A Sustainable Green Supply Chain Model with Carbon Emissions for Defective Items under Learning in a Fuzzy Environment

Assuming the significance of sustainability, it is considered necessary to ensure conservation our natural resources, in addition minimizing waste. To promote significant sustainable effects, factors including production, transportation, energy usage, product control management, etc., act as chief supports any modern supply chain model. The buyer performs firsthand inspection and returns defective items received from customer vendor a process that known first-level inspection. uses policy recovery management obtain greater profit. A concluding accomplished at vendor’s end order distinguish returned item belonging one four specific categories, namely re-workable, reusable, recyclable, disposable, second-level Then, observed some are suitable for secondary market, while can be disassembled shape new derived products, leftovers scrapped disposal cost. This ensures we meet target cleaner drive with lower percentage carbon emissions, reducing adverse effects landfills. activity both players this model presented briefly flowchart shown abstract. Thus, aim restoration best practices maintaining economic value, ultimate goal removing surrounding waste minimum financial costs. In regard, assumed demand rate precise nature. learning effect fuzzy environment also present proposed studies impacts emissions on an integrated green shortage conditions. We optimized total profit respect quantity shortages. described strategy buyer’s through flowcharts model, here, flowchart, R-R-R stands recyclable. was treated triangular number. paper, numerical example, sensitivity analysis, limitations, future scope, conclusion validation