Blockchain acceptance rate prediction in the resilient supply chain with hybrid system dynamics and machine learning approach

نویسندگان

چکیده

In today’s era, the importance and implementation of blockchain networks have become feasible as it improves resilience supply chain network at all levels by clarifying information creating security in network, improving speed response, gaining trust customers. This paper aims to investigate behavior acceptance rate (BAR) home appliances flexible Iran using. system dynamics (SD), which is used better define relationships between variables model that are non-linearly connected. Through simulating BAR long term chain, whilst conducting sensitivity analysis, policy design, validation, this will be implemented for years 2020 2030. Additionally, post-simulation, assessed having simulated data considered input studied Multi-Layer Perceptron (MLP) Vector Regression (SVR) (data highest correlation with BAR). The predicted help machine learning methods best prediction 2020-2022 since function compared daily real obtained these years. results show 2030, around 0.6 if COVID-19 outbreak impact medium, designs implemented, reach a maximum 0.8. So paying attention creation design policies can achieve positive implications increasing run. Findings suggest SD-MLP method than SD-SVR has less error predict BAR.

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ژورنال

عنوان ژورنال: Operations Management Research

سال: 2022

ISSN: ['1936-9735', '1936-9743']

DOI: https://doi.org/10.1007/s12063-022-00336-x