An automated inspection and green technology investment to control waste and carbon in a fuzzy EPQ model with multivariate demand and learning effect

Automated screening processes along with green investment result in a reduction in carbon emissions and waste from the imperfect manufacturing system. In addition to this, the familiarity of labour with tools, dies, and machines helps in the reduction of defective products in the manufacturing process. This paper develops a fuzzy manufacturing and re-manufacturing inventory model for spare parts with the objective of reducing waste and carbon with the help of an automated screening process and green technology investment. We also incorporate the effect of learning to reduce the number of defective products in the manufacturing system. Smart manufacturing systems have led to investments in reducing setup and inspection costs. Nowadays, customers are more aware, so demand is considered price- and green-investment-dependent. Shortages are allowed in the manufacturing system, which is partially backlogged. The results show that investments in green technology, the learning effect, the impreciseness of different costs, and the automated screening process all contribute to achieving sustainability goals.