for strategic and non -strategic products groups. In this context, two cases are created, ten criteria are selected, and three supplier types are determined. Supplier types are as follows: (i) Classical suppliers, (ii) Hybrid -Classical to green centric suppliers, and (iii) Start -Ups & green centric suppliers. To devise a solution for addressing the research problem, the selection of the optimal green supplier type was accomplished through a recommended T2NNCRITIC-MABAC hybrid model. Accordingly, for both strategic and non -strategic product groups, micromobility service providers identified Hybrid -Classical to Green Centric Suppliers as the most suitable green supplier type. In addition, the findings are strengthened by creating twenty-two sensitivity analysis scenarios. In conclusion, the T2NN-CRITIC-MABAC hybrid method introduced in this study represents a methodologically diverse, fuzzy logic -based approach with practical implications for supplier selection in the micro mobility services sector. Its application and empirical testing contribute to the literature and provide valuable insights for decision -makers in the industry.
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    Sustainable brand logo selection using an AI-Supported PF-WENSLO-ARLON hybrid method
    (Pergamon-Elsevier Science Ltd, 2025) Kara, Karahan; Ergin, Elif Akagun; Yalcin, Galip Cihan; Celik, Tugce; Deveci, Muhammet; Kadry, Seifedine
    Brands use logos to reflect their identities and convey their company character to target customers. Logos not only represent brand value but also provide various information about brands. Companies aiming to emphasize sustainable brand value also seek to showcase this aspect in their logos. Therefore, sustainable brand design and development are preferred. The focus of this research is on sustainable brand development through Artificial Intelligence-supported logo design and developing a decision support system for selecting the best logo among Artificial Intelligence-generated logos. In this study, Artificial Intelligence-supported logo design is explained, and the Multi Attribute Group Decision Making approach is adopted for identifying the best logo among the obtained logos. The Picture Fuzzy - Weights by Envelope and Slope - Alternative Ranking using Two-Step Logarithmic Normalization hybrid method is proposed for logo decision-making, combining the Weights by Envelope and Slope criterion weighting method using picture fuzzy sets with Alternative Ranking using Two-Step Logarithmic Normalization alternative ranking methods. Additionally, the Picture Fuzzy Yager Weighted Average aggregation operator based on Yager t-norm and t-conorm operations is employed. The steps of the hybrid method are outlined, and its algorithm is formulated. To test the applicability and robustness of the algorithm, sustainable logo design for an architectural firm is developed and executed as a real-case study. The research concludes that Artificial Intelligence-supported sustainable logo design can be achieved, and the hybrid method can support determining the best logo based on expert opinions. Based on the research findings, the hybrid method is recommended for sustainable logo design and selection.