Abstract
Pharmaceutical supply chains face increasing pressure to deliver high service levels while meeting environmental and social expectations. Vaccine supply chains amplify these challenges due to strict cold-chain requirements, demand uncertainty driven by acceptance and preferences, and the urgency of public-health objectives. This paper develops a multi-objective mixed-integer linear programming (MILP) framework for national-scale vaccine distribution that explicitly integrates economic cost, service level, greenhouse-gas emissions, and population-level vaccine effectiveness. Behavioral realism is incorporated by modeling vaccine acceptance and brand preferences as operational constraints rather than ex-post indicators. Trade-offs are explored using an e-constraint method that preserves the MILP structure and enables systematic recovery of Pareto-optimal solutions. The framework is applied to a 52-week national case study for metropolitan France during the 2021 COVID-19 vaccination campaign, focusing on Pfizer-BioNTech and Moderna mRNA vaccines. Results show that population-level effectiveness saturates early once acceptance-driven coverage is achieved, making cost and emissions the primary decision levers under service guarantees. Balanced solutions maintain near-maximal protection while achieving substantial emission reductions at moderate additional cost, whereas deeper decarbonization requires explicit service concessions. The proposed framework provides transparent, policy-relevant insights and supports informed decision-making in large-scale vaccine logistics.