Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2026Scopus SJRQ1/r/munzur/1234
  Enhancing waiting time performance of clinical laboratories for pregnant patients with seasonal respiratory diseases: a simulation-based Lean Six Sigma approach

  Ortíz-Barrios, Miguel; Celani de Souza, Helder Jose; Petrillo, Antonella; Gul, Muhammet; Yücesan, Melih; Navarro-Jiménez, Eduardo; Alfaro-Saíz, Juan-José; Ariza-Ortíz, Andrés

  Purpose – Clinical laboratory performance is critical in managing seasonal respiratory diseases (SRDs); however, prolonged waiting times can lead to patient dissatisfaction and nonquality costs. This challenge is particularly acute for pregnant patients, who face increased health risks from diagnostic delays. This study aims to improve waiting times before sample collection in clinical laboratories serving pregnant patients with SRDs by integrating Lean Six Sigma (LSS) and Discrete-Event Simulation (DES). Design/methodology/approach – The proposed framework was validated in a public clinical laboratory using the DMAIC (Define–Measure–Analyze–Improve–Control) methodology. DES was embedded within the Analyze and Improve phases to model patient flow and assess improvement scenarios before implementation. Findings – The integrated LSS–DES approach resulted in a significant reduction in waiting times. During SRD peak periods, waiting time before sample collection for pregnant patients decreased from 1.01 to 0.10 h per patient. These results demonstrate the framework's effectiveness and practical usefulness for decision-makers seeking performance improvements in resource-constrained healthcare environments. Originality/value – This study enhances existing literature by integrating LSS and DES to address laboratory performance challenges associated with SRDs. The combined LSS-DES used for pregnant patients during outbreak periods is novel. The study offers contextual innovation for a vulnerable population, methodological advancement through embedding DES within the DMAIC cycle, and practical laboratory validation. The framework is particularly valuable for low- and middle-income countries (LMICs), where scalable solutions are essential.

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• MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/1015
  Analyzing Failures In Wind–Solar Hybrid Energy Systems Using a Fuzzy-Based BWM-MARCOS Approach: Challenges and Solutions

  Başhan, Veysi; Yucesan, Melih; Demirel, Hakan; Gul, Muhammet

  The reliability of hybrid renewable energy systems (HRES) depends heavily on the identification and management of potential failure modes. This study employs a fuzzy-based BWM-MARCOS approach to systematically analyze and prioritize failure modes within wind–solar hybrid systems. The model aims to prioritize the failures considering four important risk parameters: (1) severity of the failure on system, staff, and failure, (2) failure occurrence chance, (3) effort and ease of detecting the cause of the failure, and (4) economic impact of the failure. In this context, four key risk indicators were evaluated to rank failures, revealing that SP1 (cell damage), ESS1 (battery degradation), and WT11 (battery fire) are the most critical due to their potential impact on system operations. Sensitivity analysis confirmed the stability of these rankings under varying parameter weights. Additionally, cross-method validation using fuzzy TOPSIS, SAW, and MARCOS demonstrated high correlation coefficients, underscoring the reliability of the results. Tailored mitigation strategies, including advanced diagnostics, durable materials, and robust monitoring systems, are proposed to address these critical failures. While the current methodology applies to various HRES configurations, future research should incorporate real-world operational data and machine learning techniques to enhance predictive capabilities and dynamic risk management.

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  AtıfScopus7·WoS7·OpenAlex8

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• MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/943
  Assessing interrelationships among circular economy implementation strategies

  Ahmadi, Hadi Badri; Gul, Muhammet; Gupta, Himanshu; Yucesan, Melih; Liou, James J.H.

  Strategies for implementing a circular economy (CE) are designed to enhance resource efficiency, minimize the consumption of raw materials, and decrease waste. Although many articles have investigated CE implementation strategies, research focusing on the interactions and interrelationships among these strategies remains scarce. To address this issue, this study develops an evaluation framework, with the aim of assessing the interactions among strategies for CE implementation in the context of the manufacturing sector of an emerging economy using Decomposed Fuzzy Decision-Making Trial and Evaluation Laboratory (DF-DEMATEL) methodology. The findings suggest that “Investment in green technology” is the most critical strategy and should receive the greatest attention for successfully implementing CE. This research helps industrial decision-makers and specialists to invest in the most critical strategies within their supply chains and move towards sustainable development.

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• Kitap BölümüYalnız künye2025/r/munzur/2038
  Bayesian Best–Worst Method Application for Assessing the Potential Effecting Areas of Climate Change: A Case Study in Turkey

  Konurhan, Zekeriya; Yücesan, Melih; Gul, Muhammet

  ‘Climate Change’ is a term used to refer to global changes in the Earth's average temperature and the consequences of changes in temperature. More clearly, it demonstrates changes in temperature and weather patterns over time. The emergence of this situation directly or indirectly affects irreparable damages and major/minor disasters in many areas. Determining the answer to the question of which of these areas has an impact at what level of importance will provide guidance on which one should be prioritized in carrying out an effective treatment. Therefore, in this study, this problem is ad-dressed as a multi-criteria decision problem using Bayesian Best–Worst Method. The potential impacts have been categorized under 8 main areas and weighted via participating of 8 competent experts (geography, emergency disaster management, geomorphologist, climate scientist etc.) in the field of climate change. The study results determined that B (Ecosystems and Water Resources) has the most critical impact area, with 0.200. The least essential domain is G4 (Adaptation and Resilience Strategies in the Tourism Industry), which weighs 0.012. The results showed that they provide important clues regarding which impacts should be addressed first.

