Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026Scopus SJRQ1Analyzing 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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İncele - MakaleAçık Erişim2026Scopus SJRQ1Assessing 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.
- MakaleAçık Erişim2025Scopus SJRQ2Forecasting 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%.
AtıfScopus16WoS19OpenAlex22İncele - MakaleAçık Erişim2025Scopus SJRQ1Evaluation 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.
AtıfScopus11OpenAlex11İncele - MakaleAçık Erişim2025Scopus SJRQ1Investigating 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.
AtıfScopus5WoS5OpenAlex7İncele - MakaleYalnız künye2024Scopus SJRQ2A 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.
AtıfScopus19WoS17OpenAlex18İncele - MakaleYalnız künye2024Scopus SJRQ2A risk assessment model with dependent failure modes for the manufacturing and design processes of medical equipment
Lo, Huai-Wei; Gul, Muhammet; Yucesan, Melih; Liaw, Ching-Fang
The reliability of medical equipment is paramount for surgical success. Manufacturers are continuously exploring risk assessment and improvement strategies to prevent equipment malfunctions during surgery. Failure mode and effects analysis (FMEA) can primarily be divided into two types: DFMEA (Design FMEA) and PFMEA (Process FMEA). Traditional FMEA serves as a practical tool for identifying and prioritizing key failure modes (FMs). However, past FMEA research has overlooked the interdependencies among FMs, treating them as independent entities, which does not align with real-world operations. This paper introduces a groundbreaking risk dependency assessment model aimed at addressing the influential relationships among FMs. The modified technique for defining the influential relationship of failure modes (modified TEDIR-FMs) is developed to compute the relative risk scores of different FMs accurately. At its core, this paper integrates concepts from multiple criteria decision-making (MCDM) techniques into FMEA, with a particular focus on the interdependencies of failure modes in the manufacturing and design manufacturing processes of medical equipment. The model’s application is demonstrated through a case study on a medical device for hernia surgery. The introduction of modified TEDIR-FMs not only enhances the accuracy of risk assessments but also offers a fresh perspective on risk management within the design and manufacturing processes. The research results indicate that the three failure modes with the highest risk are “adhesive does not deploy or only partially deploys from the dispenser,” “the mesh does not adhere to the adhesive,” and “the adhesive drips from the tip.” In the manufacturing and design processes of this device, greater attention should be given to improving the adhesive components.
AtıfScopus5WoS4OpenAlex2İncele - MakaleYalnız künye2024Scopus SJRQ1A comprehensive risk assessment framework for mooring risks at hydrocarbon berths using fuzzy rule-based Bayesian network and multi-attribute decision-making
Demirel, Hakan; Başhan, Veysi; Yucesan, Melih; Gul, Muhammet
Mooring operations -especially at hydrocarbon berths- are critical components of the marine and offshore industry. They usually involve securing the berths of ships carrying valuable cargo and ensuring the safety of personnel, assets, and the environment. For this purpose, a comprehensive risk assessment framework for mooring operations at hydrocarbon berths is proposed in this study. This framework helps evaluate risks using a rule-based Bayesian network. In the assessment of mooring risks, four risk parameters of severity, occurrence, detection, and maintenance are considered to construct the BN structure of each mooring risk. These parameters are weighted with the aid of the fuzzy Best Worst Method. Hereafter, a fuzzy rule-based system is constructed by incorporating BN to determine a risk priority score. The framework also develops mitigation strategies to maintain effective risk management for safe and secure maritime transportation. Sensitivity analyzes and comparison studies were conducted to test the validity of the proposed comprehensive risk management framework. The study reveals that the most critical risk is associated with Technical Failures (Q1) in the cluster pertaining to the automation of Quick Release Hooks. This risk stems from technical malfunctions in automation systems, encompassing sensors and control mechanisms, potentially resulting in the unintended release of mooring lines. The second highest priority risk is linked to Human Error (M1) in the mooring risks cluster, attributed to human errors such as inadequate training, miscommunication, and procedural mistakes during mooring operations, posing risks of accidents and damage to ships and infrastructure. Conversely, the least significant risk, Redundancy (Q5), focuses on redundancy. This risk is associated with automation and underscores the importance of implementing redundancy mechanisms to ensure the safe continuation of mooring operations in the face of system failures. In conclusion, the proposed comprehensive risk assessment framework offers a systematic approach to evaluate and prioritize mooring risks at hydrocarbon berths. The study’s findings emphasize the critical importance of addressing technical malfunctions in the automation of Quick Release Hooks and human errors during mooring operations. By identifying the most significant risks and developing mitigation strategies, this framework contributes to enhancing the safety and security of maritime transportation, particularly in the context of hydrocarbon berths.
