Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleYalnız künye2019Scopus SJRQ1A decision-support system based on Pythagorean fuzzy VIKOR for occupational risk assessment of a natural gas pipeline construction
Mete, Suleyman; Serin, Faruk; Oz, Nurdan Ece; Gul, Muhammet
Natural gas pipeline construction projects are major projects between countries that include onshore and offshore sections. Aim of these projects is to supply the natural gas from one place to another. Safety of workplace, environment and employers is also another important argument. In this respect, it is essential to conduct an occupational risk assessment (ORA) study to eliminate the risks that may arise around the pipeline. Application of multi-criteria decision analysis (MCDA) methods for the ORA has shown great increase recently. Also, developing a decision support system (DSS) which is easy to use and capable of extracting results enables decision makers to assess health and safety risks. Therefore, (i) A DSS based on Pythagorean fuzzy VIKOR (PFVIKOR) in ORA literature is developed for the first time; (ii) A case study is employed to risk assessment for trenchless crossings installation by auger boring process of the pipeline project; (iii) A sensitivity analysis is performed in the results of PFVIKOR-based DSS; and (iv) Two additional risk parameters called sensitivity to PPE non-utilization and undetectability are considered in ORA process apart from the classical risk assessment parameters of severity and probability. Developing such a DSS and applying it for risk assessment in a natural gas pipeline construction will contribute stakeholders in the energy industry.
AtıfScopus89WoS67OpenAlex83
İncele - MakaleYalnız künye2019Scopus SJRQ2An efficient algorithm for U-type assembly line re-balancing problem with stochastic task times
Serin, Faruk; Mete, Süleyman; Çelik, Erkan
Purpose: Changing the product characteristics and demand quantity resulting from the variability of the modern market leads to re-assigned tasks and changing the cycle time on the production line. Therefore, companies need re-balancing of their assembly line instead of balancing. The purpose of this paper is to propose an efficient algorithm approach for U-type assembly line re-balancing problem using stochastic task times. Design/methodology/approach: In this paper, a genetic algorithm is proposed to solve approach for U-type assembly line re-balancing problem using stochastic task times. Findings: The performance of the genetic algorithm is tested on a wide variety of data sets from literature. The task times are assumed normal distribution. The objective is to minimize total re-balancing cost, which consists of workstation cost, operating cost and task transposition cost. The test results show that proposed genetic algorithm approach for U-type assembly line re-balancing problem performs well in terms of minimizing total re-balancing cost. Practical implications: Demand variation is considered for stochastic U-type re balancing problem. Demand change also affects cycle time of the line. Hence, the stochastic U-type re-balancing problem under four different cycle times are analyzed to present practical case. Originality/value: As per the authors’ knowledge, it is the first time that genetic algorithm is applied to stochastic U-type re balancing problem. The large size data set is generated to analyze performance of genetic algorithm. The results of proposed algorithm are compared with ant colony optimization algorithm.
AtıfScopus19WoS17OpenAlex20İncele - MakaleAçık Erişim2019Scopus SJRQ3Optimization of medical waste routing problem: The case of TRB1 region in Turkey
Mete, Suleyman; Serin, Faruk
A fundamental problem concerning medical waste disposal is the evaluation of the real and potential risks arising from waste with the focus on the risk of infection. Therefore, the optimization of medical waste routing from collection to disposal center can minimize the risk of infection. The routing of medical waste considers significant to determine potential routes and select the route with minimum distance. The management of the medical waste is important decision for environmental sustainability and includes the collection, transportation and disposal of these materials. In this paper, a geographic information system (GIS) solution approach is proposed to determine the best location of disposal center. Proposed approach is applied to medical waste transportation between 167 health institutions (collection centers) and predetermined 5 disposal centers through TRB1 region in Turkey, which consist of Malatya, Elazığ, Bingöl and Tunceli provinces. The results of case study are examined and suggestions for future research are provided.
AtıfScopus7OpenAlex7
İncele - MakaleYayıncıda açık erişim2019Scopus SJRQ3An integrated best-worst and interval type-2 fuzzy TOPSIS methodology for green supplier selection
Yucesan, Melih; Mete, Suleyman; Serin, Faruk; Celik, Erkan; Gul, Muhammet
Supplier selection is one of the most important multi-criteria decision-making (MCDM) problems for decision-makers in the competitive market. Today's organizations are seeking new ways to reduce the negative effects they have on the environment and to achieve a greener system. Currently, the concept of green supplier selection has gained great importance for its ability to incorporate environmental or green criteria into classical supplier selection practices. Therefore, in this study, a multi-phase MCDM model based on the best-worst method (BWM) and the interval type-2 fuzzy technique for order preference by similarity to ideal solution (IT2F TOPSIS) is proposed. A case study in a plastic injection molding facility in Turkey was carried out to show the applicability of the proposed integrated methodology. The paper offers insights into decision-making, methodology, and managerial implications. Results of the case study are examined and suggestions for future research are provided.
AtıfScopus93WoS85OpenAlex98İncele - MakaleYalnız künye2017Scopus SJRQ1A novel overlapped nuclei splitting algorithm for histopathological images
Serin, Faruk; Erturkler, Metin; Gul, Mehmet
Background and objective Nuclei segmentation is a common process for quantitative analysis of histopathological images. However, this process generally results in overlapping of nuclei due to the nature of images, the sample preparation and staining, and image acquisition processes as well as insufficiency of 2D histopathological images to represent 3D characteristics of tissues. We present a novel algorithm to split overlapped nuclei. Methods The histopathological images are initially segmented by K-Means segmentation algorithm. Then, nuclei cluster are converted to binary image. The overlapping is detected by applying threshold area value to nuclei in the binary image. The splitting algorithm is applied to the overlapped nuclei. In first stage of splitting, circles are drawn on overlapped nuclei. The radius of the circles is calculated by using circle area formula, and each pixel's coordinates of overlapped nuclei are selected as center coordinates for each circle. The pixels in the circle that contains maximum number of intersected pixels in both the circle and the overlapped nuclei are removed from the overlapped nuclei, and the filled circle labeled as a nucleus. Results The algorithm has been tested on histopathological images of healthy and damaged kidney tissues and compared with the results provided by an expert and three related studies. The results demonstrated that the proposed splitting algorithm can segment the overlapping nuclei with accuracy of 84%. Conclusions The study presents a novel algorithm splitting the overlapped nuclei in histopathological images and provides more accurate cell counting in histopathological analysis. Furthermore, the proposed splitting algorithm has the potential to be used in different fields to split any overlapped circular patterns.
AtıfScopus6WoS7OpenAlex7
İncele