Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
Browse
2 results
Search Results
Conference Object Citation - WoS: 4Citation - Scopus: 3Green Supplier Selection by Using Fuzzy TOPSIS Method(World Scientific Publishing Co. Pte Ltd wspc@wspc.com.sg, 2016-08) Dogan, Ahmet; Söylemez, İsmet; Özcan, Uǧur; Stylemez, IsmetWith the increased environmental consciousness in customers, organizations took upon the task of redesigning their strategic goals in a more environment-sensitive way in order to fulfill their social obligations, to enable sustainability, to gain competitive advantage and to make the world more habitable. Because, the emerging conditions in the 21st century indicate that the traditional criteria -such as price, cost so on for supply chain management, supplier selection and performance measurement of suppliers are no more sufficient and there is the necessity of adding new criteria such as environmental matters. This paper deals with the problem of selecting green suppliers in an organization in Turkey that has operations in the field of accumulator. The aim is to select the greenest of 3 suppliers in Turkey, France and Bulgaria which supply the organization with the plastic material used in the production of accumulator. The problem is solved via fuzzy TOPSIS, which is a multi-criteria decision making method (MCDM), and the results are used to select the greenest supplier. © 2017 Elsevier B.V., All rights reserved.Article Citation - Scopus: 10Captain: A Testbed for Co-Simulation of Scalable Serverless Computing Environments for AIoT Enabled Predictive Maintenance in Industry 4.0(Institute of Electrical and Electronics Engineers Inc., 2025-08-15) Golec, Muhammed; Wu, Huaming; Ozturac, Ridvan; Kumar Parlikad, Ajith; Cuadrado Latasa, Felix; Gill, Sukhpal Singh; Uhlig, Steve; Cuadrado, Felix; Singh Gill, SukhpalThe massive amounts of data generated by the Industrial Internet of Things (IIoT) require considerable processing power, which increases carbon emissions and energy usage, and we need sustainable solutions to enable flexible manufacturing. Serverless computing shows potential for meeting this requirement by scaling idle containers to zero energy-efficiency and cost, but this will lead to a cold start delay. Most solutions rely on idle containers, which necessitates dynamic request time forecasting and container execution monitoring. Furthermore, Artificial Intelligence of Things (AIoT) can provide autonomous and sustainable solutions by combining IIoT with artificial intelligence (AI) to solve this problem. Therefore, we develop a new testbed, CAPTAIN, to facilitate AI-based co-simulation of scalable and flexible serverless computing in IIoT environments. The AI module in the CAPTAIN framework employs random forest (RF) and light gradient-boosting machine (LightGBM) models to optimize cold start frequency and prevent cold starts based on their prediction results. The proxy module additionally monitors the client-server network and constantly updates the AI module training dataset via a message queue. Finally, we evaluated the proxy module’s performance using a predictive maintenance-based real-world IIoT application and the AI module’s performance in a realistic serverless environment using a Microsoft Azure dataset. The AI module of the CAPTAIN outperforms baselines in terms of cold start frequency, computational time with 0.5 ms, energy consumption with 1161.0 joules, and CO2 emissions with 32.25e-05 gCO<inf>2</inf>. The CAPTAIN testbed provides a co-simulation of sustainable and scalable serverless computing environments for AIoT-enabled predictive maintenance in Industry 4.0. © 2025 Elsevier B.V., All rights reserved.