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    Analyzing the impact of artificial intelligence on operational efficiency in wastewater treatment: a comprehensive neutrosophic AHP-based SWOT analysis
    (Springer, 2024) Yalcin, Selin; Ayyildiz, Ertugrul
    The escalating global challenges of population growth, climate crisis, and resource depletion have intensified water scarcity, emphasizing the critical role of wastewater treatment (WWT) in environmental preservation. While discharging untreated wastewater poses extinction risks to various species, effective WWT operations are indispensable for ecosystem continuity and sustainable water sources. Recognizing the complexity of WWT management, this study delves into the potential of artificial intelligence (AI) in strategic planning and decision-making within the WWT domain. Through a comprehensive SWOT analysis, this study evaluates the strengths, weaknesses, opportunities, and threats associated with AI integration in WWT processes. Utilizing the SWOT analysis framework, key criteria are identified, and their importance weights are assessed via the interval-valued neutrosophic analytical hierarchy process (IVN-AHP). According to analysis, the strengths in WWT are crucial, but potential opportunities and threats should not be ignored. The results of the study highlight several key findings regarding the integration of AI in WWT processes. While concerns about the reduction in human resources and potential unemployment, as well as the activation time and high energy consumption of AI systems, are identified as significant challenges, the study underscores the success of AI in data analytics as a strong aspect. Specifically, advanced data analysis techniques and the ability to proactively prevent problems emerge as important strengths of AI in WWT. WWT operators and practitioners are encouraged to prioritize the adoption of advanced data analysis techniques and proactive problem-solving strategies to maximize the effectiveness of AI integration in WWT processes. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.
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    Assessing of causes of accidents based on a novel integrated interval-valued Fermatean fuzzy methodology: towards a sustainable construction site
    (Springer London Ltd, 2023) Bouraima, Mouhamed Bayane; Gore, Abibata; Ayyildiz, Ertugrul; Yalcin, Selin; Badi, Ibrahim; Kiptum, Clement Kiprotich; Qiu, Yanjun
    The statistics pertaining to accidents occurring at construction sites underscore the pressing requirement for a substantial and timely reevaluation of safety measures within the construction sector. Accidents do not occur randomly; rather, they arise from the presence of unsafe actions, hazardous conditions, or a combination of both. The majority of accidents stem from a combination of contributing causes, including unsafe acts and conditions. To enhance safety performance on a broader scale, this study undertakes an extensive analysis to identify these causes, evaluate their importance, and determine the countries that are most and least impacted by them. Ten African countries were selected as potential alternatives based on the frequency of infrastructure construction projects. A thorough review of existing literature was conducted to establish a three-level criteria framework. The framework was further refined through the Modified Delphi method to gather expert opinions. The weights assigned to the criteria were determined using the interval-valued Fermatean fuzzy analytical hierarchy process methodology. The Technique for Order Preference by Similarity to Ideal Solution method under the same fuzzy environment was then employed to rank the alternative countries. A sensitivity analysis was carried out to assess the robustness of the proposed methodology. The analysis revealed that the main cause of accidents was attributed to poor management, as it included ineffective project supervision, inadequate safety policies, poor organizational structure, and inappropriate scheduling/planning as the main underlying sub-factors. Additionally, it was observed that the sixth alternative country exhibited the highest susceptibility to accidents occurring at construction sites.
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    Prioritizing freight carrier selection factors with the best worst method
    (Springer, 2024) Yalcin, Selin; Ayyildiz, Ertugrul
    Efficient freight carrier selection is pivotal to modern logistics and supply chain management, demanding a systematic approach to navigate the complexity of factors such as cost, reliability, sustainability, and collaboration. This study aims to prioritize these factors using the Best Worst Method (BWM), offering a novel framework for enhancing decision-making precision in carrier selection. Drawing insights from literature, industry expertise, and stakeholder perspectives, a criteria set is identified. Utilizing the BWM, we established a structured evaluative framework where expert judgments and pairwise comparisons helped identify the best and worst criteria for each factor, assigning appropriate priority weights. Cost emerged as the most important criterion, highlighting its significance in the decision-making process. The results show that cost, reliability, and safety are the top priorities for freight carrier selection, with sustainability and collaboration being less critical but still significant. This study has profound implications for logistics practitioners, supply chain managers, and decision architects engaged in carrier selection processes. Moreover, the study enriches the theoretical understanding of carrier selection within a multifaceted criteria context. This study offers a novel framework that elevates decision-making precision in carrier selection.
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    Prioritizing Vulnerability Factors of Global Food Supply Chains by Fermatean Fuzzy Analytical Hierarchy Process
    (Sciendo, 2024) Yalcin, Selin; Ayyildiz, Ertugrul
    In response to heightened competition arising from globalization, companies are crafting strategies to sustain their operations. However, these strategies also introduce risks that require meticulous management. The onset of the COVID-19 pandemic has exacerbated disruptions in supply chains, including the vulnerable food supply chain (FCS), strained further by escalating food prices and resource depletion in recent times. Within this context, the vulnerability of global FSCs has escalated significantly due to government-imposed lockdowns during the pandemic. This study aims to comprehensively investigate the multifaceted disruptions in global FSCs caused by the COVID-19 pandemic. By delving deep into the complexities of these disruptions, it seeks to uncover the key factors contributing to the vulnerability of supply chains. Employing a blend of literature review and expert opinions, the study identifies and prioritizes factors using the Fermatean Fuzzy Analytical Hierarchy Process (FF-AHP). A two-level criteria framework consisting of three main criteria and eleven sub-criteria has been developed, taking into account expert recommendations and previous studies. According to the results obtained, it has been revealed that the Managerial factors within the main criteria are the most significant factors in the fragility of the FSC. Among these factors, it has been observed that Technology, Corporation, and Inventory Management are the leading criteria causing to the vulnerability of the FSC. This is the first study to investigate the vulnerabilities of FSC using fuzzy logic. The research underscores the imperative of comprehensive risk management strategies that encompass all stakeholders within the supply chain, particularly during unanticipated crises like pandemics.