A Multi-Objective, Energy-Aware Inventory-Routing Model for Sustainable Blood-Bag Supply Chains

The sustainability of healthcare supply chains is challenged by high energy use, environmental impacts, and the need for equitable access to critical resources. The blood supply chain (BSC) is especially complex due to perishability, strict temperature control, and biomedical waste. Unlike prior studies that treat refrigeration as a fixed cost, this work integrates a thermodynamically based refrigeration model into an inventory–routing framework, allowing energy use and emissions to be determined by inventory levels, routing, and ambient conditions. A multi-objective model is developed to minimize cost and emissions while maximizing service levels, aligning with triple bottom line goals. Small instances are solved using mixed-integer linear programming (MILP) with ε-constraint methods, and larger cases are handled using NSGA-II. The model is validated through a real-world case study of the Kerala blood supply chain, using primary data from eight hospitals and two manufacturers. Baseline operations cost about ₹12.6 million per month and produce around 60 tons of CO₂, with transportation accounting for nearly 70% of emissions. Optimization reduces emissions by up to 30% with only a 10–15% cost increase, while knee-point solutions achieve balanced performance across objectives. Sensitivity analysis shows strong effects of ambient temperature and highlights the benefits of improving refrigeration efficiency (COP) and implementing carbon taxes. Overall, the study demonstrates that embedding thermodynamic modeling within supply chain optimization enables more realistic sustainability assessment and supports low-carbon, climate-resilient healthcare logistics design.