Optimizing sustainable production scheduling and routing in supply chains

• Studied integrated production-vehicle routing for environmental sustainability. • Developed bi-objective model: reduce emissions, minimize tardiness. • Created new problem instances for integrated production-routing. • Proposed improved NSGA-II algorithm for balanced objectives. • Introduced “Elbow Point” as a decision-making tool for sustainable decisions. This research addresses an integrated production scheduling and vehicle routing problem in a flexible job-shop-based manufacturing supply chain, with a focus on achieving both economic and environmental sustainability. A bi-objective mathematical model is developed to minimize total tardiness from delivery delays and CO 2 emissions from production and distribution operations. To solve this complex problem, we propose a hybrid, non-dominated sorting genetic algorithm (HNSGA-II). The proposed approach is benchmarked against classical optimization methods using CPLEX, non-hybridized versions of NSGA-II and NSGA-III, and Yağmur & Kesen (2023)s’ algorithm as a state-of-the-art approach. Performance comparisons on realistic problem instances reveal that HNSGA-II consistently provides higher-quality Pareto solutions, achieving better trade-offs between objectives within comparable runtimes. These findings demonstrate the proposed algorithm’s efficiency and applicability to integrated production and distribution optimization in sustainable supply chains.