AtıfScopus11WoS11OpenAlex13İncele - MakaleYalnız künye2024Scopus SJRQ1A stratified Bayesian decision-making model for occupational risk assessment of production facilities
Gul, Muhammet; Yucesan, Melih; Karci, Coskun
In the production industry, harmony and good management of the workplace environment, production machinery/vehicles, and workers are necessary to carry out production by occupational health and safety (OHS) principles. Therefore, occupational risk assessment (ORA) is crucial for manufacturing-based industries. When deciding on the prioritization of risks in ORA, adding to the analysis “how the parameters defining the risk changes in possible different states in the future” positively affects the soundness of decision-making. Therefore, this study aims to develop a unique ORA model handling future changes in the importance levels of risk parameters in the risk assessment process. To this aim, the concept of stratification and the best-worst method (BWM) are used together to determine the importance weights of the risk parameters in the ORA. In addition, the Bayesian version of BWM considers more than one expert's evaluations without losing information. In a nutshell, an approach called stratified Bayesian BWM (SBBWM) that can be used for further studies has been introduced to the literature. The technique determines the priority scores of each hazard by technique for order preference by similarity to the ideal solution sorting (TOPSIS-Sort) method. Thus, while determining each hazard's priority score and order, the class of this risk has also been determined. The proposed approach evaluated thirty-six risks encountered in manufacturing, storage, handling, and laboratory processes of a flour production facility. Control measures to be taken for each risk were also determined. Methodologically, various scenario analyses and sensitivity studies were conducted to reveal how the results changed in different conditions. The proposed approach provides a more comprehensive procedure for production facilities than traditional methods and avoids the deficiencies of traditional methods.
AtıfScopus18WoS16OpenAlex18İncele - MakaleAçık Erişim2024Scopus SJRQ1Integrated Approach Using Intuitionistic Fuzzy Multicriteria Decision-Making to Support Classifier Selection for Technology Adoption in Patients with Parkinson Disease: Algorithm Development and Validation
Ortiz-Barrios, Miguel; Cleland, Ian; Donnelly, Mark; Gul, Muhammet; Yucesan, Melih; Jiménez-Delgado, Genett Isabel; Nugent, Chris; Madrid-Sierra, Stephany
Background: Parkinson disease (PD) is reported to be among the most prevalent neurodegenerative diseases globally, presenting ongoing challenges and increasing burden on health care systems. In an effort to support patients with PD, their carers, and the wider health care sector to manage this incurable condition, the focus has begun to shift away from traditional treatments. One of the most contemporary treatments includes prescribing assistive technologies (ATs), which are viewed as a way to promote independent living and deliver remote care. However, the uptake of these ATs is varied, with some users not ready or willing to accept all forms of AT and others only willing to adopt low-technology solutions. Consequently, to manage both the demands on resources and the efficiency with which ATs are deployed, new approaches are needed to automatically assess or predict a user’s likelihood to accept and adopt a particular AT before it is prescribed. Classification algorithms can be used to automatically consider the range of factors impacting AT adoption likelihood, thereby potentially supporting more effective AT allocation. From a computational perspective, different classification algorithms and selection criteria offer various opportunities and challenges to address this need. Objective: This paper presents a novel hybrid multicriteria decision-making approach to support classifier selection in technology adoption processes involving patients with PD. Methods: First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) was implemented to calculate the relative priorities of criteria and subcriteria considering experts’ knowledge and uncertainty. Second, the intuitionistic fuzzy decision-making trial and evaluation laboratory (IF-DEMATEL) was applied to evaluate the cause-effect relationships among criteria/subcriteria. Finally, the combined compromise solution (CoCoSo) was used to rank the candidate classifiers based on their capability to model the technology adoption. Results: We conducted a study involving a mobile smartphone solution to validate the proposed methodology. Structure (F5) was identified as the factor with the highest relative priority (overall weight=0.214), while