Barriers and enhance strategies for green supply chain management using continuous linear diophantine neural networks

Abstract Artificial neural networks, a major element of machine learning, focus additional attention on the decision-making process. We extended the idea of artificial neural networks to continuous linear Diophantine fuzzy neural networks. A few operational concepts for continuous linear Diophantine fuzzy sets are further developed, and they are subsequently made simpler to apply to more than two such sets. Also, a real multi-criteria decision-making problem has been formulated. The environment plays a very important role in our daily lives. We cause different types of pollution in our environment, and it has a bad impact on our lives. Air pollution is one of the various forms of pollution that is thought to affect the entire globe. Millions of people die due to air pollution, and industries are the main contributors to air pollution. To overcome air pollution, green supply chain management plays a vital role, but green supply chain management faces some barriers as well. According to the proposed model, $${\mathfrak{R}}_{1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>R</mml:mi> <mml:mn>1</mml:mn> </mml:msub> </mml:math> is the best alternative and green supply chain management faces financial problems more than other barriers and also provides strategies to overcome financial barriers. In addition, a comparative analysis develops to illustrate the reliability and feasibility of the suggested technique in relation to current techniques.