Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYalnız künye2026Scopus SJRQ1Enhancing 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.
- Kitap BölümüYalnız künye2025Bayesian 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.
- MakaleAçık Erişim2025Scopus SJRQ1Selection 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.
AtıfScopus5WoS4
İncele - BildiriYalnız künye2024Scopus SJRQ4Constructing the Criteria in Determining the Product Groups for Agriculture 4.0 Applications
Erdoğan, Melike; Konurhan, Zekeriya; Yücesan, Melih; Gül, Muhammet; Mirzazadeh, A.; Molamohamadi, Z.; Erdebilli, B.; Babaee Tirkolaee, E.; Weber, G.-W.
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.
- MakaleAçık Erişim2024Scopus SJRQ1Integrating discrete-event simulation and artificial intelligence for shortening bed waiting times in hospitalization departments during respiratory disease seasons
Ortiz-Barrios, Miguel; Ishizaka, Alessio; Barbati, Maria; Arias-Fonseca, Sebastián; Khan, Jehangir; Gul, Muhammet; Yücesan, Melih; Alfaro-Saíz, Juan-Jose; Pérez-Aguilar, Armando
Seasonal Respiratory Diseases (SRDs) usually produce a heightened number of Emergency Department (ED) attendances due to their rapid dissemination within the community and the ineffective prevention measures. Such a context requires effective management of the emergency care processes to provide in-time diagnosis and treatment to infected patients. Nonetheless, EDs have evidenced severe operational deficiencies during these periods, thereby provoking extended bed waiting times in Hospitalization Departments (HDs). Therefore, this paper presents a hybrid approach merging Artificial Intelligence (AI) and Discrete-Event Simulation (DES) to shorten the bed waiting times in HDs considering patient records collated in the first emergency care stages. First, we implemented Random Forest (RF) to estimate the probability of respiratory worsening based on sociodemographic and clinical patient data. Second, we inserted these probabilities into a DES model mimicking the emergency care from the admission to the HD. We then pretested different HD configurations and strategies seeking to reduce the HD bed waiting time. A case study of a European hospital group was used to validate the suggested framework. The AI-DES model enabled decision-makers to identify an improvement proposal with hospitalization bed waiting time lessening, oscillating between 7.93 and 7.98 h.
AtıfScopus16OpenAlex17İncele - MakaleAçık Erişim2023Scopus SJRQ3Investigation of issues affecting thermal comfort in water system underfloor heating applications of buildings with Bayesian networks
Erkol, Samet; Yücesan, Melih; Gül, Muhammet; Güneri, Ali Fuat
Thermal comfort is related to the stability of the ambient temperature. Constant changes in ambient temperature appear as a situation that negatively affects comfort. The selected building systems must be arranged to maintain this stability for the continuity of thermal comfort. In this study, issues affecting thermal comfort in water system underfloor heating applications of buildings are handled and analyzed using the Bayesian Network modeling methodology. Visual examples of the problems encountered in field applications are also given. Three different scenarios are tested with the constructed Bayesian Network model. In the first scenario, assumed that mechanical project failures were prevented. In this case, it was observed that the failure rate decreased by about 5%. In the second scenario, assumed that mechanical application failures are prevented along with mechanical project failures. The failure rate decreased by 11% compared to the first situation. The third scenario assumed that the mechanical project preparation phase was concluded without any problems, the mechanical project was implemented without any failures, and the mechanical system was commissioned without any problems. In the last scenario, the failure rate decreased by 14% compared to the first case, and the probability of not providing thermal comfort remained at 2%. As a result of these three scenarios, the possibility of not providing thermal comfort in the underfloor heating system is detailed and interpreted.
- Kitap BölümüYalnız künye2023Avalanche Risk Analysis by a Combined Geographic Information System and Bayesian Best-Worst Method
Konurhan, Zekeriya; Yücesan, Melih; Gul, Muhammet
The formation of avalanches is related to the land structure, climatic conditions, and snow cover. It is usually seen in mountainous and sloping terrains without vegetation. In Turkey, especially in Eastern Anatolia and the Black Sea Region, which have high elevations, avalanche events are observed. This study aims to perform a risk analysis by integrating the Bayesian Best-Worst method (BWM) and Geographic Information System (GIS) for Tunceli province, which is the scene of significant avalanche events. Bayesian BWM is a method that improves the original BWM by effectively integrating the preferences of multiple experts. In the study, 16 sub-criteria, such as elevation, slope, and the number of snowy days, were determined, and experts evaluated these criteria through questionnaires created. The weight of each criterion were calculated using the Bayesian-BWM. By integrating the criteria weights from the Bayesian-BWM model into GIS, the risky places for natural avalanche disasters in Tunceli province were determined, according to which the risk in the northern part of the study area is identified as high.