adaptability (F4) (D-R=1.234) was found to be the most influencing aspect when selecting classifiers for technology adoption in patients with PD. In this case, the most appropriate algorithm for supporting technology adoption in patients with PD was the A3 - J48 decision tree (M3=2.5592). The results obtained by comparing the CoCoSo method in the proposed approach with 2 alternative methods; (simple additive weighting and technique for order of preference by similarity to ideal solution) support the accuracy and applicability of the proposed methodology. It was observed that the final scores of the algorithms in each method were highly correlated (Pearson correlation coefficient >0.8). Conclusions: The IF-AHP-IF-DEMATEL-CoCoSo approach helped to identify classification algorithms that do not just discriminate between good and bad adopters of assistive technologies within the Parkinson population but also consider technology-specific features like design, quality, and compatibility that make these classifiers easily implementable by clinicians in the health care system.
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İncele - MakaleYalnız künye2024Scopus SJRQ1Evaluating sustainability of urban mobility of Asian cities: An integrated approach of interval type-2 fuzzy best-worst method and MULTIMOORA
Yucesan, Melih; Özkan, Barış; Mete, Suleyman; Gul, Muhammet; Özceylan, Eren
The motivation for sustainable urban mobility is driven by the need to create environmentally friendly, socially inclusive, economically viable, and healthier cities for current and future generations. It requires a comprehensive approach that combines infrastructure development, policy changes, behavioral shifts, and technological advancements to transform urban transportation systems. The importance of creating increasingly sustainable urban transportation is now greater than it has ever been. One of the attempts in this field is the sustainable urban transport index (SUTI) developed by the United Nations (UN). Although the SUTI has a comprehensive pool of criteria for the evaluation of mobility sustainability of cities, considering the criteria weights as equal and disregarding the uncertainties present in expert opinions is viewed as a significant drawback. To satisfy the aforementioned deficiency, a novel integrated approach including the best-worst method (BWM), multi-objective optimization by ratio analysis (MOORA) and MOORA plus full multiplicative form (MULTIMOORA) method is develloped and applied for the SUTI evaluation. In order to manage ambiguous linguistic approximations, the utilization of BWM is applied within the framework of interval type-2 fuzzy (IT2F) sets. While IT2F-BWM is used to prioritize the main and sub-indicators of SUTI, MULTIMOORA approach is applied to rank the Asian cities. The findings imply that the proposed approach not only provides a more scientific perspective but also provides the opportunity to perform sensitivity analysis on criteria weights. Therefore, the results obtained in this study are expected to help cities to create a road map for planning sustainable urban mobility (e.g. Sustainable Urban Mobility Plan).
AtıfScopus23OpenAlex26İncele - MakaleAçık Erişim2023An integrated Bayesian Best-Worst Method and GIS-based approach for offshore wind power plant site selection: A case study in North Aegean and Marmara Sea (Türkiye)
Gul, Muhammet; Yucesan, Melih; Konurhan, Zekeriya
In today’s world, renewable energy sources are in great demand due to the negative ef- fects of fossil fuels on the environment. Wind power plants are an important renewable energy source alternative to fossil fuel consumption. Offshore wind farms established in coastal areas and seas are used effectively in many parts of the world. The wind power plants, especially in the Northwest region of Turkey and the Aegean coasts, constitute an important potential. This study selects suitable sites for offshore wind farms in the Marmara Sea and North Aegean Coasts of Turkey by integrating the Bayesian Best-Worst method (BWM) and GIS. Bayesian BWM improves the traditional BWM integrating the preferences of multiple experts. In the study, 17 sub-criteria were determined under four main criteria of “technical”, “socio-economic”, “environment,” and “location”. Ex- perts’ judgments through the filled enabled the criterion weights to be obtained. The criteria weights found using the Bayesian-BWM model were integrated into the GIS, and suitable locations for the offshore wind farm were determined. Accordingly, the study area off the coasts of Aliağa, Bozcaada, and Gökçeada on the North Aegean coast, and the part south of the Marmara Sea and the area around Kapıdağ Peninsula are sugges- ted as suitable areas for wind power plants.
