Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2022Scopus SJRQ1/r/munzur/2684
  A 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.

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  AtıfScopus26·WoS24·OpenAlex29

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• MakaleYalnız künye2020Scopus SJRQ1/r/munzur/3707
  Emergency department network under disaster conditions: The case of possible major Istanbul earthquake

  Gul, Muhammet; Fuat Guneri, Ali; Gunal, Murat M.

  Emergency departments (EDs) provide health care services to people in need of urgent care. Their role is remarkable when extraordinary events that affect the public, such as earthquakes, occur. In this paper, we present a hybrid framework to evaluate earthquake preparedness of EDs in cities. Our hybrid framework uses artificial neural networks (ANNs) to estimate number of casualties and discrete event simulation (DES) to analyse the effect of surge in patient demand in EDs, after an earthquake happens. At the core of our framework, Earthquake Time Emergency Department Network Simulation Model (ET-EDNETSIM) resides which can simulate patient movements in a network of multiple and coordinated EDs. With the design of simulation experiments, different resource levels and sharing rules between EDs can be evaluated. We demonstrated our framework in a network of five EDs located in a region of which is estimated to have the highest injury rate after an earthquake in Istanbul, Turkey. Results of our study contributed to the planning for expected earthquake in Istanbul. Simulating a network of EDs extends the individual ED studies in the literature and furthermore, our hybrid framework can help increase earthquake preparedness in cities around the world. On the methodological side, the use of ANN, which is a member of machine learning (ML) algorithms family, in our hybrid framework also shows the close links between ML and DES.

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  AtıfScopus30·WoS31·OpenAlex40

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