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• MakaleAçık Erişim2025Scopus SJRQ2/r/munzur/1927
  Forecasting daily natural gas consumption with regression, time series and machine learning based methods

  Yucesan, Melih; Pekel, Engin; Celik, Erkan; Gul, Muhammet; Serin, Faruk

  An effective short-term natural gas forecasting method contributes to social contributions and allows industrial chain elements to function effectively and minimize economic losses. We dealt with a comparative framework on the applicability of different methods in daily natural gas service (NGS) consumption forecasting. In this context, time series, machine learning, evolutionary and population-based approaches, and their hybrid versions are applied to the NGS data. Hybridized approaches are tested in the scope of NGS consumption forecasting for the first time in the literature in this study. The case of Turkey is handled, and its NGS data is used to demonstrate the comparative framework’s applicability. The comparative study is assessed in the lights of common forecasting accuracy measures of mean absolute percentage error (MAPE), R-squared (R2), and mean squared error (MSE). According to each method’s results, the seasonal autoregressive integrated moving average with exogenous regressors (SARIMAX) and artificial neural network (ANN) hybrid model provides the most dominant performance with respect to MAPE. The lowest error was obtained with a MAPE value of 0.357 in this hybrid model constructed under seven neurons in its ANN structure. This model is followed by another hybrid model, autoregressive integrated moving average (ARIMA)-ANN, with a MAPE value of 0.5 under nine neurons in terms of accuracy performance. The worst performance value belongs to the Genetic algorithm-ANN hybrid model with a MAPE value of approximately 26%.

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  AtıfScopus16·WoS19·OpenAlex22

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• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/950
  Evaluation of Green Deal compliance performance with a hybrid comparative multi-attribute decision model

  Karadag, Mehmet; Gul, Muhammet; Yucesan, Melih; Ortiz-Barrios, Miguel; Ishizaka, Alessio; Khan, Jehangir

  The environmental and economic transformation initiated by the European Green Deal is influencing not only EU member states, but also other countries engaged in trade with the EU. This transformation mandates a shift towards sustainable practices in sectors such as agriculture, industry, transportation, energy, and finance. Compliance with the Green Deal is increasingly crucial for countries, particularly those with significant commercial ties to the EU. However, differing levels of development lead to varying degrees of implementation. While studies on the Green Deal compliance of EU member states exist, an evaluation of Türkiye—a candidate EU member with substantial trade relations—would address a significant gap in the literature. This study introduces a novel hybrid comparative multi-attribute decision model that combines subjective (Best Worst Method and its derivatives) and objective (Entropy) weighting to determine the relative importance of 14 sub-criteria under three main criteria: “Reducing” the impacts of climate change, “Protecting” the planet and health, and “Enabling” a green and just transition. Using the Ratio Product Model (RPM), Türkiye's Green Deal compliance from 2017 to 2021 was evaluated. Sensitivity analysis was conducted to validate the robustness of the findings, and comparisons were made using various multi-attribute decision models, including SAW, TOPSIS, and VIKOR, as well as benchmarking against EU countries. The criteria weights, derived from a combination of subjective and objective methods, highlighted the dominant importance of the “Reducing” criterion across all methods. The “Enabling” and “Protecting” criteria showed slight variations in importance depending on the method. The RPM results indicated that 2021 was Türkiye's best-performing year in terms of Green Deal compliance, while 2017 showed the weakest performance. Despite some fluctuations between 2018 and 2019, Türkiye demonstrated significant progress overall. The study not only advances the methodology for evaluating Green Deal compliance but also provides actionable insights for policymakers and industry stakeholders, enabling more informed decision-making. By highlighting Türkiye's trajectory, the findings offer strategic guidance for non-EU countries aiming to align with Green Deal objectives, fostering sustainable development and strengthening economic ties with the EU.

