Sürdürülebilir Kalkınma Amaçları

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• MakaleYalnız künye2022/r/gumushane/14409
  Alleviating the Impact of the Barriers to Circular Economy Adoption Through Blockchain: An Investigation Using an Integrated MCDM-based QFD With Hesitant Fuzzy Linguistic Term Sets

  Erol, Ismail; Murat Ar, Ilker; Peker, Iskender; Searcy, Cory

  Despite its clear potential to contribute to sustainability, there are many challenges to adopting circular economy (CE). These barriers impede the implementation of CE; however, research to address them is ongoing. For example, novel technologies have become a key enabler for easing the impact of CE adoption barriers. Blockchain is one disruptive technology that has the potential to address some of those barriers, notably technological, organizational, and ecological. Blockchain is particularly well suited to help address supply chain-oriented barriers to CE adoption. However, a review of the literature shows that while there are a limited number of conceptual studies that address the capabilities of blockchain to alleviate the impact of the barriers to CE, no previous study has focused on this vital subject using a decision tool based on expert data. To this end, this paper proposes an integrated decision framework involving Multi-Criteria Decision-Making (MCDM)-based Quality Function Deployment (QFD) method with Hesitant Fuzzy Linguistic Term Sets (HFLTS) to investigate the true potential of blockchain to address the CE adoption barriers. Our findings indicate that enhanced supply chain traceability management, improved collaboration and coordination in supply chain ecosystems, building higher levels of trust in supply chain ecosystems, and enhanced business models are the most significant functions of blockchain to help overcome the CE adoption barriers.

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  İncele
• MakaleYalnız künye2021/r/gumushane/11670
  Cost-oriented LSTM methods for possible expansion of control charting signals

  Ünlü, Ramazan

  Manual quality control may result in delayed detection of a system defect, or none at all, potentially resulting in malfunctions that can lead to disruption of the system, incur extra costs, or complete downtime of the system. To mitigate such issues, more advanced statistics and machine learning-based systems such as SVM, WSVM, etc. are used to automatically detect error signals during the process. However, these methodologies are not developed for sequential dataset such as a classical CCPR dataset. As a result, in this study, we have implemented a preliminary Cost-Oriented Long-Short Term Memory (LSTM), which is designed to learn from a sequential dataset and compare it with SVM and WSVM which are traditional methods utilized in the field. Additionally, we compared the performance of methods in terms of early detection of an abnormal pattern. Based on the findings, the Cost-Oriented LSTM method outperforms SVM and WSVM in the majority of abnormal patterns in terms of both classification and early detection performance. Global accuracy score which is the average of accuracy rates in all combination of abnormal parameters and window lengths shows that LSTM gives a better accuracy score than SVM and WSVM in all seven abnormal patterns detection.

  İncele
• MakaleYalnız künye2019/r/gumushane/15155
  A new use of the ant system algorithm for the max-mean dispersion problem

  Cura, Tunchan; Özdemir, Muhlis

  This study addresses an NP-hard problem known as the max-mean dispersion problem (MaxMeanDP), which belongs to a well-known class of diversity problems. In view of the increasing importance of parallel computing, this study proposes an ant system (AS) approach to the MaxMeanDP in which ants execute the search process in parallel. Moreover, by proposing a rather different use of the pheromone trail and visibility compared to the original AS, a new AS algorithm is introduced that runs in parallel. Our analysis indicates that the new use of the pheromone trail and visibility significantly contributes to the quality of the solution. The results show that the proposed method is effective, efficient and comparable to other approaches.

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