AtıfScopus5İncele - MakaleAçık Erişim2023Scopus SJRQ1A comparative framework for criticality assessment of strategic raw materials in Turkey
Göçmen Polat, Elifcan; Yücesan, Melih; Gül, Muhammet
The rapid development of innovative technologies, population growth, shifting resource management patterns, and decarbonization challenges are expected to increase the demand for raw materials labeled as critical in the following years. The increasing concerns have led to evaluate the critical raw materials (CRMs) with high economic importance and supply risk. In this context, this paper aims to develop a conceptual methodology to identify CRMs in Turkey. The methodology integrates a multi-criteria decision-making method (MCDM) to provide the raw material criticality matrix regarding various criteria, the EU (European Union) method, which represents supply risk and economic importance indicators, and the time series analysis method, in which dynamic evaluation of materials over time instead of the static indicators. We first provide a Bayesian Best-Worst method (B-BWM) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) based model for the criticality matrix, which enables evaluations of participated experts to be combined to reveal the weights of the criteria and sub-criteria from a probabilistic point of view without loss of knowledge. The main and sub-criteria include supply risk and economic importance, which are frequently studied in the literature, but also environment, price, secondary production, and socio-economic indicators are evaluated in the criticality matrix. Then, exponential smoothing forecast, one of the Time Series Analysis models, which determine the element criticality in the perspective of future years with the data of import figures of Turkey, is superior to the other models. Finally, with some modifications, the EU criticality methodology is used to quantify the raw materials regarding each material associated with the sector applications and their value-added and supply disruptions. Thus, a comprehensive criticality list integrating the results of the three mentioned methods is important because Turkey's raw materials characterize many sectors. The findings should also interest practitioners and researchers as the detailed assessment of raw material criticality ensures a sufficient database for future studies.
AtıfScopus24WoS14İncele - MakaleYalnız künye2022Scopus SJRQ1A calibrated Fuzzy Best-Worst-method to reinforce supply chain resilience during the COVID 19 pandemic
López, Cristina; Ishizaka, Alessio; Gul, Muhammet; Yücesan, Melih; Valencia, Daniela
Making decisions require increasingly more sophisticated methods. The Best-Worst method was developed recently to find a good balance between the amount of information required and its consistency. The Fuzzy Best-Worst method was then proposed to integrate the uncertainty and imprecision of evaluations. In this paper, we propose the Calibrated Fuzzy Best-Worst Method, which enables us to define personalized fuzzy numbers. This new method was applied to the real case study of a global fashion supply chain suffering a dramatic drop of demand due to the COVID-19 shocks. Since its upstream was leading to a ripple effect on the rest of the supply chain stages, our study focused on evaluating the viability of its distribution channels based on weighted resilience capabilities and performance indicators. The proposed method allowed being more precise than the traditional Best-Worst method because it personalizes the uncertainty and subjectivity. The results revealed that cash flow, revenues, and inventory turnover were the most important performance indicators in the supply chain resilience. Financial strength, adaptability, and market position were shown to be the most critical resilience capabilities in preserving supply chain survivability with low-demand items and longer disruptions. Their effects explain why e-commerce was the most viable distribution channel.
AtıfScopus26WoS24OpenAlex29İncele - Derleme MakaleYalnız künye2018Bulanık dematel yöntemi ile hidroelektrik santrallerin işletilmesindeki risklerin değerlendirilmesi
Kahraman, Gökhan; Yücesan, Melih
Yenilenebilir enerji alanında sistem kurulumu ve işletimi sırasında meydana gelebilecek çevreselrisklerin büyüklüğünü ve ihtimalini azaltmak çok önemlidir. Risk analiz metotları mühendislikuygulamalarındaki riskin analizi ve azaltılması için yaygın bir şekilde kullanılan karar destek araçlarıkabul edilir. Bu metodların entegrasyonu sayesinde daha yetkin ve verimli açıklamalar ve sonuçlar eldeetmek mümkündür. Bu çalışmada hidroelektrik santrallerde işletme esnasında gerçekleşen hasarlarındeğerlendirilmesinde bulanık DEMATEL (Karar Verme Örneği ve Değerlendirme Labaratuvarı) metoduincelenecektir. DEMATEL metodu neden sonuç ilişkisi içinde muhtemel riskleri tanıtıp analiz etmeimkanı sağlar. Çalışmanın sonuç kısmında belli güvenlik önlemlerini artırma ve riskleri azaltmaihtiyaçlarına dair öneriler yer almaktadır. Bu çalışmanın hidroelektrik santrallerdeki iş güvenliğikonusuna katkı sağlayacağı can kayıplarını ve ekonomik kayıpları azaltacağı öngörülmektedir.