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  AtıfScopus11·OpenAlex11

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• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/884
  Selection of the central composting facility for organic wastes through the Bayesian best worst method and Geographic information system incorporation: The case of Istanbul

  Güler, Mustafa; Samastı, Mesut; Yücesan, Melih; Gül, Muhammet; Çelik, Erkan; Ciner, Miraç Nur; Algorabi, Ömer

  The rapid increase in municipal solid waste (MSW) generation, driven by population growth and unplanned urbanization, has made the location of organic waste collection and disposal facilities a critical issue for metropolitan cities. Improper waste management practices threaten environmental sustainability, human health, and urban futures. This study applies multicriteria decision-making (MCDM) methods to identify the optimal placement for a central composting facility in Istanbul. A novel MCDM mechanism was developed, building on existing methods and addressing a gap in the literature. While determining the criteria, academic studies on this subject were researched in depth and the criteria used in these studies were brought together to obtain 28 criteria. The study assessed 28 criteria using the Bayesian Best-Worst Method (BWM) to determine their relative importance. These weights were then integrated into a Geographic Information System (GIS) to produce a suitability map, highlighting the most appropriate locations for the facility. Based on this analysis, the Çatalca Karacabey region was identified as the optimal site, ensuring reduced environmental impact and improved efficiency in organic waste management. The study contributes both theoretically and practically to the field, offering a robust framework for sustainable waste facility siting and providing actionable insights for urban governance. The study provides valuable insights for facility siting and urban governance, while highlighting areas for future research.

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  AtıfScopus5·WoS4

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• MakaleAçık Erişim2025Scopus SJRQ1/r/munzur/793
  Investigating forest fire causes through an integrated Bayesian network and geographic information system approach

  Konurhan, Zekeriya; Yucesan, Melih; Gul, Muhammet

  Forests are among the most critical natural resources worldwide and are essential for a sustainable ecosystem. In recent years, climate change and human activities have increased their impact on forests, and forest fires-especially in summer- have caused significant forest losses globally. This paper integrates Bayesian Network (BN) and Geographic Information Systems (GIS) to pinpoint the possible causes of forest fires and analyze their complex interactions. The study used data from 1465 fires between 2017 and 2022 in Muğla province in southwestern Türkiye. Within the scope of the study, 11 variables were chosen, such as elevation, slope, aspect, wind speed, population density, and road network, to build a BN model that combines physical and human geographical features. These variables overlapped using the pairwise intersect tool from ArcGIS Pro during the BN model setup, and probability values were calculated. The overall probability of fire in the BN model was determined to be 0.81, with probabilities ranging from 0.81 to 0.56 in low-altitude, moderately sloped, and south-facing areas. Scenario analyses examined fire risk under different conditions, highlighting the most influential variables for fire prevention efforts. The study identified fire-prone areas as spatial data, revealing that densely forested coastal and certain inland regions are at higher risk, whereas bare high-altitude areas with steep slopes pose lower risk. The BN model can be further enhanced by incorporating additional variables, making it a valuable tool for future fire risk assessment and mitigation strategies research.

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  AtıfScopus5·WoS5·OpenAlex7

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• BildiriYalnız künye2024Scopus SJRQ4/r/munzur/2230
  Constructing the Criteria in Determining the Product Groups for Agriculture 4.0 Applications

  Erdoğan, Melike; Konurhan, Zekeriya; Yücesan, Melih; Gül, Muhammet

  With Agriculture 4.0, the use of techniques such as sensors, robots, artificial intelligence, and machine learning in agriculture has started. It is aimed to increase productivity in agriculture by reducing food loss and waste through Agriculture 4.0. It is a critical decision to determine which products should be handled first for Turkey to benefit from the advantages of Agriculture 4.0 as soon as possible compared to developed countries in the field of agriculture. At this point, the problem of which factors should be addressed in the determination of product & product groups arises. To handle this, in this study, a multi-criteria analysis has been applied to prioritize the factors that should be considered in the determination of the critical fruit and vegetable group for export, which should be considered as a priority within the scope of Agriculture 4.0. In this context, a multi-criteria analysis has been carried out by adopting the Bayesian Best Worst Method BWM (B-BWM), which is an improved version of a pairwise comparison-based BWM method and applied to group decisions. As a result of the analysis, the most important and least important criteria to be used in determining which products or product groups are more suitable for Agriculture 4.0 applications in Turkey and which should be invested in priority have been determined.

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• MakaleYalnız künye2024Scopus SJRQ2/r/munzur/2169
  A fuzzy Bayesian network risk assessment model for analyzing the causes of slow-down processes in two-stroke ship main engines

  Başhan, Veysi; Yucesan, Melih; Gul, Muhammet; Demirel, Hakan

  This paper presents a risk assessment approach for analyzing the causes of malfunction-related main engine slowdowns. A fuzzy Bayesian Network-based methodology is used to assess the factors contributing to the engine’s slow-down processes. The model addresses the complexity and uncertainty inherent in maritime operations with fuzzy sets where numerous interrelated factors can affect engine performance, and the Bayesian network to capture probabilistic dependencies. It considers various potential causes of the slow-down of ship engines that the manufacturer provides. Results demonstrate the model's ability to identify the influential factors leading to engine slow-down events and quantify the overall risk. Integrating fuzzy logic and Bayesian Networks comprehensively assesses relevant risk factors. It enables maritime stakeholders to manage engine performance and improves operational safety proactively. Findings can inform decision-makers, enabling the implementation of targeted maintenance strategies, fuel quality control measures, and crew training programs in the maritime industry.

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  AtıfScopus19·WoS17·OpenAlex18

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