A green inventory model in intuitionistic fuzzy environment with remanufacturing of defective products under cap-and-trade policy

Abstract

This research focuses on creating a multi-objective inventory management model for supply chains that addresses the difficulties of product deterioration and poor-quality production in an intuitive fuzzy environment. The purpose of this research is to lower both total operational costs and carbon emissions to improve supply chain profitability and prevent global warming. To minimize carbon emissions, the study considers carbon cap-and-trade policies and green technologies. Preservation technology is used to slow decreased product deterioration, while reworking abilities are used to repair imperfect items. Additionally, investments in quality improvements are considered to boost demand. In real-world scenarios, inventory management parameters are often uncertain, and thus, triangular intuitionistic fuzzy numbers are used to model these uncertainties. The study utilizes neutrosophic compromise programming to solve the resulting multi-objective model. The approach is demonstrated with a practical example, comparing crisp, fuzzy, and intuitionistic fuzzy models.