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İncele - MakaleAçık Erişim2023Scopus SJRQ1COVID-19 safe campus evaluation for universities by a hybrid interval type-2 fuzzy decision-making model
Baskak, Dilber; Ozbey, Sumeyye; Yucesan, Melih; Gul, Muhammet
The fight against the COVID-19 pandemic, which has affected the whole world in recent years and has had devastating effects on all segments of society, has been one of the most important priorities. The Turkish Standards Institution has determined a checklist to contribute to developing safe and clean environments in higher education institutions in Turkey and to follow-up on infection control measures. However, this study is only a checklist that makes it necessary for decision-makers to make a subjective evaluation during the evaluation process, while the need to develop a more effective, systematic framework that takes into account the importance levels of multiple criteria has emerged. Therefore, this study applies the best-worst method under interval type-2 fuzzy set concept (IT2F-BWM) to determine the importance levels of criteria affecting the “COVID-19 safe campus” evaluation of universities in the context of global pandemic. A three-level hierarchy consisting of three main criteria, 11 sub-criteria, and 58 sub-criteria has been created for this aim. Considering the hierarchy, the most important sub-criterion was determined as periodic disinfection. The high contribution of the interval-valued type-2 fuzzy sets in expressing the uncertainty in the decision-makers’ evaluations and the fact that BWM provides criterion weights with a mathematical optimization model that produces less pairwise comparisons and higher consistency are the main factors in choosing this approach. Simple additive weighting (SAW) has also been injected into the IT2F-BWM to determine the safety level of any university campus regarding COVID-19. Thus, decision-makers will be better prepared for the devastating effects of the pandemic by first improving the factors that are relatively important in the fight against the pandemic. In addition, a threshold value will be determined by considering all criteria, and it will prepare the ground for a road map for campuses. A case study is employed to apply the proposed model, and a comparison study is also presented with the Bayesian BWM to validate the results of the criteria weights.
AtıfScopus4WoS3OpenAlex2İncele - MakaleYalnız künye2023Scopus SJRQ4Development of a best-worst method based MCDM approach for solar power plant location selection: an application to Tunceli, Turkey
Aykac, Yunus Emre; Yucesan, Melih; Gul, Muhammet
Solar energy stands out with its increasing potential in the world. The decision to choose the location of a solar power plant is very important due to the investment return. In this study, a solar power plant site selection for Tunceli, Turkey is studied with a two-stage multi-criteria decision-making approach. In the first stage, the importance levels of the determined nine factors are obtained by the best-worst method (BWM). In the second stage, the most appropriate site is selected among the eight districts of Tunceli. As a result of the first stage, solar and fault line emerged as the most and least important factor, respectively. The second stage showed that Pertek district is the most suitable place. A comparative framework has been created including five different MCDM methods, which strengthens the final decision's accuracy and reveals whether each method's results are correlated.
AtıfScopus5OpenAlex3İncele - Kitap BölümüYalnız künye2023A GIS-Based BWM Approach for the Location Selection of Solar Power Plant in Tunceli Province (Turkey)
Konurhan, Zekeriya; Yucesan, Melih; Gul, Muhammet; Rezaei, J; Brunelli, M; Mohammadi, M
The world has recently tended to turn to renewable energy sources due to the serious negative impact of fossil fuels on the environment. One of the top priorities for solar energy is the selection of the most suitable location of the solar power plant to be established. In this study, the most suitable solar power plant location selection for Tunceli, a province located in the eastern part of Turkey, is studied with an approach based on Geographic Information System (GIS) and the best-worst method (BWM). A weighting was performed using the BWM model for 16 sub-criteria under four the main criteria of “climatic conditions, geographical conditions, land conditions, and location (distance to crucial points)”. Afterward, maps were created for these criteria, and a 6-point scale in the legends of each map for each sub-criteria was applied. Finally, the most suitable locations were determined with GIS-based calculations.
