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

Abstract

The sustainability of healthcare supply chains is emerging as a global concern, given their high energy intensity, environmental footprint and critical social role in ensuring equitable access to life-saving products. Among these, the blood bag supply chain (BSC) is particularly challenging due to the perishability of blood components, strict temperature requirements and biomedical waste burdens from expired units. Unsustainable practices in the BSC not only escalate costs but also increase emissions and shortages, threatening both efficiency and equity of care.Current research on blood supply chains has primarily focused on donor management, demand forecasting and routing optimization. While such approaches improve service availability, they often treat refrigeration as a fixed cost, overlooking its thermodynamic dependence on equipment performance, insulation and climatic stress. Consequently, energy consumption and emissions are systematically underestimated, limiting the value of prior sustainability assessments.This study develops a multi-objective optimization framework that integrates logistics decision-making with a refrigeration sub-model based on thermodynamic energy balance equations. The formulation minimizes economic costs, reduces environmental emissions and maximizes service levels, aligning with the principles of the triple bottom line. Small-scale instances were solved using mixed-integer linear programming (MILP) with ε-constraint generation, while large-scale applications employed the Non-dominated Sorting Genetic Algorithm II (NSGA-II). A regional case study was conducted in Kerala, India, using primary survey data from eight hospitals and two manufacturers, calibrated with secondary data on emissions, costs and technical refrigeration parameters.Results show that baseline operations in Kerala incur costs of ₹12.6 million per month and generate 60.2 tons of emissions, with transport responsible for 70%. Optimization achieves emission reductions of up to 30% with only 10-15% cost increases, while knee-point solutions deliver balanced improvements. Sensitivity analyses highlight the vulnerability of the cold chain to heatwaves and the benefits of COP upgrades and carbon taxation. The framework thus demonstrates how sustainability in blood supply chains can be advanced through integrated engineering-logistics modeling, offering actionable pathways for low-carbon, resilient healthcare systems.