An analytics-driven circular supply chain framework integrating quality, warranty, and human efficiency

This paper presents an advanced human-centric circular supply chain optimization framework that integrates economic, environmental, and behavioral dimensions into a unified multi-objective model. By jointly optimizing selling price, product quality, warranty duration, and production cycle time, the model captures the intricate trade-offs between profitability and sustainability-related penalties. A distinctive feature of the framework is the incorporation of a Human Efficiency Index and a circularity-based return function, enabling dynamic modeling of skill-driven waste minimization and quality-sensitive consumer behavior. The resulting nonlinear optimization problem is addressed using four powerful metaheuristic algorithms—Teaching-Learning-Based Optimization (TLBO), TLBO with Learning Rate, Non-dominated Sorting Genetic Algorithm II (NSGA-II), and Multi-Objective Particle Swarm Optimization (MOPSO). Extensive numerical simulations demonstrate the efficacy of the TLBO-based methods in achieving high-profit, low-penalty solutions, while statistical analyses confirm their robustness and superiority through the Friedman test and the Wilcoxon signed-rank test. From a managerial perspective, the model offers critical insights for aligning operational decisions with sustainability-oriented goals by demonstrating the nonlinear effects of human efficiency and product lifecycle attributes on supply chain performance. From a policy standpoint, the findings advocate for institutional mechanisms that incentivize investment in skill development, recycling, and circularity-driven design practices. Furthermore, the social relevance of this work lies in its contribution to Industry 5.0 paradigms, where inclusive, sustainable, and human-empowered production systems are prioritized. This research thus provides a robust, actionable framework for decision-makers seeking to design resilient and circular supply chains that promote long-term economic value and social welfare. • Optimise circular supply chain with pricing, quality, warranty, and workforce. • Use learning to cut waste and boost human efficiency over time. • Define dynamic indices for circularity and sustainability under constraints. • Use metaheuristics to solve multi-objective circular supply chain problems. • Link human investment to policy insights on environmental and economic outcomes.