AtıfScopus6WoS5OpenAlex7İncele - Kitap BölümüYalnız künye2023A Multi-parameter Occupational Safety Risk Assessment Model for Chemicals in the University Laboratories by an MCDM Sorting Method
Gul, Muhammet; Yucesan, Melih; Karacahan, Mehmet Kayra
University laboratories are high-risk working environments where many chemicals coexist to conduct teaching and scientific research. The frequent occurrence of such chemical-related occupational accidents in recent times highlights the importance of ensuring these units’ safety in universities for researchers, students, workplaces, and institutions. In occupational safety risk assessment, very few studies have grouped risks. Many of them also ignored some important risk parameters. Overcoming these disadvantages, this study developed a multi-parameter and multi-criteria decision-making (MCDM) sorting-based methodology for the occupational safety risk assessment of chemicals in a university laboratory. First, six different risk parameters (probability, severity, exposure, detectability, worsening factor, sensitivity to non-usage of personal protective equipment) were determined. The weight value was calculated using the best–worst method (BWM). Then, a risk priority value and classification for each chemical with TOPSIS-Sort, the MCDM sorting method. Finally, some control measures are recommended to reduce the safety risk of the laboratory. The applicability of the proposed methodology has been tested with a real case study. The methodology is intended to adapt to university laboratories’ risk assessment and become a primary reference for university safety analysts.
AtıfScopus2OpenAlex1İncele - MakaleYalnız künye2023Scopus SJRQ1A holistic failure modes and effects analysis for university plastic injection laboratory under Bayesian Network
Yucesan, Melih; Gul, Muhammet; Pamučar, Dragan
University Research & Development (R&D) laboratories are facilities used for research and manufacturing activities in engineering and basic science fields. Various filling materials are used in plastic R&D laboratories to improve the mechanical properties of thermoplastics or to recycle waste materials. The use of filling materials causes changes in plastic production parameters. This study dealt with the risks arising from extrusion, injection and shredder in a plastic injection laboratory, which led to the inability to manufacture the appropriate product under a holistic methodology. The proposed holistic methodology merges the concepts of failure mode and effects analysis (FMEA), fuzzy best-worst method (FBWM), and a rule-based three-hierarchical Bayesian network (RB-THBN). First, 15 root risks have been identified under a three-stage hierarchy with the support of laboratory decision makers. Then, questionnaires were created using the FMEA concept. The importance values (degree of belief-DoB) of three different FMEA parameters (severity, occurrence and detection) by experts for each risk have been calculated with FBWM. And finally, the RB-THBN has been created, and the final risk values have been computed using the linear utility function for the network. The system has been analyzed via a Bayesian modeling software. A validity test and a control measures planning has also been executed. According to the results, the highest root risk has been determined as “E2-The prepared mixture is not homogeneous” with a crisp risk score of 0.639559. In addition, “Failure due to extrusion” has been determined as the highest risk category with a score of 0.564059. It was discussed in detail that these risks arise from homogenization and how the used mixture should be homogenized. A comparative study among traditional FMEA, FMEA extended under FBWM and the proposed approach is performed to observe the differences in final ranking of root risks and to highlight the arguments that strengthen the advantages of the proposed method over the other two.
AtıfScopus7WoS7OpenAlex8İncele - MakaleAçık Erişim2023Scopus SJRQ1Integrating stratified best–worst method and GIS for landslide susceptibility assessment: a case study in Erzurum province (Turkey)
Konurhan, Zekeriya; Yucesan, Melih; Gul, Muhammet
Landslides are among the most destructive geological disasters that seriously damage human life and infrastructures. Landslides mainly occur in mountainous regions around the world. One of the key processes to reduce these damages is to uncover landslide-exposed areas through different data-driven methods such as Geographical Information System (GIS) and multi-criteria decision-making (MCDM). In the literature, there are many studies developed with these fundamental tools. In this study, unlike the literature, a new landslide susceptibility assessment model is proposed by integrating GIS with the stratified best–worst method (S-BWM). This model has four main dimensions and 16 sub-dimensions under topography, environment-land, location, and hydrological factors, weighted with the S-BWM. A network was created considering the different states that may arise in the importance weights of these dimensions in the future. The transition probabilities of these states were predicted and injected into the classical BWM. Then, maps were created for these dimensions and classifications for each sub-dimension according to the map characteristics. Finally, the most susceptive landslide locations were determined with GIS-based calculations. To demonstrate the model’s applicability, a case study was conducted for the Erzurum region, one of Turkey’s landslide-prone regions. In addition, besides the landslide map, an analysis and discussion about the spatial distribution of susceptibility classes was presented, contributing to the study’s robustness. In the results of landslide susceptibility analysis, landslides are higher in the range of about 1600–2500 m. Approximately 42% (35.59 sq. km) of the study area has high landslide susceptibility, while 58% (64.41 sq. km) has medium and low landslide susceptibility.
AtıfScopus19WoS17OpenAlex18İncele - MakaleYalnız künye2023Scopus SJRQ1An integrated hybrid MCDM approach to evaluate countries’ COVID-19 risks
Mete, Suleyman; Yucesan, Melih; Gul, Muhammet; Ozceylan, Eren
To enable planning and early reaction efforts, it is important to identify the nations most at risk from COVID-19's health and humanitarian effects, which could exceed present national response capabilities. INFORM COVID-19 Risk Index developed by European Commission is one of the initiatives to meet the aforementioned need. However, the Index has 40 conflicting and comprehensive indicators (e.g., health system capacity, hygiene, vulnerability) and different countries that lead to a need for multicriteria decision-making (MCDM) approaches. This paper offers an integrated MCDM approach with two phases to re-assess the countries' COVID-19 risks based on INFORM COVID-19 Risk Index. In the first phase, the 40 indicators are prioritized using the Entropy-based multi-choice best-worst method (MC-BWM) to provide multiple viewpoints for the preferences values. In the second phase, 29 countries from different geographical regions are ranked using the COPRAS (Complex Proportional Assessment) method. Applying these three methods together on the COVID-19 risk assessment of countries is the most significant novelty for the relevant literature. The proposed approach has undergone a thorough comparative evaluation with the released INFORM COVID-19 Risk Index, and recommendations are provided based on different scenario analyses.
AtıfScopus21WoS16OpenAlex18İncele - MakaleAçık Erişim2023Scopus SJRQ2A Fuzzy Hybrid MCDM Approach for Assessing the Emergency Department Performance during the COVID-19 Outbreak
Ortíz-Barrios, Miguel; Jaramillo-Rueda, Natalia; Gul, Muhammet; Yucesan, Melih; Jiménez-Delgado, Genett; Alfaro-Saíz, Juan-José
The use of emergency departments (EDs) has increased during the COVID-19 outbreak, thereby evidencing the key role of these units in the overall response of healthcare systems to the current pandemic scenario. Nevertheless, several disruptions have emerged in the practical scenario including low throughput, overcrowding, and extended waiting times. Therefore, there is a need to develop strategies for upgrading the response of these units against the current pandemic. Given the above, this paper presents a hybrid fuzzy multicriteria decision-making model (MCDM) to evaluate the performance of EDs and create focused improvement interventions. First, the intuitionistic fuzzy analytic hierarchy process (IF-AHP) technique is used to estimate the relative priorities of criteria and sub-criteria considering uncertainty. Then, the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL) is employed to calculate the interdependence and feedback between criteria and sub-criteria under uncertainty, Finally, the combined compromise solution (CoCoSo) is implemented to rank the EDs and detect their weaknesses to device suitable improvement plans. The aforementioned methodology was validated in three emergency centers in Turkey. The results revealed that the most important criterion in ED performance was ER facilities (14.4%), while Procedures and protocols evidenced the highest positive D + R value (18.239) among the dispatchers and is therefore deemed as the main generator within the performance network.
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